diff --git a/explore-analyze/alerts-cases/alerts.md b/explore-analyze/alerts-cases/alerts.md index dee005996e..f768993d33 100644 --- a/explore-analyze/alerts-cases/alerts.md +++ b/explore-analyze/alerts-cases/alerts.md @@ -28,7 +28,7 @@ For example, when monitoring a set of servers, a rule might: Each project type supports a specific set of rule types. Each *rule type* provides its own way of defining the conditions to detect, but an expression formed by a series of clauses is a common pattern. For example, in an {{es}} query rule, you specify an index, a query, and a threshold, which uses a metric aggregation operation (`count`, `average`, `max`, `min`, or `sum`): -:::{image} ../../images/serverless-es-query-rule-conditions.png +:::{image} /explore-analyze/images/serverless-es-query-rule-conditions.png :alt: UI for defining rule conditions in an {{es}} query rule :screenshot: ::: @@ -56,14 +56,14 @@ Each action uses a connector, which provides connection information for a {{kib} After you select a connector, set the *action frequency*. If you want to reduce the number of notifications you receive without affecting their timeliness, some rule types support alert summaries. For example, if you create an {{es}} query rule, you can set the action frequency such that you receive summaries of the new, ongoing, and recovered alerts on a custom interval: -:::{image} ../../images/serverless-es-query-rule-action-summary.png +:::{image} /explore-analyze/images/serverless-es-query-rule-action-summary.png :alt: UI for defining rule conditions in an {{es}} query rule :screenshot: ::: Alternatively, you can set the action frequency such that the action runs for each alert. If the rule type does not support alert summaries, this is your only available option. You must choose when the action runs (for example, at each check interval, only when the alert status changes, or at a custom action interval). You must also choose an action group, which affects whether the action runs. Each rule type has a specific set of valid action groups. For example, you can set *Run when* to `Query matched` or `Recovered` for the {{es}} query rule: -:::{image} ../../images/serverless-es-query-rule-recovery-action.png +:::{image} /explore-analyze/images/serverless-es-query-rule-recovery-action.png :alt: UI for defining a recovery action :screenshot: ::: @@ -93,7 +93,7 @@ To get notified only once when a server exceeds the threshold, you can set the a You can pass rule values to an action at the time a condition is detected. To view the list of variables available for your rule, click the "add rule variable" button: -:::{image} ../../images/serverless-es-query-rule-action-variables.png +:::{image} /explore-analyze/images/serverless-es-query-rule-action-variables.png :alt: Passing rule values to an action :screenshot: ::: @@ -110,7 +110,7 @@ Using the server monitoring example, each server with average CPU > 0.9 is track A rule consists of conditions, actions, and a schedule. When conditions are met, alerts are created that render actions and invoke them. To make action setup and update easier, actions use connectors that centralize the information used to connect with {{kib}} services and third-party integrations. The following example ties these concepts together: -:::{image} ../../images/serverless-rule-concepts-summary.svg +:::{image} /explore-analyze/images/serverless-rule-concepts-summary.svg :alt: Rules :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/alerting-common-issues.md b/explore-analyze/alerts-cases/alerts/alerting-common-issues.md index 699f940c93..5c86f9e995 100644 --- a/explore-analyze/alerts-cases/alerts/alerting-common-issues.md +++ b/explore-analyze/alerts-cases/alerts/alerting-common-issues.md @@ -71,7 +71,7 @@ By default, rules have a `5m` timeout. Rules that run longer than this timeout a and in the [details page](create-manage-rules.md#rule-details): -:::{image} ../../../images/kibana-rule-details-timeout-error.png +:::{image} /explore-analyze/images/kibana-rule-details-timeout-error.png :alt: Rule details page with timeout error :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/alerting-getting-started.md b/explore-analyze/alerts-cases/alerts/alerting-getting-started.md index da34cdf432..378614c419 100644 --- a/explore-analyze/alerts-cases/alerts/alerting-getting-started.md +++ b/explore-analyze/alerts-cases/alerts/alerting-getting-started.md @@ -9,7 +9,7 @@ navigation_title: Getting started with alerts Alerting enables you to define *rules*, which detect complex conditions within different {{kib}} apps and trigger actions when those conditions are met. Alerting is integrated with [**{{observability}}**](../../../solutions/observability/incident-management/alerting.md), [**Security**](security-docs://reference/prebuilt-rules/index.md), [**Maps**](../../../explore-analyze/alerts-cases/alerts/geo-alerting.md) and [**{{ml-app}}**](../../../explore-analyze/machine-learning/anomaly-detection/ml-configuring-alerts.md). It can be centrally managed from **{{stack-manage-app}}** and provides a set of built-in [connectors](../../../deploy-manage/manage-connectors.md) and [rules](../../../explore-analyze/alerts-cases/alerts/rule-types.md#stack-rules) for you to use. -:::{image} ../../../images/kibana-alerting-overview.png +:::{image} /explore-analyze/images/kibana-alerting-overview.png :alt: {{rules-ui}} UI ::: @@ -36,7 +36,7 @@ For example, when monitoring a set of servers, a rule might: * Check every minute (schedule). * Send a warning email message via SMTP with subject `CPU on {{server}} is high` (action). -:::{image} ../../../images/kibana-what-is-a-rule.svg +:::{image} /explore-analyze/images/kibana-what-is-a-rule.svg :alt: Three components of a rule ::: @@ -89,7 +89,7 @@ When checking for a condition, a rule might identify multiple occurrences of the Using the server monitoring example, each server with average CPU > 0.9 is tracked as an alert. This means a separate email is sent for each server that exceeds the threshold whenever the alert status changes. -:::{image} ../../../images/kibana-alerts.svg +:::{image} /explore-analyze/images/kibana-alerts.svg :alt: {{kib}} tracks each detected condition as an alert and takes action on each alert ::: @@ -97,7 +97,7 @@ Using the server monitoring example, each server with average CPU > 0.9 is track A rule consists of conditions, actions, and a schedule. When conditions are met, alerts are created that render actions and invoke them. To make action setup and update easier, actions use connectors that centralize the information used to connect with {{kib}} services and third-party integrations. The following example ties these concepts together: -:::{image} ../../../images/kibana-rule-concepts-summary.svg +:::{image} /explore-analyze/images/kibana-rule-concepts-summary.svg :alt: Rules ::: diff --git a/explore-analyze/alerts-cases/alerts/alerting-troubleshooting.md b/explore-analyze/alerts-cases/alerts/alerting-troubleshooting.md index 959b10cc78..d59508cd16 100644 --- a/explore-analyze/alerts-cases/alerts/alerting-troubleshooting.md +++ b/explore-analyze/alerts-cases/alerts/alerting-troubleshooting.md @@ -31,7 +31,7 @@ The following debugging tools are available: **{{rules-ui}}** in **{{stack-manage-app}}** lists the rules available in the space you’re currently in. When you click a rule name, you are navigated to the [details page](create-manage-rules.md#rule-details) for the rule, where you can see currently active alerts. The start date on this page indicates when a rule is triggered, and for what alerts. In addition, the duration of the condition indicates how long the instance is active. -:::{image} ../../../images/kibana-rule-details-alerts-inactive.png +:::{image} /explore-analyze/images/kibana-rule-details-alerts-inactive.png :alt: Alerting management details :screenshot: ::: @@ -40,7 +40,7 @@ The following debugging tools are available: When creating or editing an index threshold rule, you see a graph of the data the rule will operate against, from some date in the past until now, updated every 5 seconds. -:::{image} ../../../images/kibana-index-threshold-chart.png +:::{image} /explore-analyze/images/kibana-index-threshold-chart.png :alt: Index Threshold chart :screenshot: ::: @@ -77,7 +77,7 @@ The result of this HTTP request (and printed to stdout by [kbn-action](https://g The **{{stack-manage-app}}** > **{{rules-ui}}** page contains an error banner that helps to identify the errors for the rules: -:::{image} ../../../images/kibana-rules-management-health.png +:::{image} /explore-analyze/images/kibana-rules-management-health.png :alt: Rule management page with the errors banner :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/create-manage-rules.md b/explore-analyze/alerts-cases/alerts/create-manage-rules.md index a7504e957d..64c4424d09 100644 --- a/explore-analyze/alerts-cases/alerts/create-manage-rules.md +++ b/explore-analyze/alerts-cases/alerts/create-manage-rules.md @@ -12,7 +12,7 @@ The **{{stack-manage-app}}** > **{{rules-ui}}** UI provides a cross-app view of You can find **Rules** in **Stack Management** > **Alerts and insights** > **Rules** in {{kib}} or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). -![Rules page navigation](../../../images/kibana-stack-management-rules.png "") +![Rules page navigation](/explore-analyze/images/kibana-stack-management-rules.png "") **{{rules-ui}}** provides a central place to: @@ -43,7 +43,7 @@ Depending on the {{kib}} app and context, you might be prompted to choose the ty Each rule type provides its own way of defining the conditions to detect, but an expression formed by a series of clauses is a common pattern. For example, in an {{es}} query rule, you specify an index, a query, and a threshold, which uses a metric aggregation operation (`count`, `average`, `max`, `min`, or `sum`): -:::{image} ../../../images/kibana-rule-types-es-query-conditions.png +:::{image} /explore-analyze/images/kibana-rule-types-es-query-conditions.png :alt: UI for defining rule conditions in an {{es}} query rule :screenshot: ::: @@ -68,14 +68,14 @@ If you choose a custom action interval, it cannot be shorter than the rule’s c For example, if you create an {{es}} query rule, you can send notifications that summarize the new, ongoing, and recovered alerts on a custom interval: -:::{image} ../../../images/kibana-es-query-rule-action-summary.png +:::{image} /explore-analyze/images/kibana-es-query-rule-action-summary.png :alt: UI for defining alert summary action in an {{es}} query rule :screenshot: ::: When you choose to run actions for each alert, you must specify an action group. Each rule type has a set of valid action groups, which affect when an action runs. For example, you can set **Run when** to `Query matched` or `Recovered` for the {{es}} query rule: -:::{image} ../../../images/kibana-es-query-rule-recovery-action.png +:::{image} /explore-analyze/images/kibana-es-query-rule-recovery-action.png :alt: UI for defining a recovery action :screenshot: ::: @@ -105,7 +105,7 @@ To get notified only once when a server exceeds the threshold, you can set the a You can pass rule values to an action at the time a condition is detected. To view the list of variables available for your rule, click the "add rule variable" button: -:::{image} ../../../images/kibana-es-query-rule-action-variables.png +:::{image} /explore-analyze/images/kibana-es-query-rule-action-variables.png :alt: Passing rule values to an action :screenshot: ::: @@ -116,13 +116,13 @@ For more information about common action variables, refer to [*Rule action varia The rule listing enables you to quickly snooze, disable, enable, or delete individual rules. For example, you can change the state of a rule: -![Use the rule status dropdown to enable or disable an individual rule](../../../images/kibana-individual-enable-disable.png "") +![Use the rule status dropdown to enable or disable an individual rule](/explore-analyze/images/kibana-individual-enable-disable.png "") If there are rules that are not currently needed, disable them to stop running checks and reduce the load on your cluster. When you snooze a rule, the rule checks continue to run on a schedule but alerts will not generate actions. You can snooze for a specified period of time, indefinitely, or schedule single or recurring downtimes: -![Snooze notifications for a rule](../../../images/kibana-snooze-panel.png "") +![Snooze notifications for a rule](/explore-analyze/images/kibana-snooze-panel.png "") When a rule is in a snoozed state, you can cancel or change the duration of this state. @@ -143,16 +143,16 @@ You can determine the health of a rule by looking at the **Last response** in ** Click the rule name to access a rule details page: -:::{image} ../../../images/kibana-rule-details-alerts-active.png +:::{image} /explore-analyze/images/kibana-rule-details-alerts-active.png :alt: Rule details page with multiple alerts :screenshot: ::: In this example, the rule detects when a site serves more than a threshold number of bytes in a 24 hour period. Four sites are above the threshold. These are called alerts - occurrences of the condition being detected - and the alert name, status, time of detection, and duration of the condition are shown in this view. Alerts come and go from the list depending on whether the rule conditions are met. For more information about alerts, go to [*View alerts*](view-alerts.md). -If there are rule actions that failed to run successfully, you can see the details on the **History** tab. In the **Message** column, click the warning or expand icon ![double arrow icon to open a flyout with the document details](../../../images/kibana-expand-icon-2.png "") or click the number in the **Errored actions** column to open the **Errored Actions** panel. In this example, the action failed because the [`xpack.actions.email.domain_allowlist`](kibana://reference/configuration-reference/alerting-settings.md#action-config-email-domain-allowlist) setting was updated and the action’s email recipient is no longer included in the allowlist: +If there are rule actions that failed to run successfully, you can see the details on the **History** tab. In the **Message** column, click the warning or expand icon ![double arrow icon to open a flyout with the document details](/explore-analyze/images/kibana-expand-icon-2.png "") or click the number in the **Errored actions** column to open the **Errored Actions** panel. In this example, the action failed because the [`xpack.actions.email.domain_allowlist`](kibana://reference/configuration-reference/alerting-settings.md#action-config-email-domain-allowlist) setting was updated and the action’s email recipient is no longer included in the allowlist: -:::{image} ../../../images/kibana-rule-details-errored-actions.png +:::{image} /explore-analyze/images/kibana-rule-details-errored-actions.png :alt: Rule histor page with alerts that have errored actions :screenshot: ::: @@ -172,7 +172,7 @@ Some rule types cannot be exported through this interface: Rules are disabled on export. You are prompted to re-enable the rule on successful import. -:::{image} ../../../images/kibana-rules-imported-banner.png +:::{image} /explore-analyze/images/kibana-rules-imported-banner.png :alt: Rules import banner :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/geo-alerting.md b/explore-analyze/alerts-cases/alerts/geo-alerting.md index 098105eaa9..be9d1c57f0 100644 --- a/explore-analyze/alerts-cases/alerts/geo-alerting.md +++ b/explore-analyze/alerts-cases/alerts/geo-alerting.md @@ -17,7 +17,7 @@ In **{{stack-manage-app}}** > **{{rules-ui}}**, click **Create rule**. Select th When you create a tracking containment rule, you must define the conditions that it detects. For example: -:::{image} ../../../images/kibana-alert-types-tracking-containment-conditions.png +:::{image} /explore-analyze/images/kibana-alert-types-tracking-containment-conditions.png :alt: Creating a tracking containment rule in Kibana :screenshot: ::: @@ -41,7 +41,7 @@ For each action, you must choose a connector, which provides connection informat After you select a connector, you must set the action frequency. You can choose to create a summary of alerts on each check interval or on a custom interval. Alternatively, you can set the action frequency such that actions run for each alert. Choose how often the action runs (at each check interval, only when the alert status changes, or at a custom action interval). You must also choose an action group, which indicates whether the action runs when the containment condition is met or when an entity is no longer contained. Each connector supports a specific set of actions for each action group. For example: -:::{image} ../../../images/kibana-alert-types-tracking-containment-action-options.png +:::{image} /explore-analyze/images/kibana-alert-types-tracking-containment-action-options.png :alt: Action frequency options for an action :screenshot: ::: @@ -52,7 +52,7 @@ You can further refine the conditions under which actions run by specifying that You can pass rule values to an action to provide contextual details. To view the list of variables available for each action, click the "add rule variable" button. For example: -:::{image} ../../../images/kibana-alert-types-tracking-containment-rule-action-variables.png +:::{image} /explore-analyze/images/kibana-alert-types-tracking-containment-rule-action-variables.png :alt: Passing rule values to an action :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/maintenance-windows.md b/explore-analyze/alerts-cases/alerts/maintenance-windows.md index 83ea7882df..e5474f663a 100644 --- a/explore-analyze/alerts-cases/alerts/maintenance-windows.md +++ b/explore-analyze/alerts-cases/alerts/maintenance-windows.md @@ -9,7 +9,7 @@ mapped_urls: # Maintenance windows -This content applies to: [![Observability](../../../images/serverless-obs-badge.svg "")](../../../solutions/observability.md) [![Security](../../../images/serverless-sec-badge.svg "")](../../../solutions/security/elastic-security-serverless.md) +This content applies to: [![Observability](/explore-analyze/images/serverless-obs-badge.svg "")](../../../solutions/observability.md) [![Security](/explore-analyze/images/serverless-sec-badge.svg "")](../../../solutions/security/elastic-security-serverless.md) ::::{warning} @@ -40,7 +40,7 @@ In **Management > {{stack-manage-app}} > Maintenance Windows** or **{{project-se When you create a maintenance window, you must provide a name and a schedule. You can optionally configure it to repeat daily, monthly, yearly, or on a custom interval. -:::{image} ../../../images/kibana-create-maintenance-window.png +:::{image} /explore-analyze/images/kibana-create-maintenance-window.png :alt: The Create Maintenance Window user interface in {{kib}} :screenshot: ::: @@ -49,7 +49,7 @@ By default, maintenance windows affect all categories of rules. The category-spe If you turn on **Filter alerts**, you can use KQL to filter the alerts affected by the maintenance window: -:::{image} ../../../images/kibana-create-maintenance-window-filter.png +:::{image} /explore-analyze/images/kibana-create-maintenance-window-filter.png :alt: The Create Maintenance Window user interface in {{kib}} with alert filters turned on :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/rule-action-variables.md b/explore-analyze/alerts-cases/alerts/rule-action-variables.md index 187eac1d13..d2702f4d99 100644 --- a/explore-analyze/alerts-cases/alerts/rule-action-variables.md +++ b/explore-analyze/alerts-cases/alerts/rule-action-variables.md @@ -435,4 +435,4 @@ You can create the following Mustache template in the email action for your rule When rendered into Markdown and then HTML and viewed in an email client, it looks like this: -![Email template rendered in an email client](../../../images/kibana-email-mustache-template-rendered.png "") +![Email template rendered in an email client](/explore-analyze/images/kibana-email-mustache-template-rendered.png "") diff --git a/explore-analyze/alerts-cases/alerts/rule-type-es-query.md b/explore-analyze/alerts-cases/alerts/rule-type-es-query.md index 5d477c0d5a..50a6ae59dd 100644 --- a/explore-analyze/alerts-cases/alerts/rule-type-es-query.md +++ b/explore-analyze/alerts-cases/alerts/rule-type-es-query.md @@ -17,7 +17,7 @@ In **{{stack-manage-app}}** > **{{rules-ui}}**, click **Create rule**. Select th When you create an {{es}} query rule, your choice of query type affects the information you must provide. For example: -:::{image} ../../../images/kibana-rule-types-es-query-conditions.png +:::{image} /explore-analyze/images/kibana-rule-types-es-query-conditions.png :alt: Define the condition to detect :screenshot: ::: @@ -70,14 +70,14 @@ Use the **Test query** feature to verify that your query is valid. If you use query DSL, KQL, or Lucene, the query runs against the selected indices using the configured time window. The number of documents that match the query is displayed. For example: -:::{image} ../../../images/kibana-rule-types-es-query-valid.png +:::{image} /explore-analyze/images/kibana-rule-types-es-query-valid.png :alt: Test {{es}} query returns number of matches when valid :screenshot: ::: If you use an ES|QL query, a table is displayed. For example: -:::{image} ../../../images/kibana-rule-types-esql-query-valid.png +:::{image} /explore-analyze/images/kibana-rule-types-esql-query-valid.png :alt: Test ES|QL query returns a table when valid :screenshot: ::: @@ -96,14 +96,14 @@ For each action, you must choose a connector, which provides connection informat After you select a connector, you must set the action frequency. You can choose to create a summary of alerts on each check interval or on a custom interval. For example, send email notifications that summarize the new, ongoing, and recovered alerts at a custom interval: -:::{image} ../../../images/kibana-es-query-rule-action-summary.png +:::{image} /explore-analyze/images/kibana-es-query-rule-action-summary.png :alt: UI for defining alert summary action in an {{es}} query rule :screenshot: ::: Alternatively, you can set the action frequency such that actions run for each alert. Choose how often the action runs (at each check interval, only when the alert status changes, or at a custom action interval). You must also choose an action group, which indicates whether the action runs when the query is matched or when the alert is recovered. Each connector supports a specific set of actions for each action group. For example: -:::{image} ../../../images/kibana-es-query-rule-action-query-matched.png +:::{image} /explore-analyze/images/kibana-es-query-rule-action-query-matched.png :alt: UI for defining a recovery action :screenshot: ::: @@ -125,7 +125,7 @@ Elasticsearch query rule '{{rule.name}}' is active: Rules use rule action variables and Mustache templates to pass contextual details into the alert notifications. There is a set of [variables common to all rules](create-manage-rules.md#defining-rules-actions-variables) and a set that is specific to this rule. To view the list of variables in {{kib}}, click the "add rule variable" button. For example: -:::{image} ../../../images/kibana-es-query-rule-action-variables.png +:::{image} /explore-analyze/images/kibana-es-query-rule-action-variables.png :alt: Passing rule values to an action :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md b/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md index ce51e4682f..c7567ee3a4 100644 --- a/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md +++ b/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md @@ -17,7 +17,7 @@ In **{{stack-manage-app}}** > **{{rules-ui}}**, click **Create rule**. Select th When you create an index threshold rule, you must define the conditions for the rule to detect. For example: -:::{image} ../../../images/kibana-rule-types-index-threshold-conditions.png +:::{image} /explore-analyze/images/kibana-rule-types-index-threshold-conditions.png :alt: Defining index threshold rule conditions in {{kib}} :screenshot: ::: @@ -45,14 +45,14 @@ For each action, you must choose a connector, which provides connection informat After you select a connector, you must set the action frequency. You can choose to create a summary of alerts on each check interval or on a custom interval. For example, summarize the new, ongoing, and recovered alerts at a custom interval: -:::{image} ../../../images/kibana-rule-types-index-threshold-example-action-summary.png +:::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-action-summary.png :alt: UI for defining alert summary action in an index threshold rule :screenshot: ::: Alternatively, you can set the action frequency such that actions run for each alert. Choose how often the action runs (at each check interval, only when the alert status changes, or at a custom action interval). You must also choose an action group, which indicates whether the action runs when the threshold is met or when the alert is recovered. Each connector supports a specific set of actions for each action group. For example: -:::{image} ../../../images/kibana-rule-types-index-threshold-example-action.png +:::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-action.png :alt: UI for defining an action for each alert :screenshot: ::: @@ -90,31 +90,31 @@ In this example, you will use the {{kib}} [sample weblog data set](/explore-anal 1. Provide a rule name. 2. Select an index. Click **Index**, and set **Indices to query** to `kibana_sample_data_logs`. Set the **Time field** to `@timestamp`. - :::{image} ../../../images/kibana-rule-types-index-threshold-example-index.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-index.png :alt: Choosing an index :screenshot: ::: 3. To detect the number of bytes served during the time window, click **When** and select `sum` as the aggregation, and `bytes` as the field to aggregate. - :::{image} ../../../images/kibana-rule-types-index-threshold-example-aggregation.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-aggregation.png :alt: Choosing the aggregation :screenshot: ::: 4. To detect the four sites that have the most traffic, click **Over** and select `top`, enter `4`, and select `host.keyword` as the field. - :::{image} ../../../images/kibana-rule-types-index-threshold-example-grouping.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-grouping.png :alt: Choosing the groups :screenshot: ::: 5. To trigger the rule when any of the top four sites exceeds 420,000 bytes over a 24 hour period, select `is above` and enter `420000`. Then click **For the last**, enter `24`, and select `hours`. - :::{image} ../../../images/kibana-rule-types-index-threshold-example-threshold.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-threshold.png :alt: Setting the threshold :screenshot: ::: 6. Schedule the rule to check every four hours. - :::{image} ../../../images/kibana-rule-types-index-threshold-example-preview.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-preview.png :alt: Setting the check interval :screenshot: ::: @@ -125,7 +125,7 @@ In this example, you will use the {{kib}} [sample weblog data set](/explore-anal 8. Define the actions for your rule. You can add one or more actions to your rule to generate notifications when its conditions are met and when they are no longer met. For each action, you must select a connector, set the action frequency, and compose the notification details. For example, add an action that uses a server log connector to write an entry to the Kibana server log: - :::{image} ../../../images/kibana-rule-types-index-threshold-example-action.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-action.png :alt: Add an action to the rule :screenshot: ::: @@ -135,7 +135,7 @@ In this example, you will use the {{kib}} [sample weblog data set](/explore-anal 9. Save the rule. 3. Find the rule and view its details in **{{stack-manage-app}} > {{rules-ui}}**. For example, you can see the status of the rule and its alerts: - :::{image} ../../../images/kibana-rule-types-index-threshold-example-alerts.png + :::{image} /explore-analyze/images/kibana-rule-types-index-threshold-example-alerts.png :alt: View the list of alerts for the rule :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/testing-connectors.md b/explore-analyze/alerts-cases/alerts/testing-connectors.md index 1c0d6af649..37d5b021d3 100644 --- a/explore-analyze/alerts-cases/alerts/testing-connectors.md +++ b/explore-analyze/alerts-cases/alerts/testing-connectors.md @@ -10,19 +10,19 @@ mapped_pages: In **{{stack-manage-app}} > {{connectors-ui}}**, you can test a newly created connector by navigating to the Test tab of Connector Edit flyout or by clicking "Save & test" button on Create flyout: -:::{image} ../../../images/kibana-connector-save-and-test.png +:::{image} /explore-analyze/images/kibana-connector-save-and-test.png :alt: Rule management page with the errors banner :screenshot: ::: or by directly opening the proper connector edit flyout: -:::{image} ../../../images/kibana-email-connector-test.png +:::{image} /explore-analyze/images/kibana-email-connector-test.png :alt: Rule management page with the errors banner :screenshot: ::: -:::{image} ../../../images/kibana-teams-connector-test.png +:::{image} /explore-analyze/images/kibana-teams-connector-test.png :alt: Five clauses define the condition to detect :screenshot: ::: diff --git a/explore-analyze/alerts-cases/alerts/view-alerts.md b/explore-analyze/alerts-cases/alerts/view-alerts.md index fe5c9559b4..73a8c8b85c 100644 --- a/explore-analyze/alerts-cases/alerts/view-alerts.md +++ b/explore-analyze/alerts-cases/alerts/view-alerts.md @@ -12,7 +12,7 @@ When the conditions of a rule are met, it creates an alert. If the rule has acti You can manage the alerts for each rule in **{{stack-manage-app}}** > **{{rules-ui}}**. Alternatively, manage all your alerts in **{{stack-manage-app}}** > **Alerts**. [preview] -:::{image} ../../../images/kibana-stack-management-alerts-page.png +:::{image} /explore-analyze/images/kibana-stack-management-alerts-page.png :alt: Alerts page with multiple alerts :screenshot: ::: @@ -32,7 +32,7 @@ In **{{stack-manage-app}}** > **Alerts**, you can filter the list (for example, By default, the list contains all the alerts that you have authority to view in the selected time period except those associated with Security rules. To view alerts for Security rules, click the query menu and select **Security rule types**: -:::{image} ../../../images/kibana-stack-management-alerts-query-menu.png +:::{image} /explore-analyze/images/kibana-stack-management-alerts-query-menu.png :alt: The Alerts page with the query menu open :screenshot: ::: diff --git a/explore-analyze/alerts-cases/cases.md b/explore-analyze/alerts-cases/cases.md index 923ac018f4..b1fc7ff713 100644 --- a/explore-analyze/alerts-cases/cases.md +++ b/explore-analyze/alerts-cases/cases.md @@ -12,7 +12,7 @@ Cases are used to open and track issues directly in {{kib}}. You can add assigne You can also optionally add custom fields and case templates. [preview] -:::{image} ../../images/kibana-cases-list.png +:::{image} /explore-analyze/images/kibana-cases-list.png :alt: Cases page :screenshot: ::: diff --git a/explore-analyze/alerts-cases/cases/manage-cases-settings.md b/explore-analyze/alerts-cases/cases/manage-cases-settings.md index 5310f103e9..d8b2bc8e59 100644 --- a/explore-analyze/alerts-cases/cases/manage-cases-settings.md +++ b/explore-analyze/alerts-cases/cases/manage-cases-settings.md @@ -12,7 +12,7 @@ To change case closure options and add custom fields, templates, and connectors To perform these tasks, you must have [full access](setup-cases.md) to the appropriate case and connector features in {{kib}}. -:::{image} ../../../images/kibana-cases-settings.png +:::{image} /explore-analyze/images/kibana-cases-settings.png :alt: View case settings :screenshot: ::: @@ -56,7 +56,7 @@ You can add optional and required fields for customized case collaboration. [8.1 To create a custom field: 1. In the **Custom fields** section, click **Add field**. - :::{image} ../../../images/kibana-cases-custom-fields-add.png + :::{image} /explore-analyze/images/kibana-cases-custom-fields-add.png :alt: Add a custom field in case settings :screenshot: ::: @@ -78,7 +78,7 @@ You can make the case creation process faster and more consistent by adding temp To create a template: 1. In the **Templates** section, click **Add template**. - :::{image} ../../../images/kibana-cases-templates-add.png + :::{image} /explore-analyze/images/kibana-cases-templates-add.png :alt: Add a template in case settings :screenshot: ::: diff --git a/explore-analyze/alerts-cases/cases/manage-cases.md b/explore-analyze/alerts-cases/cases/manage-cases.md index b055ea1d87..3ace7a1701 100644 --- a/explore-analyze/alerts-cases/cases/manage-cases.md +++ b/explore-analyze/alerts-cases/cases/manage-cases.md @@ -15,7 +15,7 @@ To perform these tasks, you must have [full access](setup-cases.md) to the appro Open a new case to keep track of issues and share their details with colleagues. 1. Go to **Management > {{stack-manage-app}} > Cases**, then click **Create case**. - :::{image} ../../../images/kibana-cases-create.png + :::{image} /explore-analyze/images/kibana-cases-create.png :alt: Create a case in {{stack-manage-app}} :screenshot: ::: @@ -76,7 +76,7 @@ When you subsequently add assignees to cases, they receive an email. After you create a case, you can upload and manage files on the **Files** tab: -:::{image} ../../../images/kibana-cases-files.png +:::{image} /explore-analyze/images/kibana-cases-files.png :alt: A list of files attached to a case :screenshot: ::: @@ -96,7 +96,7 @@ Uploaded files are also accessible in **{{stack-manage-app}} > Files**. When you You can also optionally add visualizations. For example, you can portray event and alert data through charts and graphs. -:::{image} ../../../images/kibana-cases-visualization.png +:::{image} /explore-analyze/images/kibana-cases-visualization.png :alt: Adding a visualization as a comment within a case :screenshot: ::: diff --git a/explore-analyze/alerts-cases/watcher/actions.md b/explore-analyze/alerts-cases/watcher/actions.md index b9f92e6c7a..c88400147f 100644 --- a/explore-analyze/alerts-cases/watcher/actions.md +++ b/explore-analyze/alerts-cases/watcher/actions.md @@ -158,7 +158,7 @@ Where `` is the id of the watch and `` is a comma-separated list The following diagram illustrates the throttling decisions made for each action of a watch during its execution: -:::{image} ../../../images/elasticsearch-reference-action-throttling.jpg +:::{image} /explore-analyze/images/elasticsearch-reference-action-throttling.jpg :alt: action throttling ::: diff --git a/explore-analyze/alerts-cases/watcher/enable-watcher.md b/explore-analyze/alerts-cases/watcher/enable-watcher.md index 52d4b4bf54..63ca4f5ba4 100644 --- a/explore-analyze/alerts-cases/watcher/enable-watcher.md +++ b/explore-analyze/alerts-cases/watcher/enable-watcher.md @@ -128,7 +128,7 @@ PUT _watcher/watch/test-alarm **In Elasticsearch versions before 7.0:**, you are not required to use the Elasticsearch keystore. Instead, you can use the console Elasticsearch settings editor for your deployment. -:::{image} ../../../images/cloud-user-settings.png +:::{image} /explore-analyze/images/cloud-user-settings.png :alt: Advanced Alerting configuration ::: diff --git a/explore-analyze/alerts-cases/watcher/how-watcher-works.md b/explore-analyze/alerts-cases/watcher/how-watcher-works.md index 14b8438259..0e339a39da 100644 --- a/explore-analyze/alerts-cases/watcher/how-watcher-works.md +++ b/explore-analyze/alerts-cases/watcher/how-watcher-works.md @@ -145,7 +145,7 @@ When the watch execution finishes, the execution result is recorded as a *Watch The following diagram shows the watch execution process: -:::{image} ../../../images/elasticsearch-reference-watch-execution.jpg +:::{image} /explore-analyze/images/elasticsearch-reference-watch-execution.jpg :alt: watch execution ::: diff --git a/explore-analyze/alerts-cases/watcher/watcher-ui.md b/explore-analyze/alerts-cases/watcher/watcher-ui.md index 1437454027..6d64b8bd89 100644 --- a/explore-analyze/alerts-cases/watcher/watcher-ui.md +++ b/explore-analyze/alerts-cases/watcher/watcher-ui.md @@ -13,7 +13,7 @@ Go to the **Watcher** page using the navigation menu or the [global search field * [Deactivate and delete a watch](#watcher-deactivate) * [Create an advanced watch using API syntax](#watcher-create-advanced-watch) -![Watcher list](../../../images/kibana-watches.png "") +![Watcher list](/explore-analyze/images/kibana-watches.png "") [Alerting on cluster and index events](../../../explore-analyze/alerts-cases/watcher.md) is a good source for detailed information on how watches work. If you are using the UI to create a threshold watch, take a look at the different watcher actions. If you are creating an advanced watch, you should be familiar with the parts of a watch—input, schedule, condition, and actions. @@ -50,7 +50,7 @@ The following example walks you through creating a threshold alert. The alert is 3. In the **Indices to query** field, enter `metricbeat-*` and select `@timestamp` as the time field. 4. Use the default schedule to run the watch every 1 minute. - ![Input and schedule for threshold alert](../../../images/kibana-create-threshold-alert-created.png "") + ![Input and schedule for threshold alert](/explore-analyze/images/kibana-create-threshold-alert-created.png "") ### Add a condition [_add_a_condition] @@ -65,7 +65,7 @@ You should now see a panel with default conditions and a visualization of the da As you change the condition, the visualization is automatically updated. The black line represents the threshold (25%), while the green fluctuating line represents the change in CPU over the set time period. - ![Condition for threshold alert](../../../images/kibana-threshold-alert-condition.png "") + ![Condition for threshold alert](/explore-analyze/images/kibana-threshold-alert-condition.png "") ### Add an action [_add_an_action] @@ -106,13 +106,13 @@ From this page you can drill down into a watch to investigate its history and st The **Execution history** tab shows each time the watch is triggered and the results of the query, whether the condition was met, and what actions were taken. -![Execution history tab](../../../images/kibana-execution-history.png "") +![Execution history tab](/explore-analyze/images/kibana-execution-history.png "") ### Acknowledge action status [_acknowledge_action_status] The **Action statuses** tab lists all actions associated with the watch and the state of each action. Some actions can be acknowledged, which will prevent too many executions of that action for the relevant watch. See [Acknowledgement and throttling](../../../explore-analyze/alerts-cases/watcher/actions.md#actions-ack-throttle) for details. -![Action status tab](../../../images/kibana-alerts-status.png "") +![Action status tab](/explore-analyze/images/kibana-alerts-status.png "") ## Deactivate and delete a watch [watcher-deactivate] @@ -129,7 +129,7 @@ Advanced watches are for users who are more familiar with {{es}} query syntax an On the Watch overview page, click **Create** and choose **Create advanced watch**. An advanced watch requires a name and ID. Name is a user-friendly way to identify the watch, and ID refers to the identifier used by {{es}}. Refer to [Watch definition](../../../explore-analyze/alerts-cases/watcher/how-watcher-works.md#watch-definition) for how to input the watch JSON. -![Create advanced watch](../../../images/kibana-advanced-watch-create.png "") +![Create advanced watch](/explore-analyze/images/kibana-advanced-watch-create.png "") ### Simulate the watch [_simulate_the_watch] @@ -144,7 +144,7 @@ Some watches, such as those using basic auth credentials, contain passwords. For After starting the simulation, you’ll see a results screen. For more information on the fields in the response, see the [Execute watch API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-watcher-execute-watch). -![Create advanced watch](../../../images/kibana-advanced-watch-simulate.png "") +![Create advanced watch](/explore-analyze/images/kibana-advanced-watch-simulate.png "") ### Examples of advanced watches [_examples_of_advanced_watches] diff --git a/explore-analyze/dashboards.md b/explore-analyze/dashboards.md index 443a6aeaea..3964d733f8 100644 --- a/explore-analyze/dashboards.md +++ b/explore-analyze/dashboards.md @@ -12,7 +12,7 @@ Dashboards are the best way to visualize and share insights from your {{es}} dat A **dashboard** is made of one or more **panels** that you can organize as you like. Each panel can display various types of content: **visualizations** such as charts, tables, metrics, and maps, **static annotations** like text or images, or even **specialized views** for Machine Learning or Observability data. -![Example dashboard](../images/kibana-dashboard-overview.png "") +![Example dashboard](/explore-analyze/images/kibana-dashboard-overview.png "") There are several [panel editors](visualize.md#panels-editors) in {{kib}} that let you create and configure different types of visualizations. diff --git a/explore-analyze/dashboards/arrange-panels.md b/explore-analyze/dashboards/arrange-panels.md index 97b93555d1..a6180a559a 100644 --- a/explore-analyze/dashboards/arrange-panels.md +++ b/explore-analyze/dashboards/arrange-panels.md @@ -14,7 +14,7 @@ Compare the data in your panels side-by-side, organize panels by priority, resiz In the toolbar, click **Edit**, then use the following options: -* To move, hover over the panel, click and hold ![The move control icon](../../images/kibana-move-control.png ""), then drag to the new location. +* To move, hover over the panel, click and hold ![The move control icon](/explore-analyze/images/kibana-move-control.png ""), then drag to the new location. * To resize, click the resize control, then drag to the new dimensions. * To maximize to full screen, open the panel menu and click **Maximize**. diff --git a/explore-analyze/dashboards/create-dashboard-of-panels-with-ecommerce-data.md b/explore-analyze/dashboards/create-dashboard-of-panels-with-ecommerce-data.md index a47eb340a5..178a3c49d9 100644 --- a/explore-analyze/dashboards/create-dashboard-of-panels-with-ecommerce-data.md +++ b/explore-analyze/dashboards/create-dashboard-of-panels-with-ecommerce-data.md @@ -263,7 +263,7 @@ To create a week-over-week comparison, shift **Count of records [1]** by one wee To use custom time shifts, enter the time value and increment, then press Enter. For example, enter **1w** to use the **1 week ago** time shift. - :::{image} ../../images/kibana-lens_time_shift.png + :::{image} /explore-analyze/images/kibana-lens_time_shift.png :alt: Line chart with week-over-week sales comparison :screenshot: :width: 50% @@ -288,7 +288,7 @@ To compare time range changes as a percent, create a bar chart that compares the 6. From the **Value format** dropdown, select **Percent**, then enter `0` in the **Decimals** field. 7. Click **Close**. - :::{image} ../../images/kibana-lens_percent_chage.png + :::{image} /explore-analyze/images/kibana-lens_percent_chage.png :alt: Bar chart with percent change in sales between the current time and the previous week :screenshot: :width: 50% @@ -321,7 +321,7 @@ To split the metric, add columns for each continent using the **Columns** field: 1. From the **Available fields** list, drag **geoip.continent_name** to the **Split metrics by** field in the layer pane. - :::{image} ../../images/kibana-lens_table_over_time.png + :::{image} /explore-analyze/images/kibana-lens_table_over_time.png :alt: Date histogram table with groups for the customer count metric :screenshot: :width: 50% diff --git a/explore-analyze/dashboards/create-dashboard-of-panels-with-web-server-data.md b/explore-analyze/dashboards/create-dashboard-of-panels-with-web-server-data.md index c914831ce1..f1944a6056 100644 --- a/explore-analyze/dashboards/create-dashboard-of-panels-with-web-server-data.md +++ b/explore-analyze/dashboards/create-dashboard-of-panels-with-web-server-data.md @@ -51,7 +51,7 @@ To create the visualizations in this tutorial, you’ll use the following fields Click a field name to view more details, such as its top values and distribution. -:::{image} ../../images/tutorial-field-more-info.gif +:::{image} /explore-analyze/images/tutorial-field-more-info.gif :alt: Clicking a field name to view more details :screenshot: :width: 50% @@ -101,7 +101,7 @@ To visualize the **bytes** field over time: 3. To zoom in on the data, click and drag your cursor across the bars. - :::{image} ../../images/kibana-lens_end_to_end_3_1_1.gif + :::{image} /explore-analyze/images/kibana-lens_end_to_end_3_1_1.gif :alt: Zoom in on the data :screenshot: ::: @@ -143,7 +143,7 @@ To save space on the dashboard, hide the axis labels. Since you removed the axis labels, add a panel title: -1. Hover over the panel and click ![Settings icon](../../images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. +1. Hover over the panel and click ![Settings icon](/explore-analyze/images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. 2. In the **Title** field, enter `Median of bytes`, then click **Apply**. :::{image} ../../images/kibana-lens_lineChartMetricOverTime_8.4.0.png @@ -229,7 +229,7 @@ Specify the file size ranges: * **Ranges** — `10240` → `+∞` * **Label** — `Above 10KB` - :::{image} ../../images/kibana-lens_end_to_end_6_1.png + :::{image} /explore-analyze/images/kibana-lens_end_to_end_6_1.png :alt: Custom ranges configuration :screenshot: ::: @@ -250,7 +250,7 @@ To display the values as a percentage of the sum of all values, use the **Pie** Add a panel title: -1. Hover over the panel and click ![Settings icon](../../images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. +1. Hover over the panel and click ![Settings icon](/explore-analyze/images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. 2. In the **Title** field, enter `Sum of bytes from large requests`, then click **Apply**. @@ -279,7 +279,7 @@ The distribution of a number can help you find patterns. For example, you can an Add a panel title: -1. Hover over the panel and click ![Settings icon](../../images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. +1. Hover over the panel and click ![Settings icon](/explore-analyze/images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. 2. In the **Title** field, enter `Website traffic`, then click **Apply**. @@ -317,7 +317,7 @@ Add the user geography grouping: 1. From the **Available fields** list, drag **geo.srcdest** to the workspace. 2. To change the **Group by** order, drag **Top 3 values of geo.srcdest** in the layer pane so that appears first. - :::{image} ../../images/kibana-lens_end_to_end_7_2.png + :::{image} /explore-analyze/images/kibana-lens_end_to_end_7_2.png :alt: Treemap visualization :screenshot: :width: 50% @@ -339,7 +339,7 @@ Remove the documents that do not match the filter criteria: Add a panel title: -1. Hover over the panel and click ![Settings icon](../../images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. +1. Hover over the panel and click ![Settings icon](/explore-analyze/images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. 2. In the **Title** field, enter `Page views by location and referrer`, then click **Apply**. @@ -370,6 +370,6 @@ Now that you have a complete overview of your web server data, save the dashboar 3. Select **Store time with dashboard**. 4. Click **Save**. You will be identified as the **creator** of the dashboard. If you or another user edit the dashboard, you can also view the **last editor** when checking the dashboard information. -:::{image} ../../images/kibana-dashboard-creator-editor.png +:::{image} /explore-analyze/images/kibana-dashboard-creator-editor.png :alt: Information panel of a dashboard showing its creator and last editor ::: diff --git a/explore-analyze/dashboards/import-dashboards.md b/explore-analyze/dashboards/import-dashboards.md index 733e1aca8f..c10a56833e 100644 --- a/explore-analyze/dashboards/import-dashboards.md +++ b/explore-analyze/dashboards/import-dashboards.md @@ -15,4 +15,4 @@ When importing dashboards, you also import their related objects, such as data v * **Check for existing objects**: When selected, objects are not imported when another object with the same ID already exists in this space or cluster. For example, if you import a dashboard that uses a data view which already exists, the data view is not imported and the dashboard uses the existing data view instead. You can also chose to select manually which of the imported or the existing objects are kept by selecting **Request action on conflict**. * **Create new objects with random IDs**: All related objects are imported and are assigned a new ID to avoid conflicts. -![Import panel](../../images/kibana-dashboard-import-saved-object.png "") +![Import panel](/explore-analyze/images/kibana-dashboard-import-saved-object.png "") diff --git a/explore-analyze/dashboards/managing.md b/explore-analyze/dashboards/managing.md index c00884d178..51512e0a81 100644 --- a/explore-analyze/dashboards/managing.md +++ b/explore-analyze/dashboards/managing.md @@ -34,7 +34,7 @@ The creator information is only available for dashboards created on or after ver :::: -:::{image} ../../images/kibana-dashboard-filter-by-creator.png +:::{image} /explore-analyze/images/kibana-dashboard-filter-by-creator.png :alt: Option to filter the list of dashboards by creator ::: @@ -51,7 +51,7 @@ All dashboards marked as favorite are gathered in the **Starred** tab so you can The list of starred dashboards is personal. Dashboards marked as favorite by other users only appear for those users. -![List of starred dashboards](../../images/kibana-dashboard-starred-list.png "") +![List of starred dashboards](/explore-analyze/images/kibana-dashboard-starred-list.png "") ## View dashboard usage [_view_dashboard_usage] @@ -62,4 +62,4 @@ You can check how much a dashboard is being used by clicking its **View details* These details include a graph showing the total number of views during the last 90 days. -![Graph showing the number of views during the last 90 days](../../images/kibana-dashboard-usage-count.png "") +![Graph showing the number of views during the last 90 days](/explore-analyze/images/kibana-dashboard-usage-count.png "") diff --git a/explore-analyze/dashboards/sharing.md b/explore-analyze/dashboards/sharing.md index 2b7cc70313..025b00a6c5 100644 --- a/explore-analyze/dashboards/sharing.md +++ b/explore-analyze/dashboards/sharing.md @@ -10,7 +10,7 @@ mapped_pages: To share a dashboard with a larger audience, click **Share** in the toolbar. For detailed information about the sharing options, refer to [Reporting](../report-and-share.md). -:::{image} ../../images/share-dashboard-copy-link.gif +:::{image} /explore-analyze/images/share-dashboard-copy-link.gif :alt: getting a shareable link for a dashboard ::: @@ -28,6 +28,6 @@ You can export dashboards from **Stack Management** > **Saved Objects**. To conf 2. Enable **Include related objects** if you want that objects associated to the selected dashboard, such as data views and visualizations, also get exported. This option is enabled by default and recommended if you plan to import that dashboard again in a different space or cluster. 3. Select **Export**. -![Option to export a dashboard](../../images/kibana-dashboard-export-saved-object.png "") +![Option to export a dashboard](/explore-analyze/images/kibana-dashboard-export-saved-object.png "") To automate {{kib}}, you can export dashboards as NDJSON using the [Export saved objects API](https://www.elastic.co/docs/api/doc/kibana/group/endpoint-saved-objects). It is important to export dashboards with all necessary references. diff --git a/explore-analyze/dashboards/using.md b/explore-analyze/dashboards/using.md index 595b0e6b10..125dafe0cf 100644 --- a/explore-analyze/dashboards/using.md +++ b/explore-analyze/dashboards/using.md @@ -45,7 +45,7 @@ You can interact with some panel visualizations to explore specific data more in As an alternative to the main query bar, you can filter dashboard data by defining individual conditions on specific fields and values, and by combining these conditions together in a filter pill. -:::{image} ../../images/kibana-dashboard-filter-editor.png +:::{image} /explore-analyze/images/kibana-dashboard-filter-editor.png :alt: Filter editor with 2 conditions ::: @@ -54,7 +54,7 @@ As an alternative to the main query bar, you can filter dashboard data by defini The query bar lets you build filters using [{{kib}} Query Language (KQL)](../query-filter/languages/kql.md). When typing, it dynamically suggests matching fields, operators, and values to help you get the exact results that you want. -:::{image} ../../images/kibana-dashboard-filter-kql.png +:::{image} /explore-analyze/images/kibana-dashboard-filter-kql.png :alt: KQL filter dynamically suggesting values ::: @@ -68,7 +68,7 @@ The data visible in a dashboard highly depends on the time range that is applied The global time range menu is located right next to the query bar, in the dashboard’s header. With this menu, you can select the time range to apply, and set the frequency for refreshing the dashboard data. Setting the time range is a common action in {{kib}}. Refer to [Set the time range](../query-filter/filtering.md) for more details. -:::{image} ../../images/kibana-dashboard-global-time-range.png +:::{image} /explore-analyze/images/kibana-dashboard-global-time-range.png :alt: Time range menu with multiple time range suggestions ::: @@ -77,7 +77,7 @@ The global time range menu is located right next to the query bar, in the dashbo **To apply a panel-level time range:** -1. Hover over the panel and click ![Settings icon](../../images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. +1. Hover over the panel and click ![Settings icon](/explore-analyze/images/kibana-settings-icon-hover-action.png ""). The **Settings** flyout appears. 2. Turn on **Apply a custom time range**. 3. Enter the time range you want to view, then click **Apply**. @@ -182,7 +182,7 @@ You can view it in more details and edit it by clicking the filter. You can display dashboards in full screen mode to gain visual space and view or show visualizations without the rest of the {{kib}} interface. -:::{image} ../../images/kibana-dashboard-full-screen.png +:::{image} /explore-analyze/images/kibana-dashboard-full-screen.png :alt: A dashboard in full screen mode ::: @@ -193,6 +193,6 @@ When sharing a dashboard with a link while a panel is in maximized view, the gen :::: -:::{image} ../../images/kibana-dashboard-panel-maximized.png +:::{image} /explore-analyze/images/kibana-dashboard-panel-maximized.png :alt: A maximized panel in a dashboard ::: diff --git a/explore-analyze/discover.md b/explore-analyze/discover.md index 45e0d58e46..3be758f777 100644 --- a/explore-analyze/discover.md +++ b/explore-analyze/discover.md @@ -13,7 +13,7 @@ You have questions about your data. What pages on your website contain a specifi $$$save-your-search$$$ With **Discover**, you can quickly search and filter your data, get information about the structure of the fields, and display your findings in a visualization. You can also customize and save your Discover sessions and place them on a dashboard. -:::{image} ../images/kibana-hello-field.png +:::{image} /explore-analyze/images/kibana-hello-field.png :alt: A view of the Discover app :screenshot: ::: diff --git a/explore-analyze/discover/discover-get-started.md b/explore-analyze/discover/discover-get-started.md index d8a8d32c77..d655654f6e 100644 --- a/explore-analyze/discover/discover-get-started.md +++ b/explore-analyze/discover/discover-get-started.md @@ -33,7 +33,7 @@ Select the data you want to explore, and then specify the time range in which to You can also [try {{esql}}](try-esql.md), that let's you query any data you have in {{es}} without specifying a {{data-source}} first. :::: If you’re using sample data, data views are automatically created and are ready to use. - :::{image} ../../images/kibana-discover-data-view.png + :::{image} /explore-analyze/images/kibana-discover-data-view.png :alt: How to set the {{data-source}} in Discover :screenshot: :width: 300px @@ -58,7 +58,7 @@ You can later filter the data that shows in the chart and in the table by specif 1. In the sidebar, check the available fields. It’s very common to have hundreds of fields. Use the search at the top of that sidebar to look for specific terms in the field names. In this example, we’ve entered `ma` in the search field to find the `manufacturer` field. - ![Fields list that displays the top five search results](../../images/kibana-discover-sidebar-available-fields.png "title =40%") + ![Fields list that displays the top five search results](/explore-analyze/images/kibana-discover-sidebar-available-fields.png "title =40%") ::::{tip} You can combine multiple keywords or characters. For example, `geo dest` finds `geo.dest` and `geo.src.dest`. :::: @@ -67,9 +67,9 @@ You can later filter the data that shows in the chart and in the table by specif **Discover** shows the top 10 values and the number of records used to calculate those values. 3. Select the **Plus** icon to add fields to the results table. You can also drag them from the list into the table. - ![How to add a field as a column in the table](../../images/kibana-discover-add-field.png "title =50%") + ![How to add a field as a column in the table](/explore-analyze/images/kibana-discover-add-field.png "title =50%") When you add fields to the table, the **Summary** column is replaced. - ![Document table with fields for manufacturer](../../images/kibana-document-table.png "") + ![Document table with fields for manufacturer](/explore-analyze/images/kibana-document-table.png "") 4. Arrange the view to your liking to display the fields and data you care most about using the various display options of **Discover**. For example, you can change the order and size of columns, expand the table to be in full screen or collapse the chart and the list of fields. Check [Customize the Discover view](document-explorer.md). 5. **Save** your changes to be able to open the same view later on and explore your data further. @@ -125,19 +125,19 @@ In the following example, we’re adding 2 fields: A simple "Hello world" field, If a field can be [aggregated](../query-filter/aggregations.md), you can quickly visualize it in detail by opening it in **Lens** from **Discover**. **Lens** is the default visualization editor in {{kib}}. 1. In the list of fields, find an aggregatable field. For example, with the sample data, you can look for `day_of_week`. - ![Top values for the day_of_week field](../../images/kibana-discover-day-of-week.png "title =60%") + ![Top values for the day_of_week field](/explore-analyze/images/kibana-discover-day-of-week.png "title =60%") 2. In the popup, click **Visualize**. {{kib}} creates a **Lens** visualization best suited for this field. 3. In **Lens**, from the **Available fields** list, drag and drop more fields to refine the visualization. In this example, we’re adding the `manufacturer.keyword` field onto the workspace, which automatically adds a breakdown of the top values to the visualization. - ![Visualization that opens from Discover based on your data](../../images/kibana-discover-from-visualize.png "") + ![Visualization that opens from Discover based on your data](/explore-analyze/images/kibana-discover-from-visualize.png "") 4. Save the visualization if you’d like to add it to a dashboard or keep it in the Visualize library for later use. -For geo point fields (![Geo point field icon](../../images/kibana-geoip-icon.png "")), if you click **Visualize**, your data appears in a map. +For geo point fields (![Geo point field icon](/explore-analyze/images/kibana-geoip-icon.png "")), if you click **Visualize**, your data appears in a map. -![Map containing documents](../../images/kibana-discover-maps.png "") +![Map containing documents](/explore-analyze/images/kibana-discover-maps.png "") ### Find and highlight values in the table [in-table-search] @@ -165,7 +165,7 @@ You can use **Discover** to compare and diff the field values of multiple result :::: - ![Comparison view in Discover](../../images/kibana-discover-compare-rows.png "") + ![Comparison view in Discover](/explore-analyze/images/kibana-discover-compare-rows.png "") 4. Optionally, customize the **Comparison settings** to your liking. You can for example choose to not highlight the differences, to show them more granularly at the line, word, or character level, or even to hide fields where the value matches for all results. 5. Exit the comparison view at any time using the **Exit comparison mode** button. @@ -190,9 +190,9 @@ You can copy the content of a single cell to your clipboard from the quick actio $$$document-explorer-expand-documents$$$ Dive into an individual document to view its fields and the documents that occurred before and after it. -1. In the document table, click the expand icon ![double arrow icon to open a flyout with the document details](../../images/kibana-expand-icon-2.png "") to show document details. +1. In the document table, click the expand icon ![double arrow icon to open a flyout with the document details](/explore-analyze/images/kibana-expand-icon-2.png "") to show document details. - ![Table view with document expanded](../../images/kibana-document-table-expanded.png "") + ![Table view with document expanded](/explore-analyze/images/kibana-document-table-expanded.png "") 2. Scan through the fields and their values. You can filter the table in several ways: @@ -215,7 +215,7 @@ Dive into an individual document to view its fields and the documents that occur One of the unique capabilities of **Discover** is the ability to combine free text search with filtering based on structured data. To search all fields, enter a simple string in the query bar. -![Search field in Discover](../../images/kibana-discover-search-field.png "") +![Search field in Discover](/explore-analyze/images/kibana-discover-search-field.png "") To search particular fields and build more complex queries, use the [Kibana Query language](../query-filter/languages/kql.md). As you type, KQL prompts you with the fields you can search and the operators you can use to build a structured query. @@ -235,10 +235,10 @@ With the query input, you can filter data using the KQL or Lucene languages. You For example, exclude results from the ecommerce sample data view where day of week is not Wednesday: -1. Click ![Add icon](../../images/kibana-add-icon.png "") next to the query bar. +1. Click ![Add icon](/explore-analyze/images/kibana-add-icon.png "") next to the query bar. 2. In the **Add filter** pop-up, set the field to **day_of_week**, the operator to **is not**, and the value to **Wednesday**. - ![Add filter dialog in Discover](../../images/kibana-discover-add-filter.png "") + ![Add filter dialog in Discover](/explore-analyze/images/kibana-discover-add-filter.png "") 3. Click **Add filter**. 4. Continue your exploration by adding more filters. diff --git a/explore-analyze/discover/discover-search-for-relevance.md b/explore-analyze/discover/discover-search-for-relevance.md index 97b4e5f69b..a71bc24b4e 100644 --- a/explore-analyze/discover/discover-search-for-relevance.md +++ b/explore-analyze/discover/discover-search-for-relevance.md @@ -31,9 +31,9 @@ This example shows how to use **Discover** to list your documents from most rele 6. To turn off sorting by the `timestamp` field, click the **field sorted** option, and then click **Clear sorting.** 7. Open the **Pick fields to sort by** menu, and then click **_score**. 8. Select **High-Low**. - ![Field sorting popover](../../images/kibana-field-sorting-popover.png "title =50%") + ![Field sorting popover](/explore-analyze/images/kibana-field-sorting-popover.png "title =50%") Your table now sorts documents from most to least relevant. - :::{image} ../../images/kibana-discover-search-for-relevance.png + :::{image} /explore-analyze/images/kibana-discover-search-for-relevance.png :alt: Documents are sorted from most relevant to least relevant. :screenshot: ::: diff --git a/explore-analyze/discover/document-explorer.md b/explore-analyze/discover/document-explorer.md index f73b477337..7f4df56543 100644 --- a/explore-analyze/discover/document-explorer.md +++ b/explore-analyze/discover/document-explorer.md @@ -10,7 +10,7 @@ mapped_pages: Fine tune your explorations by customizing **Discover** to bring out the the best view of your documents. -:::{image} ../../images/kibana-hello-field.png +:::{image} /explore-analyze/images/kibana-hello-field.png :alt: A view of the Discover app :screenshot: ::: @@ -26,7 +26,7 @@ Fine tune your explorations by customizing **Discover** to bring out the the bes Customize the appearance of the document table and its contents to your liking. -![Options to customize the table in Discover](../../images/kibana-discover-customize-table.png "") +![Options to customize the table in Discover](/explore-analyze/images/kibana-discover-customize-table.png "") ### Reorder and resize the columns [document-explorer-columns] @@ -57,7 +57,7 @@ When the number of results returned by your search query (displayed at the top o On the last page of the table, a message indicates that you’ve reached the end of the loaded search results. From that message, you can choose to load more results to continue exploring. -![Limit sample size in Discover](../../images/kibana-discover-limit-sample-size.png "title =50%") +![Limit sample size in Discover](/explore-analyze/images/kibana-discover-limit-sample-size.png "title =50%") ### Sort the fields [document-explorer-sort-data] @@ -69,11 +69,11 @@ To add or remove a sort on a single field, click the column header, and then sel To sort by multiple fields: 1. Click the **Sort fields** option. - ![Pop-up in document table for sorting columns](../../images/kibana-document-explorer-sort-data.png "title =50%") + ![Pop-up in document table for sorting columns](/explore-analyze/images/kibana-document-explorer-sort-data.png "title =50%") 2. To add fields to the sort, select their names from the dropdown menu. By default, columns are sorted in the order they are added. - :::{image} ../../images/kibana-document-explorer-multi-field.png + :::{image} /explore-analyze/images/kibana-document-explorer-multi-field.png :alt: Multi field sort in the document table :screenshot: :width: 50% @@ -98,7 +98,7 @@ Narrow your results to a subset of documents so you’re comparing just the data 1. Select the documents you want to compare. 2. Click the **Selected** option, and then select **Show selected documents only**. - :::{image} ../../images/kibana-document-explorer-compare-data.png + :::{image} /explore-analyze/images/kibana-document-explorer-compare-data.png :alt: Compare data in the document table :screenshot: :width: 50% @@ -112,7 +112,7 @@ You can also compare individual field values using the [**Compare selected** opt To change the numbers of results you want to display on each page, use the **Rows per page** menu. The default is 100 results per page. -:::{image} ../../images/kibana-document-table-rows-per-page.png +:::{image} /explore-analyze/images/kibana-document-table-rows-per-page.png :alt: Menu with options for setting the number of results in the document table :screenshot: ::: diff --git a/explore-analyze/discover/run-pattern-analysis-discover.md b/explore-analyze/discover/run-pattern-analysis-discover.md index 6d7c39f5de..2d92fa45d0 100644 --- a/explore-analyze/discover/run-pattern-analysis-discover.md +++ b/explore-analyze/discover/run-pattern-analysis-discover.md @@ -19,7 +19,7 @@ This example uses the [sample web logs data](../index.md#gs-get-data-into-kibana 3. If you don’t see any results, expand the time range, for example, to **Last 15 days**. 4. Click the **Patterns** tab next to **Documents** and **Field statistics**. The pattern analysis starts. The results are displayed under the chart. You can change the analyzed field by using the field selector. In the **Pattern analysis menu**, you can change the **Minimum time range**. This option enables you to widen the time range for calculating patterns which improves accuracy. The patterns, however, are still displayed by the time range you selected in step 3. -:::{image} ../../images/kibana-log-pattern-analysis-results.png +:::{image} /explore-analyze/images/kibana-log-pattern-analysis-results.png :alt: Log pattern analysis results in Discover. :screenshot: ::: diff --git a/explore-analyze/discover/save-open-search.md b/explore-analyze/discover/save-open-search.md index 83aa0b7f38..e8f4fcfdd2 100644 --- a/explore-analyze/discover/save-open-search.md +++ b/explore-analyze/discover/save-open-search.md @@ -16,7 +16,7 @@ A saved Discover session is a convenient way to reuse a search that you’ve cre If you don’t have sufficient privileges to save Discover sessions, the following indicator is displayed and the **Save** button is not visible. For more information, refer to [Granting access to {{kib}}](../../deploy-manage/users-roles/cluster-or-deployment-auth/built-in-roles.md). -:::{image} ../../images/kibana-read-only-badge.png +:::{image} /explore-analyze/images/kibana-read-only-badge.png :alt: Example of Discover's read only access indicator in Kibana's header :screenshot: ::: diff --git a/explore-analyze/discover/search-sessions.md b/explore-analyze/discover/search-sessions.md index ad1f465f71..6a0ccca1e7 100644 --- a/explore-analyze/discover/search-sessions.md +++ b/explore-analyze/discover/search-sessions.md @@ -19,7 +19,7 @@ Sometimes you might need to search through large amounts of data, no matter how Save your search session from **Discover** or **Dashboard**, and when your session is complete, view and manage it in **Stack Management**. Search sessions are [enabled by default](kibana://reference/configuration-reference/search-sessions-settings.md). -:::{image} ../../images/kibana-search-session.png +:::{image} /explore-analyze/images/kibana-search-session.png :alt: Search Session indicator displaying the current state of the search :screenshot: ::: @@ -39,11 +39,11 @@ You’re trying to understand a trend you see on a dashboard. You need to look a Your search session begins automatically. The icon after the dashboard title displays the current state of the search session. A clock icon indicates the search session is in progress. A checkmark indicates that the search session is complete. 2. To continue a search in the background, click the clock icon, and then click **Save session**. - ![Search Session indicator displaying the current state of the search](../../images/kibana-search-session-awhile.png "title =50%") + ![Search Session indicator displaying the current state of the search](/explore-analyze/images/kibana-search-session-awhile.png "title =50%") Once you save a search session, you can start a new search, navigate to a different application, or close the browser. 3. To view your saved search sessions, go to the **Search Sessions** management page using the navigation menu or the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). For a saved or completed session, you can also open this view from the search sessions popup. - ![Search Sessions management view with actions for inspecting](../../images/kibana-search-sessions-menu.png "") + ![Search Sessions management view with actions for inspecting](/explore-analyze/images/kibana-search-sessions-menu.png "") 4. Use the edit menu in **Search Sessions** to: diff --git a/explore-analyze/discover/show-field-statistics.md b/explore-analyze/discover/show-field-statistics.md index ee5e7f4104..2502508553 100644 --- a/explore-analyze/discover/show-field-statistics.md +++ b/explore-analyze/discover/show-field-statistics.md @@ -21,7 +21,7 @@ This example explores the fields in the [sample web logs data](../index.md#gs-ge 4. Click **Field statistics**. The table summarizes how many documents in the sample contain each field for the selected time period the number of distinct values, and the distribution. - :::{image} ../../images/kibana-field-statistics-view.png + :::{image} /explore-analyze/images/kibana-field-statistics-view.png :alt: Field statistics view in Discover showing a summary of document data. :screenshot: ::: @@ -29,7 +29,7 @@ This example explores the fields in the [sample web logs data](../index.md#gs-ge 5. Expand the `hour_of_day` field. For numeric fields, **Discover** provides the document statistics, minimum, median, and maximum values, a list of top values, and a distribution chart. Use this chart to get a better idea of how the values in the data are clustered. - :::{image} ../../images/kibana-field-statistics-numeric.png + :::{image} /explore-analyze/images/kibana-field-statistics-numeric.png :alt: Field statistics for a numeric field. :screenshot: ::: @@ -38,11 +38,11 @@ This example explores the fields in the [sample web logs data](../index.md#gs-ge For geo fields, **Discover** provides the document statistics, examples, and a map of the coordinates. - :::{image} ../../images/kibana-field-statistics-geo.png + :::{image} /explore-analyze/images/kibana-field-statistics-geo.png :alt: Field statistics for a geo field. :screenshot: ::: 7. Explore additional field types to see the statistics that **Discover** provides. -8. To create a visualization of the field data, click ![Click the magnifying glass icon to create a visualization of the data in Lens](../../images/kibana-visualization-icon.png "") or ![Click the Maps icon to explore the data in a map](../../images/kibana-map-icon.png "") in the **Actions** column. +8. To create a visualization of the field data, click ![Click the magnifying glass icon to create a visualization of the data in Lens](/explore-analyze/images/kibana-visualization-icon.png "") or ![Click the Maps icon to explore the data in a map](/explore-analyze/images/kibana-map-icon.png "") in the **Actions** column. diff --git a/explore-analyze/discover/try-esql.md b/explore-analyze/discover/try-esql.md index f2ebec7c4d..6774946a13 100644 --- a/explore-analyze/discover/try-esql.md +++ b/explore-analyze/discover/try-esql.md @@ -50,7 +50,7 @@ Let’s say we want to find out what operating system users have and how much RA :::: 3. Click **▶Run**. - ![An image of the query result](../../images/kibana-esql-machine-os-ram.png "") + ![An image of the query result](/explore-analyze/images/kibana-esql-machine-os-ram.png "") ::::{note} {{esql}} keywords are not case sensitive. :::: @@ -70,7 +70,7 @@ Let’s add `geo.dest` to our query, to find out the geographical destination of ::::{note} When you don’t specify any specific fields to retain using `KEEP`, the visualization isn’t broken down automatically. Instead, an additional option appears above the visualization and lets you select a field manually. :::: - ![An image of the extended query result](../../images/kibana-esql-limit.png "") + ![An image of the extended query result](/explore-analyze/images/kibana-esql-limit.png "") We will now take it a step further to sort the data by machine ram and filter out the `GB` destination. @@ -87,7 +87,7 @@ We will now take it a step further to sort the data by machine ram and filter ou 2. Click **▶Run** again. The table and visualization no longer show results for which the `geo.dest` field value is "GB", and the results are now sorted in descending order in the table based on the `machine.ram` field. - ![An image of the full query result](../../images/kibana-esql-full-query.png "") + ![An image of the full query result](/explore-analyze/images/kibana-esql-full-query.png "") 3. Click **Save** to save the query and visualization to a dashboard. @@ -114,7 +114,7 @@ FROM kibana_sample_data_ecommerce | KEEP customer_first_name, email, products._id.keyword ``` -:::{image} ../../images/kibana-esql-no-time-series.png +:::{image} /explore-analyze/images/kibana-esql-no-time-series.png :alt: ESQL query without time series capabilities enabled ::: @@ -125,6 +125,6 @@ FROM kibana_sample_data_ecommerce | WHERE order_date >= ?_tstart and order_date <= ?_tend ``` -:::{image} ../../images/kibana-esql-custom-time-series.png +:::{image} /explore-analyze/images/kibana-esql-custom-time-series.png :alt: ESQL query with a custom time field enabled ::: diff --git a/explore-analyze/elastic-inference/inference-api.md b/explore-analyze/elastic-inference/inference-api.md index b00aa3a6a4..700327b37a 100644 --- a/explore-analyze/elastic-inference/inference-api.md +++ b/explore-analyze/elastic-inference/inference-api.md @@ -20,7 +20,7 @@ You can also manage inference endpoints using the UI. The **Inference endpoints** page provides an interface for managing inference endpoints. -:::{image} ../../images/kibana-inference-endpoints-ui.png +:::{image} /explore-analyze/images/kibana-inference-endpoints-ui.png :alt: Inference endpoints UI :screenshot: ::: diff --git a/explore-analyze/find-and-organize/data-views.md b/explore-analyze/find-and-organize/data-views.md index 77f021c017..b0fcb631a6 100644 --- a/explore-analyze/find-and-organize/data-views.md +++ b/explore-analyze/find-and-organize/data-views.md @@ -42,7 +42,7 @@ If you collected data using one of the {{kib}} [ingest options](../../manage-dat 1. Open **Lens** or **Discover**, and then open the data view menu. - :::{image} ../../images/kibana-discover-data-view.png + :::{image} /explore-analyze/images/kibana-discover-data-view.png :alt: How to set the {{data-source}} in Discover :screenshot: :width: 50% @@ -51,7 +51,7 @@ If you collected data using one of the {{kib}} [ingest options](../../manage-dat 2. Click **Create a {{data-source}}**. 3. Give your {{data-source}} a name. 4. Start typing in the **Index pattern** field, and {{kib}} looks for the names of indices, data streams, and aliases that match your input. You can view all available sources or only the sources that the data view targets. - ![Create data view](../../images/kibana-create-data-view.png "") + ![Create data view](/explore-analyze/images/kibana-create-data-view.png "") * To match multiple sources, use a wildcard (*). `filebeat-*` matches `filebeat-apache-a`, `filebeat-apache-b`, and so on. * To match multiple single sources, enter their names, separated by a comma. Do not include a space after the comma. `filebeat-a,filebeat-b` matches two indices. @@ -79,7 +79,7 @@ Want to explore your data or create a visualization without saving it as a data A temporary {{data-source}} remains in your space until you change apps, or until you save it. -:::{image} ../../images/ad-hoc-data-view.gif +:::{image} /explore-analyze/images/ad-hoc-data-view.gif :alt: how to create an ad-hoc data view :screenshot: ::: @@ -169,7 +169,7 @@ Deleting a {{data-source}} breaks all visualizations, saved Discover sessions, a 1. Go to the **Data Views** management page using the navigation menu or the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). -2. Find the {{data-source}} that you want to delete, and then click ![Delete icon](../../images/kibana-delete.png "") in the **Actions** column. +2. Find the {{data-source}} that you want to delete, and then click ![Delete icon](/explore-analyze/images/kibana-delete.png "") in the **Actions** column. ## {{data-source}} field cache [data-view-field-cache] @@ -233,7 +233,7 @@ Return `Hello World!`: emit("Hello World!"); ``` -![Runtime field with keyword type](../../images/kibana-runtime_field.png "") +![Runtime field with keyword type](/explore-analyze/images/kibana-runtime_field.png "") #### Perform a calculation on a single field [perform-a-calculation-on-a-single-field] @@ -273,7 +273,7 @@ emit('subfield_a', 'Hello'); emit('subfield_b', 42); ``` -![Runtime field with composite type](../../images/kibana-runtime_field_composite.png "") +![Runtime field with composite type](/explore-analyze/images/kibana-runtime_field_composite.png "") #### Replace nulls with blanks [replace-nulls-with-blanks] @@ -461,7 +461,7 @@ Built-in validation is unsupported for scripted fields. When your scripts contai 1. Go to the **Data Views** management page using the navigation menu or the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). 2. Click the data view that contains the field you want to change. -3. Find the field, then open the edit options (![Data field edit icon](../../images/kibana-edit_icon.png "")). +3. Find the field, then open the edit options (![Data field edit icon](/explore-analyze/images/kibana-edit_icon.png "")). 4. Select **Set custom label**, then enter a **Custom label** for the field. 5. Select **Set format**, then enter the **Format** for the field. diff --git a/explore-analyze/find-and-organize/files.md b/explore-analyze/find-and-organize/files.md index 0fc3242ef5..41b44eab76 100644 --- a/explore-analyze/find-and-organize/files.md +++ b/explore-analyze/find-and-organize/files.md @@ -12,7 +12,7 @@ Several features let you upload files. For example, you can add files to [cases] You can access and manage all of the files currently stored in {{kib}} from the **Files** page. -:::{image} ../../images/serverless-file-management.png +:::{image} /explore-analyze/images/serverless-file-management.png :alt: Files UI :screenshot: ::: diff --git a/explore-analyze/find-and-organize/find-apps-and-objects.md b/explore-analyze/find-and-organize/find-apps-and-objects.md index a03946c932..386e5b19a0 100644 --- a/explore-analyze/find-and-organize/find-apps-and-objects.md +++ b/explore-analyze/find-and-organize/find-apps-and-objects.md @@ -10,7 +10,7 @@ mapped_pages: To quickly find apps and the objects you create, use the search field in the global header. Search suggestions include deep links into applications, allowing you to directly navigate to the views you need most. -:::{image} ../../images/kibana-app-navigation-search.png +:::{image} /explore-analyze/images/kibana-app-navigation-search.png :alt: Example of searching for apps :screenshot: :width: 60% @@ -31,7 +31,7 @@ You can search for objects by type, name, and tag. To get the most from the sear This example searches for visualizations with the tag `design` . -:::{image} ../../images/kibana-tags-search.png +:::{image} /explore-analyze/images/kibana-tags-search.png :alt: Example of searching for tags :screenshot: :width: 60% diff --git a/explore-analyze/find-and-organize/reports.md b/explore-analyze/find-and-organize/reports.md index 4e1b2d90f0..b1df8f0c84 100644 --- a/explore-analyze/find-and-organize/reports.md +++ b/explore-analyze/find-and-organize/reports.md @@ -14,7 +14,7 @@ For example, in **Discover**, you can create and download comma-separated values To view and manage reports, go to **Management** > **Reporting**. -:::{image} ../../images/serverless-reports-management.png +:::{image} /explore-analyze/images/serverless-reports-management.png :alt: {{reports-app}} :screenshot: ::: diff --git a/explore-analyze/find-and-organize/saved-objects.md b/explore-analyze/find-and-organize/saved-objects.md index 6b35811353..475462edd2 100644 --- a/explore-analyze/find-and-organize/saved-objects.md +++ b/explore-analyze/find-and-organize/saved-objects.md @@ -32,7 +32,7 @@ Edit, import, export, and copy your saved objects. These objects include dashboa You can find the **Saved Objects** page using the navigation menu or the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). -:::{image} ../../images/kibana-management-saved-objects.png +:::{image} /explore-analyze/images/kibana-management-saved-objects.png :alt: Saved Objects :screenshot: ::: @@ -49,7 +49,7 @@ Granting access to `Saved Objects Management` authorizes users to manage all sav ## View and delete [saved-objects-view-and-delete] * To view and edit a saved object in its associated application, click the object title. -* To show objects that use this object, so you know the impact of deleting it, click the actions icon ![More actions](../../images/serverless-boxesHorizontal.svg "") and then select **Relationships**. +* To show objects that use this object, so you know the impact of deleting it, click the actions icon ![More actions](/explore-analyze/images/serverless-boxesHorizontal.svg "") and then select **Relationships**. * To delete one or more objects, select their checkboxes, and then click **Delete**. @@ -92,7 +92,7 @@ The [`savedObjects.maxImportExportSize`](kibana://reference/configuration-refere Copy saved objects and their related objects between spaces. -1. Click the actions icon ![Actions](../../images/serverless-boxesHorizontal.svg ""). +1. Click the actions icon ![Actions](/explore-analyze/images/serverless-boxesHorizontal.svg ""). 2. Click **Copy to spaces**. 3. Specify whether to automatically overwrite any objects that already exist in the target spaces, or resolve them on a per-object basis. 4. Select the spaces in which to copy the object. diff --git a/explore-analyze/find-and-organize/tags.md b/explore-analyze/find-and-organize/tags.md index 11482f7a01..958a3fd698 100644 --- a/explore-analyze/find-and-organize/tags.md +++ b/explore-analyze/find-and-organize/tags.md @@ -14,7 +14,7 @@ Use tags to categorize your saved objects, then filter for related objects based To get started, go to the **Tags** management page using the navigation menu or the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). -:::{image} ../../images/kibana-tag-management-section.png +:::{image} /explore-analyze/images/kibana-tag-management-section.png :alt: Tags management :screenshot: ::: @@ -50,10 +50,10 @@ Create a tag to assign to your saved objects. To assign and remove tags, you must have `write` permission on the objects to which you assign the tags. 1. Find the tag you want to assign. -2. Click the actions icon ![Actions icon](../../images/kibana-actions_icon.png ""), and then select **Manage assignments**. +2. Click the actions icon ![Actions icon](/explore-analyze/images/kibana-actions_icon.png ""), and then select **Manage assignments**. 3. Select the objects to which you want to assign or remove tags. - :::{image} ../../images/kibana-manage-assignments-flyout.png + :::{image} /explore-analyze/images/kibana-manage-assignments-flyout.png :alt: Assign flyout :screenshot: :width: 50% @@ -66,7 +66,7 @@ To assign and remove tags, you must have `write` permission on the objects to wh When you delete a tag, you remove it from all saved objects that use it. -1. Click the actions icon ![Actions icon](../../images/kibana-actions_icon.png ""), and then select **Delete**. +1. Click the actions icon ![Actions icon](/explore-analyze/images/kibana-actions_icon.png ""), and then select **Delete**. 2. Click **Delete tag**. ::::{tip} diff --git a/explore-analyze/geospatial-analysis.md b/explore-analyze/geospatial-analysis.md index b9a90ba133..f9b15647e0 100644 --- a/explore-analyze/geospatial-analysis.md +++ b/explore-analyze/geospatial-analysis.md @@ -77,7 +77,7 @@ Visualize geo data with [Kibana](visualize/maps.md). 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@@ This example uses the sample eCommerce orders and sample web logs data sets. For To get the best results from {{ml}} analytics, you must understand your data. You can use the **{{data-viz}}** in the **{{ml-app}}** app for this purpose. Search for specific fields or field types, such as geo-point fields in the sample data sets. You can see how many documents contain those fields within a specific time period and sample size. You can also see the number of distinct values, a list of example values, and preview them on a map. For example: -:::{image} ../../../images/machine-learning-weblogs-data-visualizer-geopoint.jpg +:::{image} /explore-analyze/images/machine-learning-weblogs-data-visualizer-geopoint.jpg :alt: A screenshot of a geo_point field in {{data-viz}} :screenshot: ::: @@ -45,7 +45,7 @@ To create an {{anomaly-job}} that uses the `lat_long` function, in {{kib}} you m For example, create a job that analyzes the sample eCommerce orders data set to find orders with unusual coordinates (`geoip.location` values) relative to the past behavior of each customer (`user` ID): -:::{image} ../../../images/machine-learning-ecommerce-advanced-wizard-geopoint.jpg +:::{image} /explore-analyze/images/machine-learning-ecommerce-advanced-wizard-geopoint.jpg :alt: A screenshot of creating an {{anomaly-job}} using the eCommerce data in {{kib}} :screenshot: ::: @@ -106,7 +106,7 @@ POST _ml/datafeeds/datafeed-ecommerce-geo/_start <4> Alternatively, create a job that analyzes the sample web logs data set to detect events with unusual coordinates (`geo.coordinates` values) or unusually high sums of transferred data (`bytes` values): -:::{image} ../../../images/machine-learning-weblogs-advanced-wizard-geopoint.jpg +:::{image} /explore-analyze/images/machine-learning-weblogs-advanced-wizard-geopoint.jpg :alt: A screenshot of creating an {{anomaly-job}} using the web logs data in {{kib}} :screenshot: ::: @@ -179,7 +179,7 @@ If you used APIs to create the jobs and {{dfeeds}}, you cannot see them in {{kib When you select a period that contains an anomaly in the **Anomaly Explorer** swim lane results, you can see a map of the typical and actual coordinates. For example, in the eCommerce sample data there is a user with anomalous shopping behavior: -:::{image} ../../../images/machine-learning-ecommerce-anomaly-explorer-geopoint.jpg +:::{image} /explore-analyze/images/machine-learning-ecommerce-anomaly-explorer-geopoint.jpg :alt: A screenshot of an anomalous event in the eCommerce data in Anomaly Explorer :screenshot: ::: @@ -188,7 +188,7 @@ A "typical" value indicates a centroid of a cluster of previously observed locat Likewise, there are time periods in the web logs sample data where there are both unusually high sums of data transferred and unusual geographical coordinates: -:::{image} ../../../images/machine-learning-weblogs-anomaly-explorer-geopoint.jpg +:::{image} /explore-analyze/images/machine-learning-weblogs-anomaly-explorer-geopoint.jpg :alt: A screenshot of an anomalous event in the web logs data in Anomaly Explorer :screenshot: ::: @@ -197,7 +197,7 @@ You can use the top influencer values to further filter your results and identif You can also view the anomaly in **Maps** by clicking **View in Maps** in the action menu in the anomaly table. -:::{image} ../../../images/machine-learning-view-in-maps.jpg +:::{image} /explore-analyze/images/machine-learning-view-in-maps.jpg :alt: A screenshot of the anomaly table with the Action menu opened and the "View in Maps" option selected :screenshot: ::: @@ -212,7 +212,7 @@ To integrate the results from your {{anomaly-job}} in **Maps**, click **Add laye For example, you can extend the map example from [Build a map to compare metrics by country or region](../../visualize/maps/maps-getting-started.md) to include a layer that uses your web logs {{anomaly-job}}: -:::{image} ../../../images/machine-learning-weblogs-anomaly-map.jpg +:::{image} /explore-analyze/images/machine-learning-weblogs-anomaly-map.jpg :alt: A screenshot of an anomaly within the Maps app :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/mapping-anomalies.md b/explore-analyze/machine-learning/anomaly-detection/mapping-anomalies.md index 4006a36344..9df2b434c2 100644 --- a/explore-analyze/machine-learning/anomaly-detection/mapping-anomalies.md +++ b/explore-analyze/machine-learning/anomaly-detection/mapping-anomalies.md @@ -20,7 +20,7 @@ This example uses the sample web logs data set. For more information, see [Add t If you have fields that contain valid vector layers, you can use the **{{data-viz}}** in the **{{ml-app}}** app to see a choropleth map, in which each area is colored based on its document count. For example: -:::{image} ../../../images/machine-learning-weblogs-data-visualizer-choropleth.png +:::{image} /explore-analyze/images/machine-learning-weblogs-data-visualizer-choropleth.png :alt: A screenshot of a field that contains vector layer values in {{data-viz}} :screenshot: ::: @@ -31,7 +31,7 @@ To create an {{anomaly-job}} in {{kib}}, click **Create job** on the **{{ml-cap} For example, use the multi-metric job wizard to create a job that analyzes the sample web logs data set to detect anomalous behavior in the sum of the data transferred (`bytes` values) for each destination country (`geo.dest` values): -:::{image} ../../../images/machine-learning-weblogs-multimetric-wizard-vector.png +:::{image} /explore-analyze/images/machine-learning-weblogs-multimetric-wizard-vector.png :alt: A screenshot of creating an {{anomaly-job}} using the web logs data in {{kib}} :screenshot: ::: @@ -98,7 +98,7 @@ After the {{anomaly-jobs}} have processed some data, you can view the results in If you used APIs to create the jobs and {{dfeeds}}, you cannot see them in {{kib}} until you follow the prompts to synchronize the necessary saved objects. :::: -:::{image} ../../../images/machine-learning-weblogs-anomaly-explorer-vectors.png +:::{image} /explore-analyze/images/machine-learning-weblogs-anomaly-explorer-vectors.png :alt: A screenshot of the anomaly count by location in Anomaly Explorer :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-explain.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-explain.md index 67b64a0358..fe9fd9164d 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-explain.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-explain.md @@ -40,7 +40,7 @@ Anomaly scores are in the range of 0 and 100. The values close to 100 signify th The process when the anomaly detection algorithm adjusts the anomaly scores of past records when new data comes in is called *renormalization*. The `renormalization_window_days` configuration parameter specifies the time interval for this adjustment. The **Single Metric Viewer** in Kibana highlights the renormalization change. -:::{image} ../../../images/machine-learning-renormalization-score-reduction.jpg +:::{image} /explore-analyze/images/machine-learning-renormalization-score-reduction.jpg :alt: Example of a record score reduction in {{kib}} :screenshot: ::: @@ -53,7 +53,7 @@ Two more factors may lead to a reduction of the initial score: a high variance i Real-world anomalies often show the impacts of several factors. The **Anomaly explanation** section in the Single Metric Viewer can help you interpret an anomaly in its context. -:::{image} ../../../images/machine-learning-detailed-single-metric.jpg +:::{image} /explore-analyze/images/machine-learning-detailed-single-metric.jpg :alt: Detailed view of the Single Metric Viewer in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md index c7b2094b4b..ed0afcdd09 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md @@ -17,7 +17,7 @@ You can also use it to estimate the probability of a time series value occurring Each forecast has a unique ID, which you can use to distinguish between forecasts that you created at different times. You can create a forecast by using the [forecast {{anomaly-jobs}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-forecast) or by using {{kib}}. For example: -:::{image} ../../../images/machine-learning-overview-forecast.jpg +:::{image} /explore-analyze/images/machine-learning-overview-forecast.jpg :alt: Example screenshot from the Machine Learning Single Metric Viewer in Kibana :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md index cc0ec94dd9..686e5d9f7d 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md @@ -24,7 +24,7 @@ If your data is located outside of {{es}}, you cannot use {{kib}} to create your You can create {{anomaly-jobs}} by using the [create {{anomaly-jobs}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-job). {{kib}} also provides wizards to simplify the process, which vary depending on whether you are using the {{ml-app}} app, {{security-app}} or {{observability}} apps. To open **Anomaly Detection**, find **{{ml-app}}** in the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md). -:::{image} ../../../images/machine-learning-ml-create-job.png +:::{image} /explore-analyze/images/machine-learning-ml-create-job.png :alt: Create New Job :screenshot: ::: @@ -188,7 +188,7 @@ To reduce the number of snapshots consuming space on your cluster, at the end of You can see the list of model snapshots for each job with the [get model snapshots API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-model-snapshots) or in the **Model snapshots** tab on the **Job Management** page in {{kib}}: -:::{image} ../../../images/machine-learning-ml-model-snapshots.png +:::{image} /explore-analyze/images/machine-learning-ml-model-snapshots.png :alt: Example screenshot with a list of model snapshots :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md index ba46fe63c0..a2d402f4f5 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md @@ -23,7 +23,7 @@ When you view your {{ml}} results, each bucket has an anomaly score. This score The {{ml}} analytics enhance the anomaly score for each bucket by considering contiguous buckets. This extra *multi-bucket analysis* effectively uses a sliding window to evaluate the events in each bucket relative to the larger context of recent events. When you review your {{ml}} results, there is a `multi_bucket_impact` property that indicates how strongly the final anomaly score is influenced by multi-bucket analysis. In {{kib}}, anomalies with medium or high multi-bucket impact are depicted in the **Anomaly Explorer** and the **Single Metric Viewer** with a cross symbol instead of a dot. For example: -:::{image} ../../../images/machine-learning-multibucketanalysis.jpg +:::{image} /explore-analyze/images/machine-learning-multibucketanalysis.jpg :alt: Examples of anomalies with multi-bucket impact in {{kib}} :screenshot: ::: @@ -34,7 +34,7 @@ Both the **Anomaly Explorer** and the **Single Metric Viewer** contain an **Anom If you have [{{anomaly-detect-cap}} alert rules](https://www.elastic.co/guide/en/machine-learning/current/creating-anomaly-alert-rules.html) applied to an {{anomaly-job}} and an alert has occured for the rule, you can view how the alert correlates with the {{anomaly-detect}} results in the **Anomaly Explorer** by using the **Anomaly timeline** swimlane and the **Alerts** panel. The **Alerts** panel contains a line chart with the alerts count over time. The cursor on the line chart is in sync with the anomaly swimlane making it easier to review anomalous buckets with the spike produced by the alerts. The panel also contains aggregated information for each alert rule associated with the job selection such as the total number of active, recovered, and untracked alerts for the selected job and time range. An alert context menu is displayed when an anomaly swimlane cell is selected with alerts in the chosen time range. The context menu contains the alert counters for the selected time buckets. -:::{image} ../../../images/machine-learning-anomaly-explorer-alerts.png +:::{image} /explore-analyze/images/machine-learning-anomaly-explorer-alerts.png :alt: Alerts table in the Anomaly Explorer :screenshot: ::: @@ -49,7 +49,7 @@ The influencer results show which entities were anomalous and when. One influenc For example, the `high_sum_total_sales` {{anomaly-job}} for the eCommerce orders sample data uses `customer_full_name.keyword` and `category.keyword` as influencers. You can examine the influencer results with the [get influencers API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-influencers). Alternatively, you can use the **Anomaly Explorer** in {{kib}}: -:::{image} ../../../images/machine-learning-influencers.jpg +:::{image} /explore-analyze/images/machine-learning-influencers.jpg :alt: Influencers in the {{kib}} Anomaly Explorer :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-categories.md b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-categories.md index 9e4c85e2de..7b00286aed 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-categories.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-categories.md @@ -28,7 +28,7 @@ Categorization is a {{ml}} process that tokenizes a text field, clusters similar 3. Select the **Categorization** wizard from the list. 4. Choose a categorization detector - it’s the `count` function in this example - and the field you want to categorize - the `message` field in this example. - :::{image} ../../../images/machine-learning-categorization-wizard.png + :::{image} /explore-analyze/images/machine-learning-categorization-wizard.png :alt: Creating a categorization job in Kibana :screenshot: ::: @@ -69,7 +69,7 @@ PUT _ml/anomaly_detectors/it_ops_app_logs Use the **Anomaly Explorer** in {{kib}} to view the analysis results: -:::{image} ../../../images/machine-learning-ml-category-anomalies.png +:::{image} /explore-analyze/images/machine-learning-ml-category-anomalies.png :alt: Categorization results in the Anomaly Explorer :screenshot: ::: @@ -96,7 +96,7 @@ Categorization uses English dictionary words to identify log message categories. If you use the categorization wizard in {{kib}}, you can see which categorization analyzer it uses and highlighted examples of the tokens that it identifies. You can also change the tokenization rules by customizing the way the categorization field values are interpreted: -:::{image} ../../../images/machine-learning-ml-category-analyzer.png +:::{image} /explore-analyze/images/machine-learning-ml-category-analyzer.png :alt: Editing the categorization analyzer in Kibana :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-populations.md b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-populations.md index 0fd2aecd21..7c4e3ea403 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-populations.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-populations.md @@ -26,7 +26,7 @@ Population analysis is resource-efficient and scales well, enabling the analysis 2. Click **Create job**, select the {{data-source}} you want to analyze. 3. Select the **Population** wizard from the list. 4. Choose a population field - it’s the `clientip` field in this example - and the metric you want to use for the analysis - `Mean(bytes)` in this example. - :::{image} ../../../images/machine-learning-ml-population-wizard.png + :::{image} /explore-analyze/images/machine-learning-ml-population-wizard.png :alt: Creating a population job in Kibana :screenshot: ::: @@ -71,14 +71,14 @@ PUT _ml/anomaly_detectors/population Use the **Anomaly Explorer** in {{kib}} to view the analysis results: -:::{image} ../../../images/machine-learning-ml-population-anomalies.png +:::{image} /explore-analyze/images/machine-learning-ml-population-anomalies.png :alt: Population results in the Anomaly Explorer :screenshot: ::: The results are often quite sparse. There might be just a few data points for the selected time period. Population analysis is particularly useful when you have many entities and the data for specific entitles is sporadic or sparse. If you click on a section in the timeline or swim lanes, you can see more details about the anomalies: -:::{image} ../../../images/machine-learning-ml-population-anomaly.png +:::{image} /explore-analyze/images/machine-learning-ml-population-anomaly.png :alt: Anomaly details for a specific user :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-transform.md b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-transform.md index 03c591d65e..491aff0e04 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-transform.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-transform.md @@ -139,7 +139,7 @@ This example demonstrates how to use runtime fields, but it contains insufficien You can alternatively use {{kib}} to create an advanced {{anomaly-job}} that uses runtime fields. To add the `runtime_mappings` property to your {{dfeed}}, you must use the **Edit JSON** tab. For example: -:::{image} ../../../images/machine-learning-ml-runtimefields.jpg +:::{image} /explore-analyze/images/machine-learning-ml-runtimefields.jpg :alt: Using runtime_mappings in {{dfeed}} config via {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-url.md b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-url.md index a820da60ed..3038039fd6 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-configuring-url.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-configuring-url.md @@ -10,14 +10,14 @@ mapped_pages: You can optionally attach one or more custom URLs to your {{anomaly-jobs}}. These links appear in the anomalies table in the **Anomaly Explorer** and **Single Metric Viewer** and can direct you to dashboards, the **Discover** app, or external websites. For example, you can define a custom URL that provides a way for users to drill down to the source data from the results set: -:::{image} ../../../images/machine-learning-ml-customurl.jpg +:::{image} /explore-analyze/images/machine-learning-ml-customurl.jpg :alt: An example of the custom URL links in the Anomaly Explorer anomalies table :screenshot: ::: When you create or edit an {{anomaly-job}} in {{kib}}, it simplifies the creation of the custom URLs for {{kib}} dashboards and the **Discover** app and it enables you to test your URLs. For example: -:::{image} ../../../images/machine-learning-ml-customurl-edit.gif +:::{image} /explore-analyze/images/machine-learning-ml-customurl-edit.gif :alt: Add a custom URL in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md index 47c11f75cd..3d8ca7bf34 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md @@ -53,13 +53,13 @@ To get the best results from {{ml}} analytics, you must understand your data. Yo You can filter the list by field names or [field types](elasticsearch://reference/elasticsearch/mapping-reference/field-data-types.md). The {{data-viz}} indicates how many of the documents in the sample for the selected time period contain each field. In particular, look at the `clientip`, `response.keyword`, and `url.keyword` fields, since we’ll use them in our {{anomaly-jobs}}. For these fields, the {{data-viz}} provides the number of distinct values, a list of the top values, and the number and percentage of documents that contain the field. For example: - :::{image} ../../../images/machine-learning-ml-gs-data-keyword.jpg + :::{image} /explore-analyze/images/machine-learning-ml-gs-data-keyword.jpg :alt: {{data-viz}} output for ip and keyword fields :screenshot: ::: For numeric fields, the {{data-viz}} provides information about the minimum, median, maximum, and top values, the number of distinct values, and their distribution. You can use the distribution chart to get a better idea of how the values in the data are clustered. For example: - :::{image} ../../../images/machine-learning-ml-gs-data-metric.jpg + :::{image} /explore-analyze/images/machine-learning-ml-gs-data-metric.jpg :alt: {{data-viz}} for sample web logs :screenshot: ::: @@ -115,7 +115,7 @@ After the {{dfeeds}} are started and the {{anomaly-jobs}} have processed some da Depending on the capacity of your machine, you might need to wait a few seconds for the {{ml}} analysis to generate initial results. :::: -:::{image} ../../../images/machine-learning-ml-gs-web-results.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-web-results.jpg :alt: Create jobs for the sample web logs :screenshot: ::: @@ -134,7 +134,7 @@ Let’s start by looking at this simple job in the **Single Metric Viewer**: 2. Click the chart icon in the **Actions** column for your `low_request_rate` job to view its results in the **Single Metric Viewer**. 3. Use the relative mode of the date picker to select a start date one week in the past and an end date one month in the future to cover the majority of the analyzed data points. -:::{image} ../../../images/machine-learning-ml-gs-job1-analysis.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job1-analysis.jpg :alt: Single Metric Viewer for low_request_rate job :screenshot: ::: @@ -158,7 +158,7 @@ You might notice a high spike in the time series. It’s not highlighted as an a For each anomaly, you can see key details such as the time, the actual and expected ("typical") values, and their probability in the **Anomalies** section of the viewer. For example: -:::{image} ../../../images/machine-learning-ml-gs-job1-anomalies.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job1-anomalies.jpg :alt: Single Metric Viewer Anomalies for low_request_rate job :screenshot: ::: @@ -171,7 +171,7 @@ The **Anomaly explanation** section gives you further insights about each anomal You can optionally annotate your job results by drag-selecting a period of time in the **Single Metric Viewer** and adding a description. Annotations are notes that refer to events in a specific time period. They can be created by the user or generated automatically by the {{anomaly-job}} to reflect model changes and noteworthy occurrences. -:::{image} ../../../images/machine-learning-ml-gs-user-annotation.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-user-annotation.jpg :alt: A user annotation in the Single Metric Viewer :screenshot: ::: @@ -198,7 +198,7 @@ Let’s start by looking at the `response_code_rates` job in the **Anomaly Explo For this particular job, you can choose to see separate swim lanes for each client IP or response code. For example: -:::{image} ../../../images/machine-learning-ml-gs-job2-explorer.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job2-explorer.jpg :alt: Anomaly explorer for response_code_rates job :screenshot: ::: @@ -207,7 +207,7 @@ Since the job uses `response.keyword` as its *partition field*, the analysis is Under the anomaly timeline, there is a section that contains annotations. You can filter the type of events by using the selector on the right side of the **Annotations** section. -:::{image} ../../../images/machine-learning-ml-gs-annotations.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-annotations.jpg :alt: Annotations section in the Anomaly Explorer :screenshot: ::: @@ -216,7 +216,7 @@ On the left side of the **Anomaly Explorer**, there is a list of the top influen Click on a section in the swim lanes to obtain more information about the anomalies in that time period. For example, click on the red section in the swim lane for the `response.keyword` value of `404`: -:::{image} ../../../images/machine-learning-ml-gs-job2-explorer-anomaly.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job2-explorer-anomaly.jpg :alt: Anomaly charts for the response_code_rates job :screenshot: ::: @@ -225,7 +225,7 @@ You can see exact times when anomalies occurred. If there are multiple detectors Below the charts, there is a table that provides more information, such as the typical and actual values and the influencers that contributed to the anomaly. For example: -:::{image} ../../../images/machine-learning-ml-gs-job2-explorer-table.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job2-explorer-table.jpg :alt: Anomaly tables for the response_code_rates job :screenshot: ::: @@ -244,7 +244,7 @@ The final sample job (`url_scanning`) is a *population {{anomaly-job}}*. As we s If you examine the results from the `url_scanning` {{anomaly-job}} in the **Anomaly Explorer**, you’ll notice its charts have a different format. For example: -:::{image} ../../../images/machine-learning-ml-gs-job3-explorer.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job3-explorer.jpg :alt: Anomaly charts for the url_scanning job :screenshot: ::: @@ -253,7 +253,7 @@ In this case, the metrics for each client IP are analyzed relative to other clie If you want to play with another example of a population {{anomaly-job}}, add the sample eCommerce orders data set. Its `high_sum_total_sales` job determines which customers have made unusual amounts of purchases relative to other customers in each bucket of time. In this example, there are anomalous events found for two customers: -:::{image} ../../../images/machine-learning-ml-gs-job4-explorer.jpg +:::{image} /explore-analyze/images/machine-learning-ml-gs-job4-explorer.jpg :alt: Anomaly charts for the high_sum_total_sales job :screenshot: ::: @@ -266,19 +266,19 @@ To create a forecast in {{kib}}: 1. View your job results (for example, for the `low_request_rate` job) in the **Single Metric Viewer**. To find that view, click the **View series** button in the **Actions** column on the **Anomaly Detection** page. 2. Click **Forecast**. - :::{image} ../../../images/machine-learning-ml-gs-forecast.png + :::{image} /explore-analyze/images/machine-learning-ml-gs-forecast.png :alt: Create a forecast from the Single Metric Viewer :screenshot: ::: 3. Specify a duration for your forecast. This value indicates how far to extrapolate beyond the last record that was processed. You must use [time units](elasticsearch://reference/elasticsearch/rest-apis/api-conventions.md#time-units). In this example, the duration is one week (`1w`): - :::{image} ../../../images/machine-learning-ml-gs-duration.png + :::{image} /explore-analyze/images/machine-learning-ml-gs-duration.png :alt: Specify a duration of 1w :screenshot: ::: 4. View the forecast in the **Single Metric Viewer**: - :::{image} ../../../images/machine-learning-ml-gs-forecast-results.png + :::{image} /explore-analyze/images/machine-learning-ml-gs-forecast-results.png :alt: View a forecast from the Single Metric Viewer :screenshot: ::: @@ -286,7 +286,7 @@ To create a forecast in {{kib}}: The yellow line in the chart represents the predicted data values. The shaded yellow area represents the bounds for the predicted values, which also gives an indication of the confidence of the predictions. Note that the bounds generally increase with time (that is to say, the confidence levels decrease), since you are forecasting further into the future. Eventually if the confidence levels are too low, the forecast stops. 5. Optional: Compare the forecast to actual data. - :::{image} ../../../images/machine-learning-ml-gs-forecast-actual.png + :::{image} /explore-analyze/images/machine-learning-ml-gs-forecast-actual.png :alt: View a forecast over actual data in the Single Metric Viewer :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-jobs-from-lens.md b/explore-analyze/machine-learning/anomaly-detection/ml-jobs-from-lens.md index 41d9ed8e0b..eb61d36228 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-jobs-from-lens.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-jobs-from-lens.md @@ -30,14 +30,14 @@ You need to have a compatible visualization on **Dashboard** to create an {{anom 3. Select **Create {{anomaly-job}}**. The option is only displayed if the visualization can be converted to an {{anomaly-job}} configuration. 4. (Optional) Select the layer from which the {{anomaly-job}} is created. -:::{image} ../../../images/machine-learning-create-ad-job-from-lens.jpg +:::{image} /explore-analyze/images/machine-learning-create-ad-job-from-lens.jpg :alt: A screenshot of a chart with the Options menu opened :screenshot: ::: If the visualization has multiple compatible layers, you can select which layer to use for creating the {{anomaly-job}}. -:::{image} ../../../images/machine-learning-select-layer-for-job.jpg +:::{image} /explore-analyze/images/machine-learning-select-layer-for-job.jpg :alt: A screenshot of a chart with the Options menu opened :screenshot: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-reverting-model-snapshot.md b/explore-analyze/machine-learning/anomaly-detection/ml-reverting-model-snapshot.md index 689db519d7..a7b5c4ced7 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-reverting-model-snapshot.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-reverting-model-snapshot.md @@ -13,7 +13,7 @@ mapped_pages: 1. In {{kib}}, navigate to **Jobs**. To open **Jobs**, find **{{ml-app}} > Anomaly Detection** in the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md). 2. Locate the {{anomaly-job}} whose model you want to revert in the job table. 3. Open the job details and navigate to the **Model Snapshots** tab. - :::{image} ../../../images/machine-learning-anomaly-job-model-snapshots.jpg + :::{image} /explore-analyze/images/machine-learning-anomaly-job-model-snapshots.jpg :alt: A screenshot of a job with the Model Snapshots tab opened :screenshot: ::: @@ -22,7 +22,7 @@ mapped_pages: 5. Optional: Select if you want to replay the analysis based on the data in your index after the revert has been applied. If you don’t select this option, there will be no {{anomaly-detect}} results after the snapshot was taken and the job results end at the point in time of the snapshot. If you select this option, you can then optionally select one or both of the next two options: * You can select whether you want the job to continue running in real time after the replay, or to just replay existing data after the snapshot. * You can select a time range you want to avoid during the replay by declaring a calendar event. This way, you can skip any problematic time frame that you want the {{anomaly-job}} to avoid. - :::{image} ../../../images/machine-learning-revert-model-snapshot.jpg + :::{image} /explore-analyze/images/machine-learning-revert-model-snapshot.jpg :alt: A screenshot of a revert model snapshot flyout :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md index 68541beb89..ece68f955d 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md @@ -10,7 +10,7 @@ mapped_pages: {{classification-cap}} is a {{ml}} process that predicts the class or category of a data point in a data set. For a simple example, consider how the shapes in the following graph can be differentiated and classified as "circles" and "triangles": -:::{image} ../../../images/machine-learning-classification-vis.png +:::{image} /explore-analyze/images/machine-learning-classification-vis.png :alt: Classification process ::: @@ -94,7 +94,7 @@ The multiclass confusion matrix provides a summary of the performance of the {{c This is an example of a confusion matrix for a binary problem: -:::{image} ../../../images/machine-learning-confusion-matrix-binary.jpg +:::{image} /explore-analyze/images/machine-learning-confusion-matrix-binary.jpg :alt: Confusion matrix of a binary problem ::: @@ -102,7 +102,7 @@ It is a two by two matrix because there are only two classes (`true` and `false` As the number of classes increases, the confusion matrix becomes more complex: -:::{image} ../../../images/machine-learning-confusion-matrix-multiclass.jpg +:::{image} /explore-analyze/images/machine-learning-confusion-matrix-multiclass.jpg :alt: Confusion matrix of a multiclass problem ::: @@ -134,7 +134,7 @@ The `class_score` is a function of the `class_probability` and has a value that If your objective is to maximize accuracy, the scores are weighted to maximize the proportion of correct predictions in the training data set. -:::{image} ../../../images/machine-learning-confusion-matrix-binary-accuracy.jpg +:::{image} /explore-analyze/images/machine-learning-confusion-matrix-binary-accuracy.jpg :alt: A confusion matrix with the correct predictions highlighted :screenshot: ::: @@ -145,7 +145,7 @@ If there is an imbalanced class distribution in your training data, focusing on By default, {{classanalysis}} jobs accept a slight degradation of the overall accuracy in return for greater sensitivity to classes that are predicted incorrectly. That is to say, their objective is to maximize the minimum recall. For example, in the context of a multi-class confusion matrix, the predictions of interest are in each row: -:::{image} ../../../images/machine-learning-confusion-matrix-multiclass-recall.jpg +:::{image} /explore-analyze/images/machine-learning-confusion-matrix-multiclass-recall.jpg :alt: A confusion matrix with a row highlighted :screenshot: ::: @@ -160,19 +160,19 @@ The model that you created is stored as {{es}} documents in internal indices. In 1. To deploy {{dfanalytics}} model in a pipeline, navigate to **Machine Learning** > **Model Management** > **Trained models** in the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md) in {{kib}}. 2. Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. - :::{image} ../../../images/machine-learning-ml-dfa-trained-models-ui.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-trained-models-ui.png :alt: The trained models UI in {{kib}} :screenshot: ::: 3. Create an {{infer}} pipeline to be able to use the model against new data through the pipeline. Add a name and a description or use the default values. - :::{image} ../../../images/machine-learning-ml-dfa-inference-pipeline.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-pipeline.png :alt: Creating an inference pipeline :screenshot: ::: 4. Configure the pipeline processors or use the default settings. - :::{image} ../../../images/machine-learning-ml-dfa-inference-processor.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-processor.png :alt: Configuring an inference processor :screenshot: ::: @@ -280,7 +280,7 @@ To predict whether a specific flight is delayed: 1. Create a {{dfanalytics-job}}. You can use the wizard on the **{{ml-app}}** > **Data Frame Analytics** tab in {{kib}} or the [create {{dfanalytics-jobs}}](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-data-frame-analytics) API. - :::{image} ../../../images/machine-learning-flights-classification-job-1.jpg + :::{image} /explore-analyze/images/machine-learning-flights-classification-job-1.jpg :alt: Creating a {{dfanalytics-job}} in {{kib}} :screenshot: ::: @@ -290,7 +290,7 @@ To predict whether a specific flight is delayed: 3. Choose `FlightDelay` as the dependent variable, which is the field that we want to predict with the {{classanalysis}}. 4. Add `Cancelled`, `FlightDelayMin`, and `FlightDelayType` to the list of excluded fields. It is recommended to exclude fields that either contain erroneous data or describe the `dependent_variable`. The wizard includes a scatterplot matrix, which enables you to explore the relationships between the numeric fields. The color of each point is affected by the value of the {{depvar}} for that document, as shown in the legend. You can highlight an area in one of the charts and the corresponding area is also highlighted in the rest of the charts. You can use this matrix to help you decide which fields to include or exclude. - :::{image} ../../../images/machine-learning-flights-classification-scatterplot.png + :::{image} /explore-analyze/images/machine-learning-flights-classification-scatterplot.png :alt: A scatterplot matrix for three fields in {{kib}} :screenshot: ::: @@ -355,7 +355,7 @@ POST _ml/data_frame/analytics/model-flight-delays-classification/_start 3. Check the job stats to follow the progress in {{kib}} or use the [get {{dfanalytics-jobs}} statistics API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-data-frame-analytics-stats). -:::{image} ../../../images/machine-learning-flights-classification-details.jpg +:::{image} /explore-analyze/images/machine-learning-flights-classification-details.jpg :alt: Statistics for a {{dfanalytics-job}} in {{kib}} :screenshot: ::: @@ -464,7 +464,7 @@ Now you have a new index that contains a copy of your source data with predictio When you view the {{classification}} results in {{kib}}, it shows the contents of the destination index in a tabular format. It also provides information about the analysis details, model evaluation metrics, total {{feat-imp}} values, and a scatterplot matrix. -:::{image} ../../../images/machine-learning-flights-classification-results.jpg +:::{image} /explore-analyze/images/machine-learning-flights-classification-results.jpg :alt: Destination index table for a classification job in {{kib}} :screenshot: ::: @@ -516,14 +516,14 @@ The class with the highest score is the prediction. In this example, `false` has If you chose to calculate {{feat-imp}}, the destination index also contains `ml.feature_importance` objects. Every field that is included in the analysis (known as a *feature* of the data point) is assigned a {{feat-imp}} value. It has both a magnitude and a direction (positive or negative), which indicates how each field affects a particular prediction. Only the most significant values (in this case, the top 10) are stored in the index. However, the trained model metadata also contains the average magnitude of the {{feat-imp}} values for each field across all the training data. You can view this summarized information in {{kib}}: -:::{image} ../../../images/machine-learning-flights-classification-total-importance.jpg +:::{image} /explore-analyze/images/machine-learning-flights-classification-total-importance.jpg :alt: Total {{feat-imp}} values in {{kib}} :screenshot: ::: You can also see the {{feat-imp}} values for each individual prediction in the form of a decision plot: -:::{image} ../../../images/machine-learning-flights-classification-importance.png +:::{image} /explore-analyze/images/machine-learning-flights-classification-importance.png :alt: A decision plot for {{feat-imp}} values in {{kib}} :screenshot: ::: @@ -671,7 +671,7 @@ Though you can look at individual results and compare the predicted value (`ml.F {{kib}} provides a *normalized confusion matrix* that contains the percentage of occurrences where the analysis classified data points correctly with their actual class and the percentage of occurrences where it misclassified them. -:::{image} ../../../images/machine-learning-flights-classification-evaluation.png +:::{image} /explore-analyze/images/machine-learning-flights-classification-evaluation.png :alt: Evaluation of a classification job in {{kib}} :screenshot: ::: @@ -686,7 +686,7 @@ Likewise if you select other quadrants in the matrix, it shows the number of doc {{kib}} also provides the *receiver operating characteristic (ROC) curve* as part of the model evaluation. The plot compares the true positive rate (y-axis) to the false positive rate (x-axis) for each class; in this example, `true` and `false`. From this plot, the area under the curve (AUC) value is computed. It is a number between 0 and 1. The higher the AUC, the better the model is at predicting the classes correctly. -:::{image} ../../../images/machine-learning-flights-classification-roc-curve.jpg +:::{image} /explore-analyze/images/machine-learning-flights-classification-roc-curve.jpg :alt: Evaluation of a classification job in {{kib}} – ROC curve :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-custom-urls.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-custom-urls.md index 985df8449b..6cfdb5132d 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-custom-urls.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-custom-urls.md @@ -10,14 +10,14 @@ mapped_pages: You can optionally attach one or more custom URLs to your {{dfanalytics-jobs}}. These links can direct you to dashboards, the **Discover** app, or external websites. For example, you can define a custom URL that provides a way for users to drill down to the source data from a {{regression}} job. You can create a custom URL during job creation under **Additional settings** in the **Job details** step. Alternatively, you can edit or add new custom URLs in the job list by clicking **Edit** in the **Actions** menu. -:::{image} ../../../images/machine-learning-ml-dfa-custom-url.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-custom-url.png :alt: Creating a custom URL during job creation :screenshot: ::: When you create or edit an {{dfanalytics-job}} in {{kib}}, it simplifies the creation of the custom URLs for {{kib}} dashboards and the **Discover** app and it enables you to test your URLs. For example: -:::{image} ../../../images/machine-learning-ml-dfa-custom-url-edit.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-custom-url-edit.png :alt: Add a custom URL in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md index 2b7ed25e7a..06f36063ee 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md @@ -120,7 +120,7 @@ The goal of {{oldetection}} is to find the most unusual documents in an index. L In this example, we’ll use the web logs sample data and pivot the data such that we get a new index that contains a network usage summary for each client IP. In particular, create a {{transform}} that calculates the number of occasions when a specific client IP communicated with the network (`@timestamp.value_count`), the sum of the bytes that are exchanged between the network and the client’s machine (`bytes.sum`), the maximum exchanged bytes during a single occasion (`bytes.max`), and the total number of requests (`request.value_count`) initiated by a specific client IP. You can preview the {{transform}} before you create it in **{{stack-manage-app}}** > **Transforms**: - :::{image} ../../../images/machine-learning-logs-transform-preview.jpg + :::{image} /explore-analyze/images/machine-learning-logs-transform-preview.jpg :alt: Creating a {{transform}} in {{kib}} :screenshot: ::: @@ -237,13 +237,13 @@ POST _transform/logs-by-clientip/_start 4. Create a {{dfanalytics-job}} to detect outliers in the new entity-centric index. In the wizard on the **Machine Learning** > **Data Frame Analytics** page in {{kib}}, select your new {{data-source}} then use the default values for {{oldetection}}. For example: - :::{image} ../../../images/machine-learning-weblog-outlier-job-1.jpg + :::{image} /explore-analyze/images/machine-learning-weblog-outlier-job-1.jpg :alt: Create a {{dfanalytics-job}} in {{kib}} :screenshot: ::: The wizard includes a scatterplot matrix, which enables you to explore the relationships between the fields. You can use that information to help you decide which fields to include or exclude from the analysis. - :::{image} ../../../images/machine-learning-weblog-outlier-scatterplot.jpg + :::{image} /explore-analyze/images/machine-learning-weblog-outlier-scatterplot.jpg :alt: A scatterplot matrix for three fields in {{kib}} :screenshot: ::: @@ -291,7 +291,7 @@ PUT _ml/data_frame/analytics/weblog-outliers 6. View the results of the {{oldetection}} analysis. The {{dfanalytics}} job creates an index that contains the original data and {{olscores}} for each document. The {{olscore}} indicates how different each entity is from other entities. In {{kib}}, you can view the results from the {{dfanalytics}} job and sort them on the outlier score: - :::{image} ../../../images/machine-learning-outliers.jpg + :::{image} /explore-analyze/images/machine-learning-outliers.jpg :alt: View {{oldetection}} results in {{kib}} :screenshot: ::: @@ -340,7 +340,7 @@ GET weblog-outliers/_search?q="111.237.144.54" {{kib}} also provides a scatterplot matrix in the results. Outliers with a score that exceeds the threshold are highlighted in each chart. The outlier score threshold can be set by using the slider under the matrix: -:::{image} ../../../images/machine-learning-outliers-scatterplot.jpg +:::{image} /explore-analyze/images/machine-learning-outliers-scatterplot.jpg :alt: View scatterplot in {{oldetection}} results :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-overview.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-overview.md index 6c185a1726..072c5243a0 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-overview.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-overview.md @@ -29,7 +29,7 @@ Elastic supervised learning enables you to train a {{ml}} model based on trainin The workflow for supervised learning consists of the following stages: -:::{image} ../../../images/machine-learning-ml-dfa-lifecycle-diagram.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-lifecycle-diagram.png :alt: Supervised learning workflow ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md index b1ee2f8c45..d90a5e880d 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md @@ -106,19 +106,19 @@ The model that you created is stored as {{es}} documents in internal indices. In 1. To deploy {{dfanalytics}} model in a pipeline, navigate to **Machine Learning** > **Model Management** > **Trained models** in the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md) in {{kib}}. 2. Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. - :::{image} ../../../images/machine-learning-ml-dfa-trained-models-ui.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-trained-models-ui.png :alt: The trained models UI in {{kib}} :screenshot: ::: 3. Create an {{infer}} pipeline to be able to use the model against new data through the pipeline. Add a name and a description or use the default values. - :::{image} ../../../images/machine-learning-ml-dfa-inference-pipeline.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-pipeline.png :alt: Creating an inference pipeline :screenshot: ::: 4. Configure the pipeline processors or use the default settings. - :::{image} ../../../images/machine-learning-ml-dfa-inference-processor.png + :::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-processor.png :alt: Configuring an inference processor :screenshot: ::: @@ -223,7 +223,7 @@ To predict the number of minutes delayed for each flight: 1. Verify that your environment is set up properly to use {{ml-features}}. The {{stack}} {{security-features}} require a user that has authority to create and manage {{dfanalytics-jobs}}. See [Setup and security](../setting-up-machine-learning.md). 2. Create a {{dfanalytics-job}}. You can use the wizard on the **{{ml-app}}** > **Data Frame Analytics** tab in {{kib}} or the [create {{dfanalytics-jobs}}](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-data-frame-analytics) API. - :::{image} ../../../images/machine-learning-flights-regression-job-1.jpg + :::{image} /explore-analyze/images/machine-learning-flights-regression-job-1.jpg :alt: Creating a {{dfanalytics-job}} in {{kib}} :screenshot: ::: @@ -233,7 +233,7 @@ To predict the number of minutes delayed for each flight: 4. Choose `FlightDelayMin` as the {{depvar}}, which is the field that we want to predict. 5. Add `Cancelled`, `FlightDelay`, and `FlightDelayType` to the list of excluded fields. These fields will be excluded from the analysis. It is recommended to exclude fields that either contain erroneous data or describe the `dependent_variable`. The wizard includes a scatterplot matrix, which enables you to explore the relationships between the numeric fields. The color of each point is affected by the value of the {{depvar}} for that document, as shown in the legend. You can highlight an area in one of the charts and the corresponding area is also highlighted in the rest of the chart. You can use this matrix to help you decide which fields to include or exclude from the analysis. - :::{image} ../../../images/machine-learning-flightdata-regression-scatterplot.png + :::{image} /explore-analyze/images/machine-learning-flightdata-regression-scatterplot.png :alt: A scatterplot matrix for three fields in {{kib}} :screenshot: ::: @@ -303,7 +303,7 @@ POST _ml/data_frame/analytics/model-flight-delays-regression/_start 4. Check the job stats to follow the progress in {{kib}} or use the [get {{dfanalytics-jobs}} statistics API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-data-frame-analytics-stats). -:::{image} ../../../images/machine-learning-flights-regression-details.jpg +:::{image} /explore-analyze/images/machine-learning-flights-regression-details.jpg :alt: Statistics for a {{dfanalytics-job}} in {{kib}} :screenshot: ::: @@ -411,7 +411,7 @@ Now you have a new index that contains a copy of your source data with predictio When you view the results in {{kib}}, it shows the contents of the destination index in a tabular format. It also provides information about the analysis details, model evaluation metrics, total feature importance values, and a scatterplot matrix. Let’s start by looking at the results table: -:::{image} ../../../images/machine-learning-flights-regression-results.jpg +:::{image} /explore-analyze/images/machine-learning-flights-regression-results.jpg :alt: Results for a {{dfanalytics-job}} in {{kib}} :screenshot: ::: @@ -420,14 +420,14 @@ In this example, the table shows a column for the {{depvar}} (`FlightDelayMin`), If you chose to calculate {{feat-imp}}, the destination index also contains `ml.feature_importance` objects. Every field that is included in the {{reganalysis}} (known as a *feature* of the data point) is assigned a {{feat-imp}} value. This value has both a magnitude and a direction (positive or negative), which indicates how each field affects a particular prediction. Only the most significant values (in this case, the top 5) are stored in the index. However, the trained model metadata also contains the average magnitude of the {{feat-imp}} values for each field across all the training data. You can view this summarized information in {{kib}}: -:::{image} ../../../images/machine-learning-flights-regression-total-importance.jpg +:::{image} /explore-analyze/images/machine-learning-flights-regression-total-importance.jpg :alt: Total {{feat-imp}} values in {{kib}} :screenshot: ::: You can also see the {{feat-imp}} values for each individual prediction in the form of a decision plot: -:::{image} ../../../images/machine-learning-flights-regression-importance.png +:::{image} /explore-analyze/images/machine-learning-flights-regression-importance.png :alt: A decision plot for {{feat-imp}} values in {{kib}} :screenshot: ::: @@ -533,7 +533,7 @@ Though you can look at individual results and compare the predicted value (`ml.F {{kib}} provides *training error* metrics, which represent how well the model performed on the training data set. It also provides *generalization error* metrics, which represent how well the model performed on testing data. -:::{image} ../../../images/machine-learning-flights-regression-evaluation.jpg +:::{image} /explore-analyze/images/machine-learning-flights-regression-evaluation.jpg :alt: Evaluating {{reganalysis}} results in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md b/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md index d2cc3e4b11..51c7c35059 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md @@ -14,28 +14,28 @@ The purpose of {{feat-imp}} is to help you determine whether the predictions are You can see the average magnitude of the {{feat-imp}} values for each field across all the training data in {{kib}} or by using the [get trained model API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-trained-models). For example, {{kib}} shows the total feature importance for each field in {{regression}} or binary {{classanalysis}} results as follows: -:::{image} ../../../images/machine-learning-flights-regression-total-importance.jpg +:::{image} /explore-analyze/images/machine-learning-flights-regression-total-importance.jpg :alt: Total {{feat-imp}} values for a {{regression}} {{dfanalytics-job}} in {{kib}} :screenshot: ::: If the {{classanalysis}} involves more than two classes, {{kib}} uses colors to show how the impact of each field varies by class. For example: -:::{image} ../../../images/machine-learning-diamonds-classification-total-importance.png +:::{image} /explore-analyze/images/machine-learning-diamonds-classification-total-importance.png :alt: Total {{feat-imp}} values for a {{classification}} {{dfanalytics-job}} in {{kib}} :screenshot: ::: You can also examine the feature importance values for each individual prediction. In {{kib}}, you can see these values in JSON objects or decision plots. For {{reganalysis}}, each decision plot starts at a shared baseline, which is the average of the prediction values for all the data points in the training data set. When you add all of the feature importance values for a particular data point to that baseline, you arrive at the numeric prediction value. If a {{feat-imp}} value is negative, it reduces the prediction value. If a {{feat-imp}} value is positive, it increases the prediction value. For example: -:::{image} ../../../images/machine-learning-flights-regression-decision-plot.png +:::{image} /explore-analyze/images/machine-learning-flights-regression-decision-plot.png :alt: Feature importance values for a {{regression}} {{dfanalytics-job}} in {{kib}} :screenshot: ::: For {{classanalysis}}, the sum of the {{feat-imp}} values approximates the predicted logarithm of odds for each data point. The simplest way to understand {{feat-imp}} in the context of {{classanalysis}} is to look at the decision plots in {{kib}}. For each data point, there is a chart which shows the relative impact of each feature on the prediction probability for that class. This information helps you to understand which features reduces or increase the prediction probability. For example: -:::{image} ../../../images/machine-learning-flights-classification-decision-plot.png +:::{image} /explore-analyze/images/machine-learning-flights-classification-decision-plot.png :alt: A decision plot in {{kib}}for a {{classification}} {{dfanalytics-job}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-trained-models.md b/explore-analyze/machine-learning/data-frame-analytics/ml-trained-models.md index 94ec566eb7..a1326740f3 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-trained-models.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-trained-models.md @@ -22,21 +22,21 @@ Alternatively, you can use APIs like [get trained models](https://www.elastic.co 2. Find the model you want to deploy in the list and click **Deploy model** in the **Actions** menu. -:::{image} ../../../images/machine-learning-ml-dfa-trained-models-ui.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-trained-models-ui.png :alt: The trained models UI in {{kib}} :screenshot: ::: 3. Create an {{infer}} pipeline to be able to use the model against new data through the pipeline. Add a name and a description or use the default values. -:::{image} ../../../images/machine-learning-ml-dfa-inference-pipeline.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-pipeline.png :alt: Creating an inference pipeline :screenshot: ::: 4. Configure the pipeline processors or use the default settings. -:::{image} ../../../images/machine-learning-ml-dfa-inference-processor.png +:::{image} /explore-analyze/images/machine-learning-ml-dfa-inference-processor.png :alt: Configuring an inference processor :screenshot: ::: diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana.md b/explore-analyze/machine-learning/machine-learning-in-kibana.md index b0356bc185..a276a6f78c 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana.md @@ -13,7 +13,7 @@ As data sets increase in size and complexity, the human effort required to inspe {{kib}} includes a free **{{data-viz}}** to learn more about your data. In particular, if your data is stored in {{es}} and contains a time field, you can use the **{{data-viz}}** to identify possible fields for {{anomaly-detect}}: -:::{image} ../../images/kibana-ml-data-visualizer-sample.png +:::{image} /explore-analyze/images/kibana-ml-data-visualizer-sample.png :alt: {{data-viz}} for sample flight data :screenshot: ::: @@ -51,7 +51,7 @@ This functionality is in technical preview and may be changed or removed in a fu You can find the data drift view in **{{ml-app}}** > **{{data-viz}}** in {{kib}} or by using the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). The data drift view shows you the differences in each field for two different time ranges in a given {{data-source}}. The view helps you to visualize the changes in your data over time and enables you to understand its behavior better. -:::{image} ../../images/kibana-ml-data-drift.png +:::{image} /explore-analyze/images/kibana-ml-data-drift.png :alt: Data drift view in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md b/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md index 81c1d1c1c9..51a07621fe 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md @@ -45,7 +45,7 @@ A common use case is a user searching FAQs, or a support agent searching a knowl The diagram below shows how documents are processed during ingestion. -:::{image} ../../../images/elasticsearch-reference-document-enrichment-diagram.png +:::{image} /explore-analyze/images/elasticsearch-reference-document-enrichment-diagram.png :alt: ML inference pipeline diagram ::: @@ -74,7 +74,7 @@ If you only see the `search-default-ingestion` pipeline, you will need to click Once your index-specific ML {{infer}} pipeline is ready, you can add {{infer}} processors that use your ML trained models. To add an {{infer}} processor to the ML {{infer}} pipeline, click the **Add Inference Pipeline** button in the **Machine Learning Inference Pipelines** card. -:::{image} ../../../images/elasticsearch-reference-document-enrichment-add-inference-pipeline.png +:::{image} /explore-analyze/images/elasticsearch-reference-document-enrichment-add-inference-pipeline.png :alt: Add Inference Pipeline :screenshot: ::: diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-aiops.md b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-aiops.md index ea3681614b..4b6d647e34 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-aiops.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-aiops.md @@ -19,7 +19,7 @@ Log rate analysis uses advanced statistical methods to identify reasons for incr You can find log rate analysis embedded in multiple applications. In {{kib}}, you can find it under **{{ml-app}}** > **AIOps Labs** or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). Here, you can select the {{data-source}} or saved Discover session that you want to analyze. -:::{image} ../../../images/kibana-ml-log-rate-analysis-before.png +:::{image} /explore-analyze/images/kibana-ml-log-rate-analysis-before.png :alt: Log event histogram chart :screenshot: ::: @@ -28,7 +28,7 @@ Select a spike or drop in the log event histogram chart to start the analysis. I Brushes in the chart show the baseline time range and the deviation in the analyzed data. You can move the brushes to redefine both the baseline and the deviation and rerun the analysis with the modified values. -:::{image} ../../../images/kibana-ml-log-rate-analysis.png +:::{image} /explore-analyze/images/kibana-ml-log-rate-analysis.png :alt: Log rate spike explained :screenshot: ::: @@ -39,7 +39,7 @@ Log pattern analysis helps you to find patterns in unstructured log messages and You can find log pattern analysis under **{{ml-app}}** > **AIOps Labs** or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). Here, you can select the {{data-source}} or saved Discover session that you want to analyze, or in **Discover** as an available action for any text field. -:::{image} ../../../images/kibana-ml-log-pattern-analysis.png +:::{image} /explore-analyze/images/kibana-ml-log-pattern-analysis.png :alt: Log pattern analysis UI :screenshot: ::: @@ -56,7 +56,7 @@ Change point detection uses the [change point aggregation](elasticsearch://refer You can find change point detection under **{{ml-app}}** > **AIOps Labs** or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). Here, you can select the {{data-source}} or saved Discover session that you want to analyze. -:::{image} ../../../images/kibana-ml-change-point-detection.png +:::{image} /explore-analyze/images/kibana-ml-change-point-detection.png :alt: Change point detection UI :screenshot: ::: @@ -65,7 +65,7 @@ Select a function and a metric field, then pick a date range to start detecting When a change point is detected, a row displays basic information including the timestamp of the change point, a preview chart, the type of change point, its p-value, the name and value of the split field. You can further examine the selected change point in a detailed view. A chart visualizes the identified change point within the analyzed time window, making the interpretation easier. If the analysis is split by a field, a separate chart is shown for every partition that has a detected change point. The chart displays the type of change point, its value, and the timestamp of the bucket where the change point has been detected. The corresponding `p-value` indicates the magnitude of the change; lower values indicate more significant changes. You can use the change point type selector to filter the results by specific types of change points. -:::{image} ../../../images/kibana-ml-change-point-detection-selected.png +:::{image} /explore-analyze/images/kibana-ml-change-point-detection-selected.png :alt: Selected change points :screenshot: ::: diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-anomalies.md b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-anomalies.md index 240aeed51d..ee31df52f6 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-anomalies.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-anomalies.md @@ -13,28 +13,28 @@ The Elastic {{ml}} {{anomaly-detect}} feature automatically models the normal be If you have a license that includes the {{ml-features}}, you can create {{anomaly-jobs}} and manage jobs and {{dfeeds}} from the **Job Management** pane: -:::{image} ../../../images/kibana-ml-job-management.png +:::{image} /explore-analyze/images/kibana-ml-job-management.png :alt: Job Management :screenshot: ::: You can use the **Settings** pane to create and edit calendars and the filters that are used in custom rules: -:::{image} ../../../images/kibana-ml-settings.png +:::{image} /explore-analyze/images/kibana-ml-settings.png :alt: Calendar Management :screenshot: ::: The **Anomaly Explorer** and **Single Metric Viewer** display the results of your {{anomaly-jobs}}. For example: -:::{image} ../../../images/kibana-ml-single-metric-viewer.png +:::{image} /explore-analyze/images/kibana-ml-single-metric-viewer.png :alt: Single Metric Viewer :screenshot: ::: You can optionally add annotations by drag-selecting a period of time in the **Single Metric Viewer** and adding a description. For example, you can add an explanation for anomalies in that time period or provide notes about what is occurring in your operational environment at that time: -:::{image} ../../../images/kibana-ml-annotations-list.png +:::{image} /explore-analyze/images/kibana-ml-annotations-list.png :alt: Single Metric Viewer with annotations :screenshot: ::: diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-dfanalytics.md b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-dfanalytics.md index 99c71abda4..c510253901 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-dfanalytics.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana/xpack-ml-dfanalytics.md @@ -12,7 +12,7 @@ The Elastic {{ml}} {{dfanalytics}} feature enables you to analyze your data usin If you have a license that includes the {{ml-features}}, you can create {{dfanalytics}} jobs and view their results on the **Data Frame Analytics** page in {{kib}}. For example: -:::{image} ../../../images/kibana-classification.png +:::{image} /explore-analyze/images/kibana-classification.png :alt: {{classification-cap}} results in {{kib}} :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md b/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md index da256d8f47..16a202dd9a 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md @@ -14,7 +14,7 @@ You can deploy a model multiple times by assigning a unique deployment ID when s You can optimize your deplyoment for typical use cases, such as search and ingest. When you optimize for ingest, the throughput will be higher, which increases the number of {{infer}} requests that can be performed in parallel. When you optimize for search, the latency will be lower during search processes. When you have dedicated deployments for different purposes, you ensure that the search speed remains unaffected by ingest workloads, and vice versa. Having separate deployments for search and ingest mitigates performance issues resulting from interactions between the two, which can be hard to diagnose. -:::{image} ../../../images/machine-learning-ml-nlp-deployment-id-elser-v2.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-deployment-id-elser-v2.png :alt: Model deployment on the Trained Models UI. :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-e5.md b/explore-analyze/machine-learning/nlp/ml-nlp-e5.md index 87e9ba3b7f..88cdd4c7f3 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-e5.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-e5.md @@ -63,7 +63,7 @@ For most cases, the preferred version is the **Intel and Linux optimized** model 1. In {{kib}}, navigate to **{{ml-app}}** > **Trained Models** from the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md). E5 can be found in the list of trained models. There are two versions available: one portable version which runs on any hardware and one version which is optimized for Intel® silicon. You can see which model is recommended to use based on your hardware configuration. 2. Click the **Add trained model** button. Select the E5 model version you want to use in the opening modal window. The model that is recommended for you based on your hardware configuration is highlighted. Click **Download**. You can check the download status on the **Notifications** page. - :::{image} ../../../images/machine-learning-ml-nlp-e5-download.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-e5-download.png :alt: Downloading E5 :screenshot: ::: @@ -73,7 +73,7 @@ For most cases, the preferred version is the **Intel and Linux optimized** model 3. After the download is finished, start the deployment by clicking the **Start deployment** button. 4. Provide a deployment ID, select the priority, and set the number of allocations and threads per allocation values. - :::{image} ../../../images/machine-learning-ml-nlp-deployment-id-e5.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-deployment-id-e5.png :alt: Deploying E5 :screenshot: ::: @@ -93,14 +93,14 @@ Alternatively, you can download and deploy the E5 model to an {{infer}} pipeline 3. Navigate to the **Pipelines** tab. 4. Under **{{ml-app}} {{infer-cap}} Pipelines**, click the **Deploy** button in the **Improve your results with E5** section to begin downloading the E5 model. This may take a few minutes depending on your network. - :::{image} ../../../images/machine-learning-ml-nlp-deploy-e5-es.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-deploy-e5-es.png :alt: Deploying E5 in Elasticsearch :screenshot: ::: 5. Once the model is downloaded, click the **Start single-threaded** button to start the model with basic configuration or select the **Fine-tune performance** option to navigate to the **Trained Models** page where you can configure the model deployment. - :::{image} ../../../images/machine-learning-ml-nlp-start-e5-es.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-start-e5-es.png :alt: Start E5 in Elasticsearch :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-elser.md b/explore-analyze/machine-learning/nlp/ml-nlp-elser.md index fccca3c7cd..f9a4c1eab9 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-elser.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-elser.md @@ -95,7 +95,7 @@ You can also download and deploy ELSER either from **{{ml-app}}** > **Trained Mo 1. In {{kib}}, navigate to **{{ml-app}}** > **Trained Models**. ELSER can be found in the list of trained models. There are two versions available: one portable version which runs on any hardware and one version which is optimized for Intel® silicon. You can see which model is recommended to use based on your hardware configuration. 2. Click the **Add trained model** button. Select the ELSER model version you want to use in the opening modal window. The model that is recommended for you based on your hardware configuration is highlighted. Click **Download**. You can check the download status on the **Notifications** page. - :::{image} ../../../images/machine-learning-ml-nlp-elser-v2-download.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-elser-v2-download.png :alt: Downloading ELSER :screenshot: ::: @@ -105,7 +105,7 @@ You can also download and deploy ELSER either from **{{ml-app}}** > **Trained Mo 3. After the download is finished, start the deployment by clicking the **Start deployment** button. 4. Provide a deployment ID, select the priority, and set the number of allocations and threads per allocation values. - :::{image} ../../../images/machine-learning-ml-nlp-deployment-id-elser-v2.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-deployment-id-elser-v2.png :alt: Deploying ELSER :screenshot: ::: @@ -125,14 +125,14 @@ Alternatively, you can download and deploy ELSER to an {{infer}} pipeline using 3. Navigate to the **Pipelines** tab. 4. Under **{{ml-app}} {{infer-cap}} Pipelines**, click the **Deploy** button to begin downloading the ELSER model. This may take a few minutes depending on your network. - :::{image} ../../../images/machine-learning-ml-nlp-deploy-elser-v2-es.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-deploy-elser-v2-es.png :alt: Deploying ELSER in Elasticsearch :screenshot: ::: 5. Once the model is downloaded, click the **Start single-threaded** button to start the model with basic configuration or select the **Fine-tune performance** option to navigate to the **Trained Models** page where you can configure the model deployment. - :::{image} ../../../images/machine-learning-ml-nlp-start-elser-v2-es.png + :::{image} /explore-analyze/images/machine-learning-ml-nlp-start-elser-v2-es.png :alt: Start ELSER in Elasticsearch :screenshot: ::: @@ -269,7 +269,7 @@ You can use data from an existing index to test the model. Select the index, the The results contain a list of ten random values for the selected field along with a score showing how relevant each document is to the query. The higher the score, the more relevant the document is. You can reload example documents by clicking **Reload examples**. -:::{image} ../../../images/machine-learning-ml-nlp-elser-v2-test.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-elser-v2-test.png :alt: Testing ELSER :screenshot: ::: @@ -325,7 +325,7 @@ The metric that is used to evaluate ELSER’s ranking ability is the Normalized The table below shows the performance of ELSER V2 compared to BM 25. ELSER V2 has 10 wins, 1 draw, 1 loss and an average improvement in NDCG@10 of 18%. -:::{image} ../../../images/machine-learning-ml-nlp-bm25-elser-v2.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-bm25-elser-v2.png :alt: ELSER V2 benchmarks compared to BM25 ::: *NDCG@10 for BEIR data sets for BM25 and ELSER V2 - higher values are better* @@ -348,18 +348,18 @@ The length of the documents in your particular dataset will have a significant i Refer to [this blog post](https://www.elastic.co/search-labs/blog/introducing-elser-v2-part-1) to learn more about ELSER V2 improved performance. -:::{image} ../../../images/machine-learning-ml-nlp-elser-bm-summary.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-elser-bm-summary.png :alt: Summary of ELSER V1 and V2 benchmark reports ::: **The optimized model** results show a nearly linear growth up until 8 allocations, after which performance improvements become smaller. In this case, the performance at 8 allocations was 22 docs/s, while the performance of 16 allocations was 26 docs/s, indicating a 20% performance increase due to virtual cores. -:::{image} ../../../images/machine-learning-ml-nlp-elser-v2-opt-bm-results.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-elser-v2-opt-bm-results.png :alt: ELSER V2 optimized benchmarks ::: **The cross-platform** model performance of 8 and 16 allocations are respectively 14 docs/s and 16 docs/s, indicating a performance improvement due to virtual cores of 12%. -:::{image} ../../../images/machine-learning-ml-nlp-elser-v2-cp-bm-results.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-elser-v2-cp-bm-results.png :alt: ELSER V2 cross-platform benchmarks ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-inference.md b/explore-analyze/machine-learning/nlp/ml-nlp-inference.md index e6757a4f49..8b8dd08f6a 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-inference.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-inference.md @@ -19,7 +19,7 @@ After you [deploy a trained model in your cluster](ml-nlp-deploy-models.md), you In {{kib}}, you can create and edit pipelines in **{{stack-manage-app}}** > **Ingest Pipelines**. To open **Ingest Pipelines**, find **{{stack-manage-app}}** in the main menu, or use the [global search field](../../find-and-organize/find-apps-and-objects.md). -:::{image} ../../../images/machine-learning-ml-nlp-pipeline-lang.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-pipeline-lang.png :alt: Creating a pipeline in the Stack Management app :screenshot: ::: @@ -171,7 +171,7 @@ Set the reindex `size` option to a value smaller than the `queue_capacity` for t Before you can verify the results of the pipelines, you must [create {{data-sources}}](../../find-and-organize/data-views.md). Then you can explore your data in **Discover**: -:::{image} ../../../images/machine-learning-ml-nlp-discover-ner.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-discover-ner.png :alt: A document from the NER pipeline in the Discover app :screenshot: ::: @@ -180,7 +180,7 @@ The `ml.inference.predicted_value` field contains the output from the {{infer}} In this {{lang-ident}} example, the `ml.inference.predicted_value` contains the ISO identifier of the language with the highest probability and the `ml.inference.top_classes` fields contain the top five most probable languages and their scores: -:::{image} ../../../images/machine-learning-ml-nlp-discover-lang.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-discover-lang.png :alt: A document from the {{lang-ident}} pipeline in the Discover app :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md index d3adea9da4..472bb06a76 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md @@ -53,7 +53,7 @@ Go to the **{{ml-app}} > Trained Models** page and synchronize your trained mode Deployed models can be evaluated in {{kib}} under **{{ml-app}}** > **Trained Models** by selecting the **Test model** action for the respective model. -:::{image} ../../../images/machine-learning-ml-nlp-ner-test.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-ner-test.png :alt: Test trained model UI :screenshot: ::: @@ -248,7 +248,7 @@ Optionally, adjust the time selector to cover the data points in the {{data-sour Update and save the visualization. -:::{image} ../../../images/machine-learning-ml-nlp-tag-cloud.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-tag-cloud.png :alt: Tag cloud created from Les Misérables :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-test-inference.md b/explore-analyze/machine-learning/nlp/ml-nlp-test-inference.md index 7a5d4daf76..ee0255dbd1 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-test-inference.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-test-inference.md @@ -12,7 +12,7 @@ When the model is deployed on at least one node in the cluster, you can begin to The simplest method to test your model against new data is to use the **Test model** action in {{kib}}. You can either provide some input text or use a field of an existing index in your cluster to test the model: -:::{image} ../../../images/machine-learning-ml-nlp-test-ner.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-test-ner.png :alt: Testing a sentence with two named entities against a NER trained model in the *{{ml}}* app :screenshot: ::: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md index ddd366765e..16bf2f74b0 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md @@ -57,7 +57,7 @@ Go to the **{{ml-app}} > Trained Models** page and synchronize your trained mode Deployed models can be evaluated in {{kib}} under **{{ml-app}}** > **Trained Models** by selecting the **Test model** action for the respective model. -:::{image} ../../../images/machine-learning-ml-nlp-text-emb-test.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-text-emb-test.png :alt: Test trained model UI :screenshot: ::: @@ -105,7 +105,7 @@ The data set `msmarco-passagetest2019-top1000` is a subset of the MS MARCO Passa Upload the file by using the [Data Visualizer](../../../manage-data/ingest/upload-data-files.md). Name the first column `id` and the second one `text`. The index name is `collection`. After the upload is done, you can see an index named `collection` with 182469 documents. -:::{image} ../../../images/machine-learning-ml-nlp-text-emb-data.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-text-emb-data.png :alt: Importing the data :screenshot: ::: @@ -197,7 +197,7 @@ GET _tasks/ You can also open the model stat UI to follow the progress. -:::{image} ../../../images/machine-learning-ml-nlp-text-emb-reindex.png +:::{image} /explore-analyze/images/machine-learning-ml-nlp-text-emb-reindex.png :alt: Model status UI :screenshot: ::: diff --git a/explore-analyze/machine-learning/setting-up-machine-learning.md b/explore-analyze/machine-learning/setting-up-machine-learning.md index 2d607dd705..e10fd4beff 100644 --- a/explore-analyze/machine-learning/setting-up-machine-learning.md +++ b/explore-analyze/machine-learning/setting-up-machine-learning.md @@ -68,7 +68,7 @@ Granting `All` or `Read` {{kib}} feature privilege for {{ml-app}} will also gran In {{kib}}, the {{ml-features}} must be visible in your [space](../../deploy-manage/manage-spaces.md). To manage which features are visible in your space, go to **{{stack-manage-app}}** > **{{kib}}** > **Spaces** or use the [global search field](../find-and-organize/find-apps-and-objects.md) to locate **Spaces** directly. -:::{image} ../../images/machine-learning-spaces.jpg +:::{image} /explore-analyze/images/machine-learning-spaces.jpg :alt: Manage spaces in {{kib}} :screenshot: ::: @@ -77,7 +77,7 @@ In addition to index privileges, source {{data-sources}} must also exist in the Each {{ml}} job and trained model can be assigned to all, one, or multiple spaces. This can be configured in **Machine Learning**. To open **Machine Learning**, find **{{stack-manage-app}} > Alerts and Insights** in the main menu, or use the [global search field](../find-and-organize/find-apps-and-objects.md). You can edit the spaces that a job or model is assigned to by clicking the icons in the **Spaces** column. -:::{image} ../../images/machine-learning-assign-job-spaces.jpg +:::{image} /explore-analyze/images/machine-learning-assign-job-spaces.jpg :alt: Assign machine learning jobs to spaces :screenshot: ::: diff --git a/explore-analyze/query-filter/filtering.md b/explore-analyze/query-filter/filtering.md index 240fcd3944..1ab6325125 100644 --- a/explore-analyze/query-filter/filtering.md +++ b/explore-analyze/query-filter/filtering.md @@ -19,7 +19,7 @@ Some apps provide more options, such as [Dashboards](../dashboards.md). Display data within a specified time range when your index contains time-based events, and a time-field is configured for the selected [{{data-source}}](../find-and-organize/data-views.md). The default time range is 15 minutes, but you can customize it in [Advanced Settings](kibana://reference/advanced-settings.md). -1. Click ![calendar icon](../../images/kibana-time-filter-icon.png). +1. Click ![calendar icon](/explore-analyze/images/kibana-time-filter-icon.png). 2. Choose one of the following: * **Quick select**. Set a time based on the last or next number of seconds, minutes, hours, or other time unit. @@ -27,14 +27,14 @@ Display data within a specified time range when your index contains time-based e * **Recently used date ranges**. Use a previously selected data range. * **Refresh every**. Specify an automatic refresh rate. -:::{image} ../../images/kibana-time-filter.png +:::{image} /explore-analyze/images/kibana-time-filter.png :alt: Time filter menu :width: 300px ::: 3. To set start and end times, click the bar next to the time filter. In the popup, select **Absolute**, **Relative** or **Now**, then specify the required options. -:::{image} ../../images/kibana-time-relative.png +:::{image} /explore-analyze/images/kibana-time-relative.png :alt: Time filter showing relative time :width: 350px ::: @@ -49,4 +49,4 @@ To manually resubmit a search, click the **Refresh** button. This is useful when Structured filters are a more interactive way to create {{es}} queries, and are commonly used when building dashboards that are shared by multiple analysts. Each filter can be disabled, inverted, or pinned across all apps. Each of the structured filters is combined with AND logic on the rest of the query. -![Add filter popup](../../images/kibana-add-filter-popup.png "") \ No newline at end of file +![Add filter popup](/explore-analyze/images/kibana-add-filter-popup.png "") \ No newline at end of file diff --git a/explore-analyze/query-filter/languages/esql-getting-started.md b/explore-analyze/query-filter/languages/esql-getting-started.md index ccb253d5d2..724f76cd01 100644 --- a/explore-analyze/query-filter/languages/esql-getting-started.md +++ b/explore-analyze/query-filter/languages/esql-getting-started.md @@ -106,7 +106,7 @@ After switching to {{esql}} mode, the query bar shows a sample query. You can re To make it easier to write queries, auto-complete offers suggestions with possible commands and functions: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-auto-complete.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-auto-complete.png :alt: esql kibana auto complete ::: @@ -119,7 +119,7 @@ You can adjust the editor’s height by dragging its bottom border to your likin Each {{esql}} query starts with a [source command](elasticsearch://reference/query-languages/esql/esql-commands.md#esql-source-commands). A source command produces a table, typically with data from {{es}}. -:::{image} ../../../images/elasticsearch-reference-source-command.svg +:::{image} /explore-analyze/images/elasticsearch-reference-source-command.svg :alt: A source command producing a table from {{es}} ::: @@ -146,7 +146,7 @@ from sample_data A source command can be followed by one or more [processing commands](elasticsearch://reference/query-languages/esql/esql-commands.md#esql-processing-commands), separated by a pipe character: `|`. Processing commands change an input table by adding, removing, or changing rows and columns. Processing commands can perform filtering, projection, aggregation, and more. -:::{image} ../../../images/elasticsearch-reference-esql-limit.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-limit.png :alt: A processing command changing an input table ::: @@ -170,7 +170,7 @@ FROM sample_data | LIMIT 3 ### Sort a table [esql-getting-started-sort] -:::{image} ../../../images/elasticsearch-reference-esql-sort.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-sort.png :alt: A processing command sorting an input table ::: @@ -208,7 +208,7 @@ There are many other processing commands, like [`KEEP`](elasticsearch://referenc You can chain processing commands, separated by a pipe character: `|`. Each processing command works on the output table of the previous command. The result of a query is the table produced by the final processing command. -:::{image} ../../../images/elasticsearch-reference-esql-sort-limit.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-sort-limit.png :alt: Processing commands can be chained ::: @@ -311,7 +311,7 @@ FROM sample_data {{esql}} enables you to [enrich](elasticsearch://reference/query-languages/esql/esql-enrich-data.md) a table with data from indices in {{es}}, using the [`ENRICH`](elasticsearch://reference/query-languages/esql/esql-commands.md#esql-enrich) command. -:::{image} ../../../images/elasticsearch-reference-esql-enrich.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-enrich.png :alt: esql enrich ::: diff --git a/explore-analyze/query-filter/languages/esql-kibana.md b/explore-analyze/query-filter/languages/esql-kibana.md index 43ba26987e..347eee1a8d 100644 --- a/explore-analyze/query-filter/languages/esql-kibana.md +++ b/explore-analyze/query-filter/languages/esql-kibana.md @@ -50,7 +50,7 @@ Click the **ES|QL help** button to open the in-product reference documentation f To make it easier to write queries, auto-complete offers suggestions with possible commands and functions: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-auto-complete.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-auto-complete.png :alt: esql kibana auto complete ::: @@ -76,7 +76,7 @@ FROM kibana_sample_data_logs You can do that using the **Add line breaks on pipes** button from the query editor’s footer. -:::{image} ../../../images/esql-line-breakdown.gif +:::{image} /explore-analyze/images/esql-line-breakdown.gif :alt: Automatic line breaks for ES|QL queries ::: @@ -94,7 +94,7 @@ You can reuse your recent {{esql}} queries in the query bar. In the query bar, c You can then scroll through your recent queries: -:::{image} ../../../images/elasticsearch-reference-esql-discover-query-history.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-discover-query-history.png :alt: esql discover query history ::: @@ -102,7 +102,7 @@ You can then scroll through your recent queries: {{esql}} features in-app help and suggestions, so you can get started faster and don’t have to leave the application to check syntax. -![The ES|QL syntax reference and the autocomplete menu](../../../images/kibana-esql-in-app-help.png "") +![The ES|QL syntax reference and the autocomplete menu](/explore-analyze/images/kibana-esql-in-app-help.png "") ### Starred queries [esql-kibana-starred-queries] @@ -115,7 +115,7 @@ From the **Recent** tab, you can star any queries you want. In the **Starred** tab, find all the queries you have previously starred. -:::{image} ../../../images/elasticsearch-reference-esql-discover-query-starred.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-discover-query-starred.png :alt: esql discover query starred ::: @@ -214,19 +214,19 @@ FROM kibana_sample_data_logs The resulting visualization is a bar chart showing the top 3 countries: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-bar-chart.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-bar-chart.png :alt: esql kibana bar chart ::: -To make changes to the visualization, like changing the visualization type, axes and colors, click the pencil button (![esql icon edit visualization](../../../images/elasticsearch-reference-esql-icon-edit-visualization.svg "")). This opens an in-line editor: +To make changes to the visualization, like changing the visualization type, axes and colors, click the pencil button (![esql icon edit visualization](/explore-analyze/images/elasticsearch-reference-esql-icon-edit-visualization.svg "")). This opens an in-line editor: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-in-line-editor.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-in-line-editor.png :alt: esql kibana in line editor ::: -You can save the visualization to a new or existing dashboard by clicking the save button (![esql icon save visualization](../../../images/elasticsearch-reference-esql-icon-save-visualization.svg "")). Once saved to a dashboard, you’ll be taken to the Dashboards page. You can continue to make changes to the visualization. Click the options button in the top-right (![esql icon options](../../../images/elasticsearch-reference-esql-icon-options.svg "")) and select **Edit ES|QL visualization** to open the in-line editor: +You can save the visualization to a new or existing dashboard by clicking the save button (![esql icon save visualization](/explore-analyze/images/elasticsearch-reference-esql-icon-save-visualization.svg "")). Once saved to a dashboard, you’ll be taken to the Dashboards page. You can continue to make changes to the visualization. Click the options button in the top-right (![esql icon options](/explore-analyze/images/elasticsearch-reference-esql-icon-options.svg "")) and select **Edit ES|QL visualization** to open the in-line editor: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-edit-on-dashboard.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-edit-on-dashboard.png :alt: esql kibana edit on dashboard ::: @@ -235,19 +235,19 @@ You can save the visualization to a new or existing dashboard by clicking the sa You can use {{esql}} queries to create panels on your dashboards. To add a panel to a dashboard, under **Dashboards**, click the **Add panel** button and select {{esql}}. -:::{image} ../../../images/elasticsearch-reference-esql-dashboard-panel.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-dashboard-panel.png :alt: esql dashboard panel ::: -Check the {{esql}} query by clicking the Panel filters button (![Panel filters button on panel header](../../../images/elasticsearch-reference-dashboard_panel_filter_button.png "")): +Check the {{esql}} query by clicking the Panel filters button (![Panel filters button on panel header](/explore-analyze/images/elasticsearch-reference-dashboard_panel_filter_button.png "")): -:::{image} ../../../images/elasticsearch-reference-esql-dashboard-panel-query.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-dashboard-panel-query.png :alt: esql dashboard panel query ::: -You can also edit the {{esql}} visualization from here. Click the options button in the top-right (![esql icon options](../../../images/elasticsearch-reference-esql-icon-options.svg "")) and select **Edit ESQL visualization** to open the in-line editor. +You can also edit the {{esql}} visualization from here. Click the options button in the top-right (![esql icon options](/explore-analyze/images/elasticsearch-reference-esql-icon-options.svg "")) and select **Edit ESQL visualization** to open the in-line editor. -:::{image} ../../../images/elasticsearch-reference-esql-dashboard-panel-edit-visualization.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-dashboard-panel-edit-visualization.png :alt: esql dashboard panel edit visualization ::: @@ -256,19 +256,19 @@ You can also edit the {{esql}} visualization from here. Click the options button The {{esql}} [`ENRICH`](elasticsearch://reference/query-languages/esql/esql-commands.md#esql-enrich) command enables you to [enrich](elasticsearch://reference/query-languages/esql/esql-enrich-data.md) your query dataset with fields from another dataset. Before you can use `ENRICH`, you need to [create and execute an enrich policy](elasticsearch://reference/query-languages/esql/esql-enrich-data.md#esql-set-up-enrich-policy). If a policy exists, it will be suggested by auto-complete. If not, click **Click to create** to create one. -:::{image} ../../../images/elasticsearch-reference-esql-kibana-enrich-autocomplete.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-enrich-autocomplete.png :alt: esql kibana enrich autocomplete ::: Next, you can enter a policy name, the policy type, source indices, and optionally a query: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-enrich-step-1.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-enrich-step-1.png :alt: esql kibana enrich step 1 ::: Click **Next** to select the match field and enrich fields: -:::{image} ../../../images/elasticsearch-reference-esql-kibana-enrich-step-2.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-enrich-step-2.png :alt: esql kibana enrich step 2 ::: @@ -289,7 +289,7 @@ FROM kibana_sample_data_logs You can use {{esql}} queries to create alerts. From Discover, click **Alerts** and select **Create search threshold rule**. This opens a panel that enables you to create a rule using an {{esql}} query. Next, you can test the query, add a connector, and save the rule. -:::{image} ../../../images/elasticsearch-reference-esql-kibana-create-rule.png +:::{image} /explore-analyze/images/elasticsearch-reference-esql-kibana-create-rule.png :alt: esql kibana create rule ::: diff --git a/explore-analyze/query-filter/languages/lucene-query-syntax.md b/explore-analyze/query-filter/languages/lucene-query-syntax.md index 96f325b5f7..3fa32a1adc 100644 --- a/explore-analyze/query-filter/languages/lucene-query-syntax.md +++ b/explore-analyze/query-filter/languages/lucene-query-syntax.md @@ -14,7 +14,7 @@ The main reason to use the Lucene query syntax in {{kib}} is for advanced Lucene To use the Lucene syntax, open the **Saved query** menu, and then select **Language: KQL** > **Lucene**. -![Click the circle icon for the saved query menu](../../../images/kibana-lucene.png "") +![Click the circle icon for the saved query menu](/explore-analyze/images/kibana-lucene.png "") To perform a free text search, simply enter a text string. For example, if you’re searching web server logs, you could enter `safari` to search all fields: diff --git a/explore-analyze/query-filter/languages/sql-client-apps-dbeaver.md b/explore-analyze/query-filter/languages/sql-client-apps-dbeaver.md index 0809c5f8d0..3b05dfe36a 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-dbeaver.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-dbeaver.md @@ -25,28 +25,28 @@ Elastic does not endorse, promote or provide support for this application; for n Create a new connection either through the menu **File** > **New** > **Database Connection** menu or directly through the **Database Connection** panel. -![dbeaver 1 new conn](../../../images/elasticsearch-reference-dbeaver-1-new-conn.png "") +![dbeaver 1 new conn](/explore-analyze/images/elasticsearch-reference-dbeaver-1-new-conn.png "") ## Select {{es}} type [_select_es_type] Select the {{es}} type from the available connection types: -![dbeaver 2 conn es](../../../images/elasticsearch-reference-dbeaver-2-conn-es.png "") +![dbeaver 2 conn es](/explore-analyze/images/elasticsearch-reference-dbeaver-2-conn-es.png "") ## Specify the {{es}} cluster information [_specify_the_es_cluster_information] Configure the Elasticsearch SQL connection appropriately: -![dbeaver 3 conn props](../../../images/elasticsearch-reference-dbeaver-3-conn-props.png "") +![dbeaver 3 conn props](/explore-analyze/images/elasticsearch-reference-dbeaver-3-conn-props.png "") ## Verify the driver version [_verify_the_driver_version] Make sure the correct JDBC driver version is used by using the **Edit Driver Settings** button: -![dbeaver 4 driver ver](../../../images/elasticsearch-reference-dbeaver-4-driver-ver.png "") +![dbeaver 4 driver ver](/explore-analyze/images/elasticsearch-reference-dbeaver-4-driver-ver.png "") DBeaver is aware of the {{es}} JDBC maven repository so simply **Download/Update** the artifact or add a new one. As an alternative one can add a local file instead if the {{es}} Maven repository is not an option. @@ -57,7 +57,7 @@ When changing the driver, make sure to click on the **Find Class** button at the Once the driver version and the settings are in place, use **Test Connection** to check that everything works. If things are okay, one should get a confirmation window with the version of the driver and that of Elasticsearch SQL: -![dbeaver 5 test conn](../../../images/elasticsearch-reference-dbeaver-5-test-conn.png "") +![dbeaver 5 test conn](/explore-analyze/images/elasticsearch-reference-dbeaver-5-test-conn.png "") Click **Finish** and the new {{es}} connection appears in the **Database Connection** panel. @@ -68,6 +68,6 @@ DBeaver is now configured to talk to {{es}}. Simply click on the {{es}} connection and start querying and exploring {{es}}: -![dbeaver 6 data](../../../images/elasticsearch-reference-dbeaver-6-data.png "") +![dbeaver 6 data](/explore-analyze/images/elasticsearch-reference-dbeaver-6-data.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-dbvis.md b/explore-analyze/query-filter/languages/sql-client-apps-dbvis.md index aa003ba244..3d1367d573 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-dbvis.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-dbvis.md @@ -29,40 +29,40 @@ Elastic does not endorse, promote or provide support for this application. Setup the {{es}} JDBC driver through **Tools** > **Driver Manager**: -![dbvis 1 driver manager](../../../images/elasticsearch-reference-dbvis-1-driver-manager.png "") +![dbvis 1 driver manager](/explore-analyze/images/elasticsearch-reference-dbvis-1-driver-manager.png "") Select **Elasticsearch** driver template from the left sidebar to create a new user driver: -![dbvis 2 driver manager elasticsearch](../../../images/elasticsearch-reference-dbvis-2-driver-manager-elasticsearch.png "") +![dbvis 2 driver manager elasticsearch](/explore-analyze/images/elasticsearch-reference-dbvis-2-driver-manager-elasticsearch.png "") Download the driver locally: -![dbvis 3 driver manager download](../../../images/elasticsearch-reference-dbvis-3-driver-manager-download.png "") +![dbvis 3 driver manager download](/explore-analyze/images/elasticsearch-reference-dbvis-3-driver-manager-download.png "") and check its availability status: -![dbvis 4 driver manager ready](../../../images/elasticsearch-reference-dbvis-4-driver-manager-ready.png "") +![dbvis 4 driver manager ready](/explore-analyze/images/elasticsearch-reference-dbvis-4-driver-manager-ready.png "") ## Create a new connection [_create_a_new_connection] Once the {{es}} driver is in place, create a new connection: -![dbvis 5 new conn](../../../images/elasticsearch-reference-dbvis-5-new-conn.png "") +![dbvis 5 new conn](/explore-analyze/images/elasticsearch-reference-dbvis-5-new-conn.png "") by double-clicking the {{es}} entry in the list of available drivers: -![dbvis 6 new conn elasticsearch](../../../images/elasticsearch-reference-dbvis-6-new-conn-elasticsearch.png "") +![dbvis 6 new conn elasticsearch](/explore-analyze/images/elasticsearch-reference-dbvis-6-new-conn-elasticsearch.png "") Enter the connection details, then press **Connect** and the driver version (as that of the cluster) should show up under **Connection Message**. -![dbvis 7 new conn connect](../../../images/elasticsearch-reference-dbvis-7-new-conn-connect.png "") +![dbvis 7 new conn connect](/explore-analyze/images/elasticsearch-reference-dbvis-7-new-conn-connect.png "") ## Execute SQL queries [_execute_sql_queries] The setup is done. DbVisualizer can be used to run queries against {{es}} and explore its content: -![dbvis 8 data](../../../images/elasticsearch-reference-dbvis-8-data.png "") +![dbvis 8 data](/explore-analyze/images/elasticsearch-reference-dbvis-8-data.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-excel.md b/explore-analyze/query-filter/languages/sql-client-apps-excel.md index 5be5d87a67..5086381dad 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-excel.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-excel.md @@ -27,12 +27,12 @@ Elastic does not endorse, promote or provide support for this application; for n First, you’ll need to choose ODBC as the source to load data from. To do so, click on the *Data* tab, then *New Query* button, in the drop-down menu expand *From Other Sources*, then choose *From ODBC*: $$$apps_excel_fromodbc$$$ -![apps excel fromodbc](../../../images/elasticsearch-reference-apps_excel_fromodbc.png "") +![apps excel fromodbc](/explore-analyze/images/elasticsearch-reference-apps_excel_fromodbc.png "") This will open a new window with a drop down menu populated with the DSNs that Excel found on the system. Choose a DSN configured to connect to your {{es}} instance and press the *OK* button: $$$apps_excel_dsn$$$ -![apps excel dsn](../../../images/elasticsearch-reference-apps_excel_dsn.png "") +![apps excel dsn](/explore-analyze/images/elasticsearch-reference-apps_excel_dsn.png "") This will lead to a new window, allowing the user to input the connection credentials. @@ -41,16 +41,16 @@ A username might be required by Excel even if the {{es}} instance has no securit Fill in the username and the password and press *Connect*. $$$apps_excel_cred$$$ -![apps excel cred](../../../images/elasticsearch-reference-apps_excel_cred.png "") +![apps excel cred](/explore-analyze/images/elasticsearch-reference-apps_excel_cred.png "") Once connected, Excel will read {{es}}'s catalog and offer the user a choice of tables (indices) to load data from. Clicking on one of the tables will load a preview of the data within: $$$apps_excel_picktable$$$ -![apps excel picktable](../../../images/elasticsearch-reference-apps_excel_picktable.png "") +![apps excel picktable](/explore-analyze/images/elasticsearch-reference-apps_excel_picktable.png "") Now click the *Load* button, which will have Excel load all the data from the table into a spreadsheet: $$$apps_excel_loaded$$$ -![apps excel loaded](../../../images/elasticsearch-reference-apps_excel_loaded.png "") +![apps excel loaded](/explore-analyze/images/elasticsearch-reference-apps_excel_loaded.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-microstrat.md b/explore-analyze/query-filter/languages/sql-client-apps-microstrat.md index 68e6073207..186ec14f0a 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-microstrat.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-microstrat.md @@ -31,28 +31,28 @@ To use the Elasticsearch SQL ODBC Driver to load data into MicroStrategy Desktop Once the application is launched, you’ll first need to create a *New Dossier*: $$$apps_microstrat_newdossier$$$ - ![apps microstrat newdossier](../../../images/elasticsearch-reference-apps_microstrat_newdossier.png "") + ![apps microstrat newdossier](/explore-analyze/images/elasticsearch-reference-apps_microstrat_newdossier.png "") 2. New Data To import into the *New Dossier* just opened, press on the *New Data* button in the *DATASETS* column: $$$apps_microstrat_newdata$$$ - ![apps microstrat newdata](../../../images/elasticsearch-reference-apps_microstrat_newdata.png "") + ![apps microstrat newdata](/explore-analyze/images/elasticsearch-reference-apps_microstrat_newdata.png "") 3. Access data from Tables This opens a new window that allows to choose the source to load data from. Click on the *Databases* icon: $$$apps_microstrat_databases$$$ - ![apps microstrat databases](../../../images/elasticsearch-reference-apps_microstrat_databases.png "") + ![apps microstrat databases](/explore-analyze/images/elasticsearch-reference-apps_microstrat_databases.png "") 4. New Data Source In the newly opened *Import from Table - Select* window, click on the **+** button to the right of *DATA SOURCES* item: $$$apps_microstrat_newds$$$ - ![apps microstrat newds](../../../images/elasticsearch-reference-apps_microstrat_newds.png "") + ![apps microstrat newds](/explore-analyze/images/elasticsearch-reference-apps_microstrat_newds.png "") 5. Data Source @@ -63,35 +63,35 @@ To use the Elasticsearch SQL ODBC Driver to load data into MicroStrategy Desktop Finally, give a name to the application-specific data source you’re just configuring: $$$apps_microstrat_dsn$$$ - ![apps microstrat dsn](../../../images/elasticsearch-reference-apps_microstrat_dsn.png "") + ![apps microstrat dsn](/explore-analyze/images/elasticsearch-reference-apps_microstrat_dsn.png "") 6. Select Import Options Choosing an import methodology follows. You can pick any of the options; we’ll exemplify the *Select Tables* option: $$$apps_microstrat_tables$$$ - ![apps microstrat tables](../../../images/elasticsearch-reference-apps_microstrat_tables.png "") + ![apps microstrat tables](/explore-analyze/images/elasticsearch-reference-apps_microstrat_tables.png "") 7. Import from Table - Select The data source you’ve named two steps before is now listed in the *DATA SOURCES* column. Clicking on its name triggers the application to query the {{es}} instance configured in the DSN and list the tables available within: $$$apps_microstrat_loadtable$$$ - ![apps microstrat loadtable](../../../images/elasticsearch-reference-apps_microstrat_loadtable.png "") + ![apps microstrat loadtable](/explore-analyze/images/elasticsearch-reference-apps_microstrat_loadtable.png "") 8. Data Access Mode Choose a table to load data from and press the *Finish* button. When doing so, the application offers to choose a loading methodology. You can choose whichever, we’ll exemplify the *Connect Live* way: $$$apps_microstrat_live$$$ - ![apps microstrat live](../../../images/elasticsearch-reference-apps_microstrat_live.png "") + ![apps microstrat live](/explore-analyze/images/elasticsearch-reference-apps_microstrat_live.png "") 9. Visualize the data From the *DATASETS* column you can choose what table columns (or index fields) to visualize: $$$apps_microstrat_visualize$$$ - ![apps microstrat visualize](../../../images/elasticsearch-reference-apps_microstrat_visualize.png "") + ![apps microstrat visualize](/explore-analyze/images/elasticsearch-reference-apps_microstrat_visualize.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-powerbi.md b/explore-analyze/query-filter/languages/sql-client-apps-powerbi.md index fdee5a09e9..2362947818 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-powerbi.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-powerbi.md @@ -27,26 +27,26 @@ Elastic does not endorse, promote or provide support for this application; for n First, you’ll need to choose ODBC as the source to load data from. Once launched, click on the *Get Data* button (under *Home* tab), then on the *More…​* button at the bottom of the list: $$$apps_pbi_fromodbc1$$$ -![apps pbi fromodbc1](../../../images/elasticsearch-reference-apps_pbi_fromodbc1.png "") +![apps pbi fromodbc1](/explore-analyze/images/elasticsearch-reference-apps_pbi_fromodbc1.png "") In the new opened window scroll at the bottom of the *All* list and select the *ODBC* entry, then click on the *Connect* button: $$$apps_pbi_fromodbc2$$$ -![apps pbi fromodbc2](../../../images/elasticsearch-reference-apps_pbi_fromodbc2.png "") +![apps pbi fromodbc2](/explore-analyze/images/elasticsearch-reference-apps_pbi_fromodbc2.png "") This will replace current window with a new *From ODBC* one, where you’ll have to select a previously configured DSN: $$$apps_pbi_dsn$$$ -![apps pbi dsn](../../../images/elasticsearch-reference-apps_pbi_dsn.png "") +![apps pbi dsn](/explore-analyze/images/elasticsearch-reference-apps_pbi_dsn.png "") Once connected Power BI will read {{es}}'s catalog and offer the user a choice of tables (indices) to load data from. Clicking on one of the tables will load a preview of the data within: $$$apps_pbi_picktable$$$ -![apps pbi picktable](../../../images/elasticsearch-reference-apps_pbi_picktable.png "") +![apps pbi picktable](/explore-analyze/images/elasticsearch-reference-apps_pbi_picktable.png "") Now tick the chosen table and click on the *Load* button. Power BI will now load and analyze the data, populating a list with the available columns. These can now be used to build the desired visualisation: $$$apps_pbi_loaded$$$ -![apps pbi loaded](../../../images/elasticsearch-reference-apps_pbi_loaded.png "") +![apps pbi loaded](/explore-analyze/images/elasticsearch-reference-apps_pbi_loaded.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-ps1.md b/explore-analyze/query-filter/languages/sql-client-apps-ps1.md index d7661919d7..fdc5ad7d69 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-ps1.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-ps1.md @@ -43,6 +43,6 @@ $dt Now open a PowerShell shell and simply execute the script: $$$apps_excel_exed$$$ -![apps ps exed](../../../images/elasticsearch-reference-apps_ps_exed.png "") +![apps ps exed](/explore-analyze/images/elasticsearch-reference-apps_ps_exed.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-qlik.md b/explore-analyze/query-filter/languages/sql-client-apps-qlik.md index 52c9efc746..042636fab0 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-qlik.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-qlik.md @@ -31,42 +31,42 @@ To use the Elasticsearch SQL ODBC Driver to load data into Qlik Sense Desktop pe Once the application is launched, you’ll first need to click on the *Create new app* button: $$$apps_qlik_newapp$$$ - ![apps qlik newapp](../../../images/elasticsearch-reference-apps_qlik_newapp.png "") + ![apps qlik newapp](/explore-analyze/images/elasticsearch-reference-apps_qlik_newapp.png "") 2. Name app …​then give it a name, $$$apps_qlik_create$$$ - ![apps qlik create](../../../images/elasticsearch-reference-apps_qlik_create.png "") + ![apps qlik create](/explore-analyze/images/elasticsearch-reference-apps_qlik_create.png "") 3. Open app …​and then open it: $$$apps_qlik_open$$$ - ![apps qlik open](../../../images/elasticsearch-reference-apps_qlik_open.png "") + ![apps qlik open](/explore-analyze/images/elasticsearch-reference-apps_qlik_open.png "") 4. Add data to your app Start configuring the source to load data from in the newly created app: $$$apps_qlik_adddata$$$ - ![apps qlik adddata](../../../images/elasticsearch-reference-apps_qlik_adddata.png "") + ![apps qlik adddata](/explore-analyze/images/elasticsearch-reference-apps_qlik_adddata.png "") 5. Load from ODBC You’ll be given a choice of sources to select. Click on the *ODBC* icon: $$$apps_qlik_odbc$$$ - ![apps qlik odbc](../../../images/elasticsearch-reference-apps_qlik_odbc.png "") + ![apps qlik odbc](/explore-analyze/images/elasticsearch-reference-apps_qlik_odbc.png "") 6. Choose DSN In the *Create new connection (ODBC)* dialog, click on the DSN name that you have previously configured for your {{es}} instance: $$$apps_qlik_dsn$$$ - ![apps qlik dsn](../../../images/elasticsearch-reference-apps_qlik_dsn.png "") + ![apps qlik dsn](/explore-analyze/images/elasticsearch-reference-apps_qlik_dsn.png "") Provide a username and password in the respective fields, if authentication is enabled on your instance and if these are not already part of the DSN. Press the *Create* button. @@ -75,14 +75,14 @@ To use the Elasticsearch SQL ODBC Driver to load data into Qlik Sense Desktop pe The application will now connect to the {{es}} instance and query the catalog information, presenting you with a list of tables that you can load data from: $$$apps_qlik_selecttable$$$ - ![apps qlik selecttable](../../../images/elasticsearch-reference-apps_qlik_selecttable.png "") + ![apps qlik selecttable](/explore-analyze/images/elasticsearch-reference-apps_qlik_selecttable.png "") 8. Visualize the data Press on the *Add data* button and customize your data visualization: $$$apps_qlik_visualize$$$ - ![apps qlik visualize](../../../images/elasticsearch-reference-apps_qlik_visualize.png "") + ![apps qlik visualize](/explore-analyze/images/elasticsearch-reference-apps_qlik_visualize.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-squirrel.md b/explore-analyze/query-filter/languages/sql-client-apps-squirrel.md index ab6b32733f..665cbc70ea 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-squirrel.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-squirrel.md @@ -25,30 +25,30 @@ Elastic does not endorse, promote or provide support for this application; for n To add the {{es}} JDBC driver, use **Windows** > **View Drivers** menu (or Ctrl+Shift+D shortcut): -![squirell 1 view drivers](../../../images/elasticsearch-reference-squirell-1-view-drivers.png "") +![squirell 1 view drivers](/explore-analyze/images/elasticsearch-reference-squirell-1-view-drivers.png "") Select **Elasticsearch** profile from the `Drivers` panel on the left-hand side (if it is missing check the SQuirreL SQL version or add a new entry to the list through the + button in the upper left corner): -![squirell 2 select driver](../../../images/elasticsearch-reference-squirell-2-select-driver.png "") +![squirell 2 select driver](/explore-analyze/images/elasticsearch-reference-squirell-2-select-driver.png "") Select the **Extra Class Path** tab and **Add** the JDBC jar. Name the connection and **List Drivers** to have `Class Name` populated if it is not already filled-in : -![squirell 3 add driver](../../../images/elasticsearch-reference-squirell-3-add-driver.png "") +![squirell 3 add driver](/explore-analyze/images/elasticsearch-reference-squirell-3-add-driver.png "") The driver should now appear in the list with a blue check mark next to its name: -![squirell 4 driver list](../../../images/elasticsearch-reference-squirell-4-driver-list.png "") +![squirell 4 driver list](/explore-analyze/images/elasticsearch-reference-squirell-4-driver-list.png "") ## Add an alias for {{es}} [_add_an_alias_for_es] Add a new connection or in SQuirreL terminology an *alias* using the new driver. To do so, select the **Aliases** panel on the left and click the `+` sign: -![squirell 5 add alias](../../../images/elasticsearch-reference-squirell-5-add-alias.png "") +![squirell 5 add alias](/explore-analyze/images/elasticsearch-reference-squirell-5-add-alias.png "") Name the new alias and select the `Elasticsearch` driver previously added: -![squirell 6 alias props](../../../images/elasticsearch-reference-squirell-6-alias-props.png "") +![squirell 6 alias props](/explore-analyze/images/elasticsearch-reference-squirell-6-alias-props.png "") The setup is completed. Double check it by clicking on **Test Connection**. @@ -57,6 +57,6 @@ The setup is completed. Double check it by clicking on **Test Connection**. The connection should open automatically (if it has been created before simply click on **Connect** in the **Alias** panel). SQuirreL SQL can now issue SQL commands to {{es}}: -![squirell 7 data](../../../images/elasticsearch-reference-squirell-7-data.png "") +![squirell 7 data](/explore-analyze/images/elasticsearch-reference-squirell-7-data.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-tableau-desktop.md b/explore-analyze/query-filter/languages/sql-client-apps-tableau-desktop.md index 4b9b6e9149..1e2d54d8af 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-tableau-desktop.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-tableau-desktop.md @@ -37,19 +37,19 @@ Move the {{es}} Connector for Tableau to the Tableau Desktop connectors director Launch Tableau Desktop. In the menu, click **More…​** and select **Elasticsearch by Elastic** as the data source. $$$apps_tableau_desktop_from_connector$$$ -![Select Elasticsearch by Elastic as the data source](../../../images/elasticsearch-reference-apps_tableau_desktop_from_connector.png "") +![Select Elasticsearch by Elastic as the data source](/explore-analyze/images/elasticsearch-reference-apps_tableau_desktop_from_connector.png "") In the **New connection** modal, enter the information for your {{es}} instance, and click **Sign In**. $$$apps_tableau_connect$$$ -![Sign in](../../../images/elasticsearch-reference-apps_tableau_desktop_connect.png "") +![Sign in](/explore-analyze/images/elasticsearch-reference-apps_tableau_desktop_connect.png "") In the main window, select your {{es}} instance as the **Database**. Then select a table to load. $$$apps_tableau_prepare$$$ -![Select a table to load](../../../images/elasticsearch-reference-apps_tableau_desktop_prepare.png "") +![Select a table to load](/explore-analyze/images/elasticsearch-reference-apps_tableau_desktop_prepare.png "") Finally, generate a report. $$$apps_tableau_report$$$ -![Generate a report](../../../images/elasticsearch-reference-apps_tableau_desktop_report.png "") +![Generate a report](/explore-analyze/images/elasticsearch-reference-apps_tableau_desktop_report.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-tableau-server.md b/explore-analyze/query-filter/languages/sql-client-apps-tableau-server.md index bdef8a5bdd..ac9fe574ce 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-tableau-server.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-tableau-server.md @@ -36,19 +36,19 @@ Restart Tableau Server. To load data into a workbook, add a **New Data Source** from the **Data** menu or using the icon. In the **Connectors** tab of the **Connect to Data** modal, select **Elasticsearch by Elastic**. $$$apps_tableau_server_from_connector$$$ -![Select Elasticsearch as the data source](../../../images/elasticsearch-reference-apps_tableau_server_from_connector.png "") +![Select Elasticsearch as the data source](/explore-analyze/images/elasticsearch-reference-apps_tableau_server_from_connector.png "") Enter the information for your {{es}} instance, and click **Sign In**. $$$apps_tableau_server_connect$$$ -![Sign in](../../../images/elasticsearch-reference-apps_tableau_server_connect.png "") +![Sign in](/explore-analyze/images/elasticsearch-reference-apps_tableau_server_connect.png "") In the main window, select your {{es}} instance as the **Database**. Then select a table to load. $$$apps_tableau_server_prepare$$$ -![Select a table to load](../../../images/elasticsearch-reference-apps_tableau_server_prepare.png "") +![Select a table to load](/explore-analyze/images/elasticsearch-reference-apps_tableau_server_prepare.png "") Finally, generate a report. $$$apps_tableau_server_report$$$ -![Generate a report](../../../images/elasticsearch-reference-apps_tableau_server_report.png "") +![Generate a report](/explore-analyze/images/elasticsearch-reference-apps_tableau_server_report.png "") diff --git a/explore-analyze/query-filter/languages/sql-client-apps-workbench.md b/explore-analyze/query-filter/languages/sql-client-apps-workbench.md index 5607b26787..806ccf12ae 100644 --- a/explore-analyze/query-filter/languages/sql-client-apps-workbench.md +++ b/explore-analyze/query-filter/languages/sql-client-apps-workbench.md @@ -25,22 +25,22 @@ Elastic does not endorse, promote or provide support for this application; for n Add the {{es}} JDBC driver to SQL Workbench/J through **Manage Drivers** either from the main windows in the **File** menu or from the **Connect** window: -![workbench 1 manage drivers](../../../images/elasticsearch-reference-workbench-1-manage-drivers.png "") +![workbench 1 manage drivers](/explore-analyze/images/elasticsearch-reference-workbench-1-manage-drivers.png "") Select **Elasticsearch** profile from the left-hand side (if it is missing check the SQL Workbench/J version or add a new entry to the list through the blank page button in the upper left corner): -![workbench 2 select driver](../../../images/elasticsearch-reference-workbench-2-select-driver.png "") +![workbench 2 select driver](/explore-analyze/images/elasticsearch-reference-workbench-2-select-driver.png "") Add the JDBC jar (if the driver name hasn’t been picked up already, click on the magnifier button): -![workbench 3 add jar](../../../images/elasticsearch-reference-workbench-3-add-jar.png "") +![workbench 3 add jar](/explore-analyze/images/elasticsearch-reference-workbench-3-add-jar.png "") ## Create a new connection profile [_create_a_new_connection_profile] With the driver configured, create a new connection profile through **File** > **Connect Window** (or Alt+C shortcut): -![workbench 4 connection](../../../images/elasticsearch-reference-workbench-4-connection.png "") +![workbench 4 connection](/explore-analyze/images/elasticsearch-reference-workbench-4-connection.png "") Select the previously configured driver and set the URL of your cluster using the JDBC syntax. Verify the connection through the **Test** button - a confirmation window should appear that everything is properly configured. @@ -51,6 +51,6 @@ The setup is complete. SQL Workbench/J is ready to talk to {{es}} through SQL: click on the profile created to execute statements or explore the data: -![workbench 5 data](../../../images/elasticsearch-reference-workbench-5-data.png "") +![workbench 5 data](/explore-analyze/images/elasticsearch-reference-workbench-5-data.png "") diff --git a/explore-analyze/query-filter/languages/sql-odbc-installation.md b/explore-analyze/query-filter/languages/sql-odbc-installation.md index 91cbb1b89a..e567739140 100644 --- a/explore-analyze/query-filter/languages/sql-odbc-installation.md +++ b/explore-analyze/query-filter/languages/sql-odbc-installation.md @@ -69,13 +69,13 @@ Double-click the downloaded `.msi` package to launch a GUI wizard that will guid You will first be presented with a welcome screen: -:::{image} ../../../images/elasticsearch-reference-installer_started.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_started.png :alt: Installer Welcome Screen ::: Clicking **Next** will present the End User License Agreement. You will need to accept the license agreement in order to continue the installation. -:::{image} ../../../images/elasticsearch-reference-installer_accept_license.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_accept_license.png :alt: Installer EULA Screen ::: @@ -86,7 +86,7 @@ The default installation path is of the format: **%ProgramFiles%\Elastic\ODBCDri :::: -:::{image} ../../../images/elasticsearch-reference-installer_choose_destination.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_choose_destination.png :alt: Installer Driver Path ::: @@ -97,19 +97,19 @@ You will require elevated privileges (administrator) for installation. :::: -:::{image} ../../../images/elasticsearch-reference-installer_ready_install.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_ready_install.png :alt: Installer Begin ::: Assuming the installation takes place without error you should see progress screen, followed by the finish screen: -:::{image} ../../../images/elasticsearch-reference-installer_installing.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_installing.png :alt: Installer Installing ::: On the finish screen you can launch the ODBC Data Source Administration screen by checking the dialog checkbox. This will automatically launch the configuration screen on close (either 32 bit or 64 bit) where you can configure a DSN. -:::{image} ../../../images/elasticsearch-reference-installer_finish.png +:::{image} /explore-analyze/images/elasticsearch-reference-installer_finish.png :alt: Installer Complete ::: @@ -180,7 +180,7 @@ An installed program can be uninstalled by pressing the Windows key and typing ` Once opened, find the Elasticsearch ODBC Driver installation within the list of installed applications, click and choose `Uninstall`: -:::{image} ../../../images/elasticsearch-reference-uninstall.png +:::{image} /explore-analyze/images/elasticsearch-reference-uninstall.png :alt: uninstall :name: odbc-msi-installer-uninstall ::: diff --git a/explore-analyze/query-filter/languages/sql-odbc-setup.md b/explore-analyze/query-filter/languages/sql-odbc-setup.md index 9ccd2d8377..8d0faef020 100644 --- a/explore-analyze/query-filter/languages/sql-odbc-setup.md +++ b/explore-analyze/query-filter/languages/sql-odbc-setup.md @@ -32,14 +32,14 @@ For DSN management, ODBC provides the *ODBC Data Source Administrator* applicati To launch it, open the search menu - *Win + S* - and type "ODBC Data Sources (64-bit)" or "ODBC Data Sources (32-bit)" and press *Enter*: $$$launch_administrator$$$ -![launch administrator](../../../images/elasticsearch-reference-launch_administrator.png "") +![launch administrator](/explore-analyze/images/elasticsearch-reference-launch_administrator.png "") Once launched, you can verify that the driver was installed correctly by clicking on the *Drivers* tab of the ODBC Data Source Administrator and checking that *Elasticsearch Driver* is present in the list of installed drivers. You should also see the version number of the installed driver. $$$administrator_drivers$$$ -![administrator drivers](../../../images/elasticsearch-reference-administrator_drivers.png "") +![administrator drivers](/explore-analyze/images/elasticsearch-reference-administrator_drivers.png "") ## 2. Configure a DSN [dsn-configuration] @@ -71,17 +71,17 @@ The configuration steps are similar for all the above points. Following is an ex Click on the *System DSN* tab, then on the *Add…​* button: $$$system_add$$$ -![administrator system add](../../../images/elasticsearch-reference-administrator_system_add.png "") +![administrator system add](/explore-analyze/images/elasticsearch-reference-administrator_system_add.png "") A new window will open, listing all available installed drivers. Click on *{{es}} Driver*, to highlight it, then on the *Finish* button: $$$launch_editor$$$ -![administrator launch editor](../../../images/elasticsearch-reference-administrator_launch_editor.png "") +![administrator launch editor](/explore-analyze/images/elasticsearch-reference-administrator_launch_editor.png "") This action closes the previously opened second window and open a new one instead, Elasticsearch SQL ODBC Driver’s DSN Editor: $$$dsn_editor$$$ -![dsn editor basic](../../../images/elasticsearch-reference-dsn_editor_basic.png "") +![dsn editor basic](/explore-analyze/images/elasticsearch-reference-dsn_editor_basic.png "") This new window has three tabs, each responsible for a set of configuration parameters, as follows. @@ -193,7 +193,7 @@ One of the following SSL options can be chosen: If using the file browser to locate the certificate - by pressing the *Browse…​* button - only files with *.pem* and *.der* extensions will be considered by default. Choose *All Files (*.*)* from the drop down, if your file ends with a different extension: $$$dsn_editor_cert$$$ - ![dsn editor security cert](../../../images/elasticsearch-reference-dsn_editor_security_cert.png "") + ![dsn editor security cert](/explore-analyze/images/elasticsearch-reference-dsn_editor_security_cert.png "") @@ -222,7 +222,7 @@ If connecting to the {{es}} node needs to go through a proxy, the following para $$$dsn_editor_proxy$$$ -![dsn editor proxy](../../../images/elasticsearch-reference-dsn_editor_proxy.png "") +![dsn editor proxy](/explore-analyze/images/elasticsearch-reference-dsn_editor_proxy.png "") #### 2.5 Connection parameters [_2_5_connection_parameters] @@ -299,7 +299,7 @@ The connection configuration can further be tweaked by the following parameters. $$$dsn_editor_misc$$$ -![dsn editor misc](../../../images/elasticsearch-reference-dsn_editor_misc.png "") +![dsn editor misc](/explore-analyze/images/elasticsearch-reference-dsn_editor_misc.png "") #### 2.6 Logging parameters [_2_6_logging_parameters] @@ -307,7 +307,7 @@ $$$dsn_editor_misc$$$ For troubleshooting purposes, the Elasticsearch SQL ODBC Driver offers functionality to log the API calls that an application makes; this is enabled in the Administrator application: $$$administrator_tracing$$$ -![administrator tracing](../../../images/elasticsearch-reference-administrator_tracing.png "") +![administrator tracing](/explore-analyze/images/elasticsearch-reference-administrator_tracing.png "") However, this only logs the ODBC API calls made by the application into the *Driver Manager* and not those made by the *Driver Manager* into the driver itself. To enable logging of the calls that the driver receives, as well as internal driver processing events, you can enable driver’s logging on Editor’s *Logging* tab: @@ -328,7 +328,7 @@ However, this only logs the ODBC API calls made by the application into the *Dri Configure the verbosity of the logs. $$$administrator_logging$$$ - ![dsn editor logging](../../../images/elasticsearch-reference-dsn_editor_logging.png "") + ![dsn editor logging](/explore-analyze/images/elasticsearch-reference-dsn_editor_logging.png "") When authentication is enabled, the password will be redacted from the logs. @@ -344,7 +344,7 @@ Debug-logging can quickly lead to the creation of many very large files and gene Once the *Hostname*, the *Port* (if different from implicit default) and the SSL options are configured, you can test if the provided parameters are correct by pressing the *Test Connection* button. This will instruct the driver to connect to the {{es}} instance and perform a simple SQL test query. (This will thus require a running {{es}} instance with the SQL plugin enabled.) $$$dsn_editor_conntest$$$ -![dsn editor conntest](../../../images/elasticsearch-reference-dsn_editor_conntest.png "") +![dsn editor conntest](/explore-analyze/images/elasticsearch-reference-dsn_editor_conntest.png "") ::::{note} When connection testing, all the configured parameters are taken into account, including the logging configuration. This will allow early detection of potential file/directory access rights conflicts. @@ -363,7 +363,7 @@ Before saving a DSN configuration the provided file/directory paths are verified If everything is correct, the name of the newly created DSN will be listed as available to use: $$$system_added$$$ -![administrator system added](../../../images/elasticsearch-reference-administrator_system_added.png "") +![administrator system added](/explore-analyze/images/elasticsearch-reference-administrator_system_added.png "") ## Alternative logging configuration [alternative_logging] @@ -383,7 +383,7 @@ The definition of the environment variable needs to be done as follows: $$$env_var_logging$$$ -![env var log](../../../images/elasticsearch-reference-env_var_log.png "") +![env var log](/explore-analyze/images/elasticsearch-reference-env_var_log.png "") ::::{note} When enabling the logging through the environment variable, the driver will create **one log file per process**. diff --git a/explore-analyze/query-filter/languages/sql-rest-overview.md b/explore-analyze/query-filter/languages/sql-rest-overview.md index 41f500d15e..a862caab21 100644 --- a/explore-analyze/query-filter/languages/sql-rest-overview.md +++ b/explore-analyze/query-filter/languages/sql-rest-overview.md @@ -36,7 +36,7 @@ James S.A. Corey |Leviathan Wakes |561 |2011-06-02T00:00:00.000Z If you are using [Kibana Console](../tools/console.md) (which is highly recommended), take advantage of the triple quotes `"""` when creating the query. This not only automatically escapes double quotes (`"`) inside the query string but also support multi-line as shown below: -![console triple quotes](../../../images/elasticsearch-reference-console-triple-quotes.png "") +![console triple quotes](/explore-analyze/images/elasticsearch-reference-console-triple-quotes.png "") :::: diff --git a/explore-analyze/query-filter/tools/console.md b/explore-analyze/query-filter/tools/console.md index 320a1a8d01..bcb0ef18ee 100644 --- a/explore-analyze/query-filter/tools/console.md +++ b/explore-analyze/query-filter/tools/console.md @@ -18,7 +18,7 @@ $$$import-export-console-requests$$$ **Console** is an interactive UI for sending requests to [{{es}} APIs](elasticsearch://reference/elasticsearch/rest-apis/index.md) and [{{kib}} APIs](https://www.elastic.co/docs/api) and viewing their responses. -:::{image} ../../../images/kibana-console.png +:::{image} /explore-analyze/images/kibana-console.png :alt: Console :screenshot: ::: @@ -27,7 +27,7 @@ To go to **Console**, find **Dev Tools** in the navigation menu or use the [glob You can also find Console directly on certain Search solution and Elasticsearch serverless project pages, where you can expand it from the footer. This Console, called **Persistent Console**, has the same capabilities and shares the same history as the Console in **Dev Tools**. -:::{image} ../../../images/kibana-persistent-console.png +:::{image} /explore-analyze/images/kibana-persistent-console.png :alt: Console :screenshot: ::: @@ -105,7 +105,7 @@ GET /_search Click **Variables** to create, edit, and delete variables. -:::{image} ../../../images/kibana-variables.png +:::{image} /explore-analyze/images/kibana-variables.png :alt: Variables :screenshot: ::: diff --git a/explore-analyze/query-filter/tools/grok-debugger.md b/explore-analyze/query-filter/tools/grok-debugger.md index d44dcd56b8..53c17e1945 100644 --- a/explore-analyze/query-filter/tools/grok-debugger.md +++ b/explore-analyze/query-filter/tools/grok-debugger.md @@ -45,7 +45,7 @@ If you’re using {{stack-security-features}}, you must have the `manage_pipelin You’ll see the simulated event that results from applying the grok pattern. - :::{image} ../../../images/kibana-grok-debugger-overview.png + :::{image} /explore-analyze/images/kibana-grok-debugger-overview.png :alt: Grok Debugger :screenshot: ::: @@ -87,7 +87,7 @@ Follow this example to define a custom pattern. You’ll see the simulated output event that results from applying the grok pattern that contains the custom pattern: - :::{image} ../../../images/kibana-grok-debugger-custom-pattern.png + :::{image} /explore-analyze/images/kibana-grok-debugger-custom-pattern.png :alt: Debugging a custom pattern :screenshot: ::: diff --git a/explore-analyze/query-filter/tools/saved-queries.md b/explore-analyze/query-filter/tools/saved-queries.md index 86ed72b98b..a590dc4a43 100644 --- a/explore-analyze/query-filter/tools/saved-queries.md +++ b/explore-analyze/query-filter/tools/saved-queries.md @@ -21,7 +21,7 @@ If you have insufficient privileges to manage saved queries, you will be unable ## Save a query [_save_a_query] -1. Once you’ve built a query worth saving, click the save query icon ![save query icon](../../../images/kibana-saved-query-icon.png ""). +1. Once you’ve built a query worth saving, click the save query icon ![save query icon](/explore-analyze/images/kibana-saved-query-icon.png ""). 2. In the menu, select the item to save the query. 3. Enter a unique name. 4. Choose whether to include or exclude filters and a time range. By default, filters are automatically included, but the time filter is not. diff --git a/explore-analyze/query-filter/tools/search-profiler.md b/explore-analyze/query-filter/tools/search-profiler.md index 09cda9634e..e92e2bde24 100644 --- a/explore-analyze/query-filter/tools/search-profiler.md +++ b/explore-analyze/query-filter/tools/search-profiler.md @@ -21,7 +21,7 @@ Find the **{{searchprofiler}}** by navigating to the **Developer tools** page us The following example shows the results of profiling the `match_all` query. If you take a closer look at the information for the `.security_7` sample index, the **Cumulative time** field shows you that the query took 0.028ms to execute. -:::{image} ../../../images/kibana-overview.png +:::{image} /explore-analyze/images/kibana-overview.png :alt: {{searchprofiler}} visualization :screenshot: ::: @@ -45,7 +45,7 @@ To query a specific index or type, you can use the **Index** filter. In the following example, the query is executed against the indices `.security-7` and `kibana_sample_data_ecommerce`. This is equivalent making a request to `GET /test,kibana_1/_search`. -:::{image} ../../../images/kibana-filter.png +:::{image} /explore-analyze/images/kibana-filter.png :alt: Filtering by index and type :screenshot: ::: @@ -107,7 +107,7 @@ To understand how the query trees are displayed inside the **{{searchprofiler}}* 4. Click **Profile** to profile the query and visualize the results. - :::{image} ../../../images/kibana-gs8.png + :::{image} /explore-analyze/images/kibana-gs8.png :alt: Profiling the more complicated query :screenshot: ::: @@ -124,7 +124,7 @@ To understand how the query trees are displayed inside the **{{searchprofiler}}* 6. Click **View details** to view the timing breakdown. - :::{image} ../../../images/kibana-gs10.png + :::{image} /explore-analyze/images/kibana-gs10.png :alt: Drilling into the first shard's details :screenshot: ::: @@ -283,7 +283,7 @@ To see how this works, copy and paste the following profile response into the qu Your output should look similar to this: -:::{image} ../../../images/kibana-search-profiler-json.png +:::{image} /explore-analyze/images/kibana-search-profiler-json.png :alt: Rendering pre-captured profiler JSON :screenshot: ::: diff --git a/explore-analyze/report-and-share.md b/explore-analyze/report-and-share.md index ae67815872..09c3b29830 100644 --- a/explore-analyze/report-and-share.md +++ b/explore-analyze/report-and-share.md @@ -48,7 +48,7 @@ To access the object shared with the link, users need to authenticate. Anonymous users can also access the link if you have configured [Anonymous authentication](/deploy-manage/users-roles/cluster-or-deployment-auth/kibana-authentication.md#anonymous-authentication) and your anonymous service account has privileges to access what you want to share. -:::{image} ../images/share-dashboard.gif +:::{image} /explore-analyze/images/share-dashboard.gif :alt: getting a shareable link for a dashboard ::: @@ -75,7 +75,7 @@ To change the output size, change the size of the browser, which resizes the sha In the following dashboard, the shareable container is highlighted: -:::{image} ../images/kibana-shareable-container.png +:::{image} /explore-analyze/images/kibana-shareable-container.png :alt: Shareable Container :screenshot: ::: diff --git a/explore-analyze/report-and-share/reporting-troubleshooting-csv.md b/explore-analyze/report-and-share/reporting-troubleshooting-csv.md index 45afdef446..00b7a09885 100644 --- a/explore-analyze/report-and-share/reporting-troubleshooting-csv.md +++ b/explore-analyze/report-and-share/reporting-troubleshooting-csv.md @@ -78,7 +78,7 @@ The listing of reports in **Stack Management > Reporting** allows you to inspect 3. Click **Inspect query in Console** in the **Actions** menu. 4. This will open the **Console** application, pre-filled with the queries used to generate the CSV export. -:::{image} ../../images/inspect-query-from-csv-export.gif +:::{image} /explore-analyze/images/inspect-query-from-csv-export.gif :alt: Inspect the query used for CSV export :screenshot: ::: diff --git a/explore-analyze/scripting/painless-lab.md b/explore-analyze/scripting/painless-lab.md index 6b608bef97..576d375aea 100644 --- a/explore-analyze/scripting/painless-lab.md +++ b/explore-analyze/scripting/painless-lab.md @@ -16,7 +16,7 @@ The **Painless Lab** is an interactive code editor that lets you test and debug Find **Painless Lab** by navigating to the **Developer tools** page using the navigation menu or the [global search field](../../explore-analyze/find-and-organize/find-apps-and-objects.md). -:::{image} ../../images/kibana-painless-lab.png +:::{image} /explore-analyze/images/kibana-painless-lab.png :alt: Painless Lab :screenshot: ::: diff --git a/explore-analyze/transforms/ecommerce-transforms.md b/explore-analyze/transforms/ecommerce-transforms.md index ee0d6e5dea..ea9bf3e0b8 100644 --- a/explore-analyze/transforms/ecommerce-transforms.md +++ b/explore-analyze/transforms/ecommerce-transforms.md @@ -22,14 +22,14 @@ mapped_pages: For example, you might want to group the data by product ID and calculate the total number of sales for each product and its average price. Alternatively, you might want to look at the behavior of individual customers and calculate how much each customer spent in total and how many different categories of products they purchased. Or you might want to take the currencies or geographies into consideration. What are the most interesting ways you can transform and interpret this data? Go to **Management** > **Stack Management** > **Data** > **Transforms** in {{kib}} and use the wizard to create a {{transform}}: - :::{image} ../../images/elasticsearch-reference-ecommerce-pivot1.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-pivot1.png :alt: Creating a simple {{transform}} in {{kib}} :screenshot: ::: Group the data by customer ID and add one or more aggregations to learn more about each customer’s orders. For example, let’s calculate the sum of products they purchased, the total price of their purchases, the maximum number of products that they purchased in a single order, and their total number of orders. We’ll accomplish this by using the [`sum` aggregation](elasticsearch://reference/data-analysis/aggregations/search-aggregations-metrics-sum-aggregation.md) on the `total_quantity` and `taxless_total_price` fields, the [`max` aggregation](elasticsearch://reference/data-analysis/aggregations/search-aggregations-metrics-max-aggregation.md) on the `total_quantity` field, and the [`cardinality` aggregation](elasticsearch://reference/data-analysis/aggregations/search-aggregations-metrics-cardinality-aggregation.md) on the `order_id` field: - :::{image} ../../images/elasticsearch-reference-ecommerce-pivot2.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-pivot2.png :alt: Adding multiple aggregations to a {{transform}} in {{kib}} :screenshot: ::: @@ -94,13 +94,13 @@ mapped_pages: 1. Supply a {{transform}} ID, the name of the destination index and optionally a description. If the destination index does not exist, it will be created automatically when you start the {{transform}}. 2. Decide whether you want the {{transform}} to run once or continuously. Since this sample data index is unchanging, let’s use the default behavior and just run the {{transform}} once. If you want to try it out, however, go ahead and click on **Continuous mode**. You must choose a field that the {{transform}} can use to check which entities have changed. In general, it’s a good idea to use the ingest timestamp field. In this example, however, you can use the `order_date` field. 3. Optionally, you can configure a retention policy that applies to your {{transform}}. Select a date field that is used to identify old documents in the destination index and provide a maximum age. Documents that are older than the configured value are removed from the destination index. - :::{image} ../../images/elasticsearch-reference-ecommerce-pivot3.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-pivot3.png :alt: Adding transfrom ID and retention policy to a {{transform}} in {{kib}} :screenshot: ::: In {{kib}}, before you finish creating the {{transform}}, you can copy the preview {{transform}} API request to your clipboard. This information is useful later when you’re deciding whether you want to manually create the destination index. - :::{image} ../../images/elasticsearch-reference-ecommerce-pivot4.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-pivot4.png :alt: Copy the Dev Console statement of the transform preview to the clipboard :screenshot: ::: @@ -294,7 +294,7 @@ mapped_pages: :::: You can start, stop, reset, and manage {{transforms}} in {{kib}}: - :::{image} ../../images/elasticsearch-reference-manage-transforms.png + :::{image} /explore-analyze/images/elasticsearch-reference-manage-transforms.png :alt: Managing {{transforms}} in {{kib}} :screenshot: ::: @@ -316,14 +316,14 @@ mapped_pages: 7. Explore the data in your new index. For example, use the **Discover** application in {{kib}}: - :::{image} ../../images/elasticsearch-reference-ecommerce-results.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-results.png :alt: Exploring the new index in {{kib}} :screenshot: ::: 8. Optional: Create another {{transform}}, this time using the `latest` method. This method populates the destination index with the latest documents for each unique key value. For example, you might want to find the latest orders (sorted by the `order_date` field) for each customer or for each country and region. - :::{image} ../../images/elasticsearch-reference-ecommerce-latest1.png + :::{image} /explore-analyze/images/elasticsearch-reference-ecommerce-latest1.png :alt: Creating a latest {{transform}} in {{kib}} :screenshot: ::: diff --git a/explore-analyze/transforms/transform-alerts.md b/explore-analyze/transforms/transform-alerts.md index 75ce4ff7c9..67c4e09d1a 100644 --- a/explore-analyze/transforms/transform-alerts.md +++ b/explore-analyze/transforms/transform-alerts.md @@ -17,7 +17,7 @@ You can create {{transform}} rules under **{{stack-manage-app}} > {{rules-ui}}** 1. Click **Create rule** and select the {{transform}} health rule type. 2. Give a name to the rule and optionally provide tags. 3. Select the {{transform}} or {{transforms}} to include. You can also use a special character (`*`) to apply the rule to all your {{transforms}}. {{transforms-cap}} created after the rule are automatically included. - :::{image} ../../images/elasticsearch-reference-transform-check-config.png + :::{image} /explore-analyze/images/elasticsearch-reference-transform-check-config.png :alt: Selecting health check :screenshot: ::: @@ -47,7 +47,7 @@ For each action, you must choose a connector, which provides connection informat After you select a connector, you must set the action frequency. You can choose to create a summary of alerts on each check interval or on a custom interval. For example, send notifications that summarize the new, ongoing, and recovered alerts: -:::{image} ../../images/elasticsearch-reference-transform-alert-summary-actions.png +:::{image} /explore-analyze/images/elasticsearch-reference-transform-alert-summary-actions.png :alt: Setting action frequency to summary of alerts :screenshot: ::: @@ -62,7 +62,7 @@ You can further refine the conditions under which actions run by specifying that There is a set of variables that you can use to customize the notification messages for each action. Click the icon above the message text box to get the list of variables or refer to [Action variables](#transform-action-variables). -:::{image} ../../images/elasticsearch-reference-transform-alert-actions.png +:::{image} /explore-analyze/images/elasticsearch-reference-transform-alert-actions.png :alt: Selecting action variables :screenshot: ::: diff --git a/explore-analyze/transforms/transform-examples.md b/explore-analyze/transforms/transform-examples.md index 959428e218..6ddbd5f15d 100644 --- a/explore-analyze/transforms/transform-examples.md +++ b/explore-analyze/transforms/transform-examples.md @@ -22,7 +22,7 @@ These examples demonstrate how to use {{transforms}} to derive useful insights f This example uses the eCommerce orders sample data set to find the customers who spent the most in a hypothetical webshop. Let’s use the `pivot` type of {{transform}} such that the destination index contains the number of orders, the total price of the orders, the amount of unique products and the average price per order, and the total amount of ordered products for each customer. -:::{image} ../../images/elasticsearch-reference-transform-ex1-1.jpg +:::{image} /explore-analyze/images/elasticsearch-reference-transform-ex1-1.jpg :alt: Finding your best customers with {{transforms}} in {{kib}} :screenshot: ::: @@ -290,14 +290,14 @@ This example uses the web log sample data set to find the last log from an IP ad Pick the `clientip` field as the unique key; the data is grouped by this field. Select `timestamp` as the date field that sorts the data chronologically. For continuous mode, specify a date field that is used to identify new documents, and an interval between checks for changes in the source index. -:::{image} ../../images/elasticsearch-reference-transform-ex4-1.jpg +:::{image} /explore-analyze/images/elasticsearch-reference-transform-ex4-1.jpg :alt: Finding the last log event for each IP address with {{transforms}} in {{kib}} :screenshot: ::: Let’s assume that we’re interested in retaining documents only for IP addresses that appeared recently in the log. You can define a retention policy and specify a date field that is used to calculate the age of a document. This example uses the same date field that is used to sort the data. Then set the maximum age of a document; documents that are older than the value you set will be removed from the destination index. -:::{image} ../../images/elasticsearch-reference-transform-ex4-2.jpg +:::{image} /explore-analyze/images/elasticsearch-reference-transform-ex4-2.jpg :alt: Defining retention policy for {{transforms}} in {{kib}} :screenshot: ::: diff --git a/explore-analyze/transforms/transform-overview.md b/explore-analyze/transforms/transform-overview.md index 78d15a5a9b..7f6d6f89e0 100644 --- a/explore-analyze/transforms/transform-overview.md +++ b/explore-analyze/transforms/transform-overview.md @@ -40,7 +40,7 @@ Imagine that you run a webshop that sells clothes. Every order creates a documen If you want to check the sales in the different categories in your last fiscal year, define a {{transform}} that groups the data by the product categories (women’s shoes, men’s clothing, etc.) and the order date. Use the last year as the interval for the order date. Then add a sum aggregation on the ordered quantity. The result is an entity-centric index that shows the number of sold items in every product category in the last year. -:::{image} ../../images/elasticsearch-reference-pivot-preview.png +:::{image} /explore-analyze/images/elasticsearch-reference-pivot-preview.png :alt: Example of a pivot {{transform}} preview in {{kib}} :screenshot: ::: @@ -49,7 +49,7 @@ If you want to check the sales in the different categories in your last fiscal y You can use the `latest` type of {{transform}} to copy the most recent documents into a new index. You must identify one or more fields as the unique key for grouping your data, as well as a date field that sorts the data chronologically. For example, you can use this type of {{transform}} to keep track of the latest purchase for each customer or the latest event for each host. -:::{image} ../../images/elasticsearch-reference-latest-preview.png +:::{image} /explore-analyze/images/elasticsearch-reference-latest-preview.png :alt: Example of a latest {{transform}} preview in {{kib}} :screenshot: ::: diff --git a/explore-analyze/visualize/canvas/canvas-present-workpad.md b/explore-analyze/visualize/canvas/canvas-present-workpad.md index 2a26221253..7e8af079bc 100644 --- a/explore-analyze/visualize/canvas/canvas-present-workpad.md +++ b/explore-analyze/visualize/canvas/canvas-present-workpad.md @@ -35,6 +35,6 @@ Change how often the data refreshes on your workpad. 1. Click **View > Auto refresh settings**. 2. Select the interval you want to use, or **Set a custom interval**. - To manually refresh the data, click ![Canvas refresh data button](../../../images/kibana-canvas-refresh-data.png ""). + To manually refresh the data, click ![Canvas refresh data button](/explore-analyze/images/kibana-canvas-refresh-data.png ""). diff --git a/explore-analyze/visualize/canvas/edit-workpads.md b/explore-analyze/visualize/canvas/edit-workpads.md index 0a6f91a8f6..8b2ed3753e 100644 --- a/explore-analyze/visualize/canvas/edit-workpads.md +++ b/explore-analyze/visualize/canvas/edit-workpads.md @@ -32,14 +32,14 @@ For example, to change the {{data-source}} for a set of charts: 1. Specify the variable options. - :::{image} ../../../images/kibana-specify_variable_syntax.png + :::{image} /explore-analyze/images/kibana-specify_variable_syntax.png :alt: Variable syntax options :screenshot: ::: 2. Copy the variable, then apply it to each element you want to update in the **Expression editor**. - :::{image} ../../../images/kibana-copy_variable_syntax.png + :::{image} /explore-analyze/images/kibana-copy_variable_syntax.png :alt: Copied variable syntax pasted in the Expression editor :screenshot: ::: diff --git a/explore-analyze/visualize/custom-visualizations-with-vega.md b/explore-analyze/visualize/custom-visualizations-with-vega.md index 9c4557e6b7..eb44b36073 100644 --- a/explore-analyze/visualize/custom-visualizations-with-vega.md +++ b/explore-analyze/visualize/custom-visualizations-with-vega.md @@ -24,7 +24,7 @@ Use **Vega** or **Vega-Lite** when you want to create visualizations with: These grammars have some limitations: they do not support tables, and can’t run queries conditionally. -:::{image} ../../images/kibana-vega.png +:::{image} /explore-analyze/images/kibana-vega.png :alt: Vega UI :screenshot: ::: @@ -222,14 +222,14 @@ To generate the data, **Vega-Lite** uses the `source_0` and `data_0`. `source_0` 2. From the **View** dropdown, select **Vega debug**. 3. From the dropdown, select **source_0**. - :::{image} ../../images/kibana-vega_lite_tutorial_4.png + :::{image} /explore-analyze/images/kibana-vega_lite_tutorial_4.png :alt: Table for data_0 with columns key :screenshot: ::: 4. To compare to the visually encoded data, select **data_0** from the dropdown. - :::{image} ../../images/kibana-vega_lite_tutorial_5.png + :::{image} /explore-analyze/images/kibana-vega_lite_tutorial_5.png :alt: Table for data_0 where the key is NaN instead of a string :screenshot: ::: @@ -260,7 +260,7 @@ In the **Vega-Lite** spec, add the `encoding` block: 1. Click **Inspect**, then select **Vega Debug** from the **View** dropdown. 2. From the dropdown, select **data_0**. - :::{image} ../../images/kibana-vega_lite_tutorial_6.png + :::{image} /explore-analyze/images/kibana-vega_lite_tutorial_6.png :alt: Table for data_0 showing that the column time_buckets.buckets.key is undefined :screenshot: ::: @@ -286,7 +286,7 @@ In the **Vega-Lite** spec, add a `transform` block, then click **Update**: 1. Click **Inspect**, then select **Vega Debug** from the **View** dropdown. 2. From the dropdown, select **data_0**. - :::{image} ../../images/kibana-vega_lite_tutorial_7.png + :::{image} /explore-analyze/images/kibana-vega_lite_tutorial_7.png :alt: Table showing data_0 with multiple pages of results :screenshot: ::: @@ -410,7 +410,7 @@ Add a `selection` block inside `mark: point`: Move your cursor around the stacked area chart. The points are able to indicate the nearest point. -:::{image} ../../images/kibana-vega_lite_tutorial_2.png +:::{image} /explore-analyze/images/kibana-vega_lite_tutorial_2.png :alt: Vega-Lite tutorial selection enabled :screenshot: ::: @@ -662,7 +662,7 @@ Add the `key` and `doc_count` fields as the X- and Y-axis values, then click **U ] ``` -:::{image} ../../images/kibana-vega_tutorial_3.png +:::{image} /explore-analyze/images/kibana-vega_tutorial_3.png :alt: vega tutorial 3 :screenshot: ::: @@ -752,7 +752,7 @@ To make the area chart interactive, locate the `marks` block, then update the `p To allow users to filter based on a time range, add a drag interaction, which requires additional signals and a rectangle overlay. -:::{image} ../../images/kibana-vega_tutorial_4.png +:::{image} /explore-analyze/images/kibana-vega_tutorial_4.png :alt: vega tutorial 4 :screenshot: ::: @@ -1453,7 +1453,7 @@ Use the contextual **Inspect** tool to gain insights into different elements. **Vega** uses the [{{es}} search API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search) to get documents and aggregation results from {{es}}. To troubleshoot these requests, click **Inspect**, which shows the most recent requests. In case your specification has more than one request, you can switch between the views using the **View** dropdown. -:::{image} ../../images/kibana-vega_tutorial_inspect_requests.png +:::{image} /explore-analyze/images/kibana-vega_tutorial_inspect_requests.png :alt: vega tutorial inspect requests :screenshot: ::: @@ -1465,7 +1465,7 @@ With the **Vega debug** view, you can inspect the **Data sets** and **Signal Val The runtime data is read from the [runtime scope](https://vega.github.io/vega/docs/api/debugging/#scope). -:::{image} ../../images/kibana-vega_tutorial_inspect_data_sets.png +:::{image} /explore-analyze/images/kibana-vega_tutorial_inspect_data_sets.png :alt: vega tutorial inspect data sets :screenshot: ::: @@ -1477,7 +1477,7 @@ To debug more complex specs, access to the `view` variable. For more informatio Because of the dynamic nature of the data in {{es}}, it is hard to help you with **Vega** specs unless you can share a dataset. To do this, click **Inspect**, select the **Vega debug** view, then select **Spec**. -:::{image} ../../images/kibana-vega_tutorial_getting_help.png +:::{image} /explore-analyze/images/kibana-vega_tutorial_getting_help.png :alt: vega tutorial getting help :screenshot: ::: diff --git a/explore-analyze/visualize/esorql.md b/explore-analyze/visualize/esorql.md index f0801a41dd..3447c698a2 100644 --- a/explore-analyze/visualize/esorql.md +++ b/explore-analyze/visualize/esorql.md @@ -13,9 +13,9 @@ You can add ES|QL visualizations to a dashboard directly from queries in Discove ## Edit and add from Discover [_edit_and_add_from_discover] -In Discover, [typing ES|QL queries](../query-filter/languages/esql-kibana.md) automatically shows a visualization. The visualization type depends on the content of the query: histogram, bar charts, etc. You can manually make changes to that visualization and edit its type and display options using the pencil button ![pencil button](../../images/kibana-esql-icon-edit-visualization.svg ""). +In Discover, [typing ES|QL queries](../query-filter/languages/esql-kibana.md) automatically shows a visualization. The visualization type depends on the content of the query: histogram, bar charts, etc. You can manually make changes to that visualization and edit its type and display options using the pencil button ![pencil button](/explore-analyze/images/kibana-esql-icon-edit-visualization.svg ""). -You can then **Save** and add it to an existing or a new dashboard using the save button of the visualization ![save button](../../images/kibana-esql-icon-save-visualization.svg ""). +You can then **Save** and add it to an existing or a new dashboard using the save button of the visualization ![save button](/explore-analyze/images/kibana-esql-icon-save-visualization.svg ""). ## Create from dashboard [_create_from_dashboard] diff --git a/explore-analyze/visualize/graph.md b/explore-analyze/visualize/graph.md index 70d5807030..1c2a0a2d81 100644 --- a/explore-analyze/visualize/graph.md +++ b/explore-analyze/visualize/graph.md @@ -21,7 +21,7 @@ The graph API provides an alternative way to extract and summarize information a The terms you want to include in the graph are called *vertices*. The relationship between any two vertices is a *connection*. The connection summarizes the documents that contain both vertices' terms. -:::{image} ../../images/kibana-graph-vertices-connections.jpg +:::{image} /explore-analyze/images/kibana-graph-vertices-connections.jpg :alt: Graph components :screenshot: ::: @@ -54,14 +54,14 @@ Use **Graph** to reveal the relationships in your data. {{kib}} graphs the relationships between the top fields. - :::{image} ../../images/kibana-graph-url-connections.png + :::{image} /explore-analyze/images/kibana-graph-url-connections.png :alt: URL connections :screenshot: ::: 3. Add more fields, or click an existing field to edit, disable or deselect it. - :::{image} ../../images/kibana-graph-menu.png + :::{image} /explore-analyze/images/kibana-graph-menu.png :alt: menu for editing, disabling, or removing a field from the graph :screenshot: :width: 50% @@ -73,7 +73,7 @@ Use **Graph** to reveal the relationships in your data. 5. To view more information about a relationship, click any connection or vertex. - :::{image} ../../images/kibana-graph-control-bar.png + :::{image} /explore-analyze/images/kibana-graph-control-bar.png :alt: Graph toolbar :screenshot: :width: 50% @@ -81,10 +81,10 @@ Use **Graph** to reveal the relationships in your data. 6. Use the graph toolbar to display additional connections: - * To display additional vertices that connect to your graph, click the expand icon ![Expand Selection](../../images/kibana-graph-expand-button.png ""). - * To display additional connections between the displayed vertices, click the link icon ![Add links to existing terms](../../images/kibana-graph-link-button.png ""). + * To display additional vertices that connect to your graph, click the expand icon ![Expand Selection](/explore-analyze/images/kibana-graph-expand-button.png ""). + * To display additional connections between the displayed vertices, click the link icon ![Add links to existing terms](/explore-analyze/images/kibana-graph-link-button.png ""). * To explore a particular area of the graph, select the vertices you are interested in, and then click expand or link. - * To step back through your changes to the graph, click undo ![Undo](../../images/kibana-graph-undo-button.png "") and redo ![Redo](../../images/kibana-graph-redo-button.png ""). + * To step back through your changes to the graph, click undo ![Undo](/explore-analyze/images/kibana-graph-undo-button.png "") and redo ![Redo](/explore-analyze/images/kibana-graph-redo-button.png ""). 7. To view more relationships in your data, submit additional queries. 8. **Save** your graph. @@ -99,7 +99,7 @@ Apply custom colors and icons to vertices, configure the number of vertices that Each vertex has a color, icon, and label. To change the color or icon of all vertices of a certain field, click it’s field, and then select **Edit settings**. -To change the color and label of selected vertices, click the style icon ![Style](../../images/kibana-graph-style-button.png "") in the control bar. +To change the color and label of selected vertices, click the style icon ![Style](/explore-analyze/images/kibana-graph-style-button.png "") in the control bar. #### Tune the noise level [edit-graph-settings] @@ -111,17 +111,17 @@ You can configure the number of vertices that a search or expand operation adds #### Block terms from the graph [graph-block-terms] -Documents that match a blocked term are not allowed in the graph. To block a term, select its vertex and click the block icon ![Block selection](../../images/kibana-graph-block-button.png "") in the graph toolbar. For a list of blocked terms, open **Settings > Blocked terms**. +Documents that match a blocked term are not allowed in the graph. To block a term, select its vertex and click the block icon ![Block selection](/explore-analyze/images/kibana-graph-block-button.png "") in the graph toolbar. For a list of blocked terms, open **Settings > Blocked terms**. #### Drill down into raw documents [graph-drill-down] With drilldowns, you can display additional information about a selected vertex in a new browser window. For example, you might configure a drilldown URL to perform a web search for the selected vertex term. -Use the drilldown icon ![Drilldown selection](../../images/kibana-graph-info-icon.png "") in the graph toolbar to show the drilldown buttons for the selected vertices. To configure drilldowns, go to **Settings > Drilldowns**. See also [Disabling drilldown configuration](graph/graph-configuration.md#disable-drill-down). +Use the drilldown icon ![Drilldown selection](/explore-analyze/images/kibana-graph-info-icon.png "") in the graph toolbar to show the drilldown buttons for the selected vertices. To configure drilldowns, go to **Settings > Drilldowns**. See also [Disabling drilldown configuration](graph/graph-configuration.md#disable-drill-down). #### Run and pause the layout [graph-run-layout] -Graph uses a "force layout", where vertices behave like magnets, pushing off of one another. By default, when you add a new vertex to the graph, all vertices begin moving. In some cases, the movement might go on for some time. To freeze the current vertex position, click the pause icon ![Block selection](../../images/kibana-graph-pause-button.png "") in the graph toolbar. +Graph uses a "force layout", where vertices behave like magnets, pushing off of one another. By default, when you add a new vertex to the graph, all vertices begin moving. In some cases, the movement might go on for some time. To freeze the current vertex position, click the pause icon ![Block selection](/explore-analyze/images/kibana-graph-pause-button.png "") in the graph toolbar. diff --git a/explore-analyze/visualize/graph/graph-configuration.md b/explore-analyze/visualize/graph/graph-configuration.md index a5dbe22905..e81029d973 100644 --- a/explore-analyze/visualize/graph/graph-configuration.md +++ b/explore-analyze/visualize/graph/graph-configuration.md @@ -45,7 +45,7 @@ The supported save policies are: You can also use security to grant read only or all access to different roles. When security is used to grant read only access, the following indicator in Kibana is displayed. For more information on granting access to Kibana, see [Granting access to {{kib}}](../../../deploy-manage/users-roles/cluster-or-deployment-auth/built-in-roles.md). -:::{image} ../../../images/kibana-graph-read-only-badge.png +:::{image} /explore-analyze/images/kibana-graph-read-only-badge.png :alt: Example of Graph's read only access indicator in Kibana's header :screenshot: :width: 50% diff --git a/explore-analyze/visualize/legacy-editors/aggregation-based.md b/explore-analyze/visualize/legacy-editors/aggregation-based.md index 7e70f178eb..f7094858de 100644 --- a/explore-analyze/visualize/legacy-editors/aggregation-based.md +++ b/explore-analyze/visualize/legacy-editors/aggregation-based.md @@ -30,16 +30,16 @@ Aggregation-based visualizations include the following limitations: | | | | --- | --- | -| **Area**: Displays data points, connected by a line, where the area between the line and axes are shaded.Use area charts to compare two or more categories over time, and display the magnitude of trends. | ![Area chart](../../../images/kibana-area.png "") | -| **Data table**: Displays your aggregation results in a tabular format. Use data tables to display server configuration details, track counts, min,or max values for a specific field, and monitor the status of key services. | ![Data table](../../../images/kibana-data_table.png "") | -| **Gauge**: Displays your data along a scale that changes color according to where your data falls on the expected scale. Use the gauge to show how metricvalues relate to reference threshold values, or determine how a specified field is performing versus how it is expected to perform. | ![Gauge](../../../images/kibana-gauge.png "") | -| **Goal**: Displays how your metric progresses toward a fixed goal. Use the goal to display an easy to read visual of the status of your goal progression. | ![Goal](../../../images/kibana-goal.png "") | -| **Heat map**: Displays graphical representations of data where the individual values are represented by colors. Use heat maps when your data set includescategorical data. For example, use a heat map to see the flights of origin countries compared to destination countries using the sample flight data. | ![Heat map](../../../images/kibana-heat_map.png "") | -| **Horizontal Bar**: Displays bars side-by-side where each bar represents a category. Use bar charts to compare data across alarge number of categories, display data that includes categories with negative values, and easily identifythe categories that represent the highest and lowest values. {{kib}} also supports vertical bar charts. | ![Bar chart](../../../images/kibana-bar.png "") | -| **Line**: Displays data points that are connected by a line. Use line charts to visualize a sequence of values, discovertrends over time, and forecast future values. | ![Line chart](../../../images/kibana-line.png "") | -| **Metric**: Displays a single numeric value for an aggregation. Use the metric visualization when you have a numeric value that is powerful enough to tella story about your data. | ![Metric](../../../images/kibana-metric.png "") | -| **Pie**: Displays slices that represent a data category, where the slice size is proportional to the quantity it represents.Use pie charts to show comparisons between multiple categories, illustrate the dominance of one category over others,and show percentage or proportional data. | ![Pie chart](../../../images/kibana-pie.png "") | -| **Tag cloud**: Graphical representations of how frequently a word appears in the source text. Use tag clouds to easily produce a summary of large documents andcreate visual art for a specific topic. | ![Tag cloud](../../../images/kibana-tag_cloud.png "") | +| **Area**: Displays data points, connected by a line, where the area between the line and axes are shaded.Use area charts to compare two or more categories over time, and display the magnitude of trends. | ![Area chart](/explore-analyze/images/kibana-area.png "") | +| **Data table**: Displays your aggregation results in a tabular format. Use data tables to display server configuration details, track counts, min,or max values for a specific field, and monitor the status of key services. | ![Data table](/explore-analyze/images/kibana-data_table.png "") | +| **Gauge**: Displays your data along a scale that changes color according to where your data falls on the expected scale. Use the gauge to show how metricvalues relate to reference threshold values, or determine how a specified field is performing versus how it is expected to perform. | ![Gauge](/explore-analyze/images/kibana-gauge.png "") | +| **Goal**: Displays how your metric progresses toward a fixed goal. Use the goal to display an easy to read visual of the status of your goal progression. | ![Goal](/explore-analyze/images/kibana-goal.png "") | +| **Heat map**: Displays graphical representations of data where the individual values are represented by colors. Use heat maps when your data set includescategorical data. For example, use a heat map to see the flights of origin countries compared to destination countries using the sample flight data. | ![Heat map](/explore-analyze/images/kibana-heat_map.png "") | +| **Horizontal Bar**: Displays bars side-by-side where each bar represents a category. Use bar charts to compare data across alarge number of categories, display data that includes categories with negative values, and easily identifythe categories that represent the highest and lowest values. {{kib}} also supports vertical bar charts. | ![Bar chart](/explore-analyze/images/kibana-bar.png "") | +| **Line**: Displays data points that are connected by a line. Use line charts to visualize a sequence of values, discovertrends over time, and forecast future values. | ![Line chart](/explore-analyze/images/kibana-line.png "") | +| **Metric**: Displays a single numeric value for an aggregation. Use the metric visualization when you have a numeric value that is powerful enough to tella story about your data. | ![Metric](/explore-analyze/images/kibana-metric.png "") | +| **Pie**: Displays slices that represent a data category, where the slice size is proportional to the quantity it represents.Use pie charts to show comparisons between multiple categories, illustrate the dominance of one category over others,and show percentage or proportional data. | ![Pie chart](/explore-analyze/images/kibana-pie.png "") | +| **Tag cloud**: Graphical representations of how frequently a word appears in the source text. Use tag clouds to easily produce a summary of large documents andcreate visual art for a specific topic. | ![Tag cloud](/explore-analyze/images/kibana-tag_cloud.png "") | ## Create an aggregation-based visualization panel [create-aggregation-based-panel] @@ -63,11 +63,11 @@ Choose the type of visualization you want to create, then use the editor to conf 3. To change the order, drag and drop the aggregations in the editor. - ![Option to change the order of aggregations](../../../images/kibana-bar-chart-tutorial-3.png "") + ![Option to change the order of aggregations](/explore-analyze/images/kibana-bar-chart-tutorial-3.png "") 4. To customize the series colors, click the series in the legend, then select the color you want to use. - ![Color picker](../../../images/kibana-aggregation-based-color-picker.png "") + ![Color picker](/explore-analyze/images/kibana-aggregation-based-color-picker.png "") diff --git a/explore-analyze/visualize/legacy-editors/timelion.md b/explore-analyze/visualize/legacy-editors/timelion.md index f2e5ada168..f3b4180a61 100644 --- a/explore-analyze/visualize/legacy-editors/timelion.md +++ b/explore-analyze/visualize/legacy-editors/timelion.md @@ -14,7 +14,7 @@ To use **Timelion**, you define a graph by chaining functions together, using th * Perform math across two or more time series * Visualize the results -![Timelion](../../../images/kibana-timelion.png "") +![Timelion](/explore-analyze/images/kibana-timelion.png "") ## Timelion expressions [_timelion_expressions] @@ -219,7 +219,7 @@ Move the legend to the north west position with two columns, then click **Updat .legend(columns=2, position=nw) ``` -:::{image} ../../../images/kibana-timelion-customize04.png +:::{image} /explore-analyze/images/kibana-timelion-customize04.png :alt: Final time series visualization :screenshot: ::: @@ -342,7 +342,7 @@ Customize and format the visualization using the following functions, then click .legend(columns=2, position=nw) ``` -:::{image} ../../../images/kibana-timelion-math05.png +:::{image} /explore-analyze/images/kibana-timelion-math05.png :alt: Final visualization that displays inbound and outbound network traffic :screenshot: ::: @@ -527,7 +527,7 @@ Customize and format the visualization using the following functions, then click .legend(columns=4, position=nw) ``` -:::{image} ../../../images/kibana-timelion-conditional04.png +:::{image} /explore-analyze/images/kibana-timelion-conditional04.png :alt: Final visualization that displays outliers and patterns over time :screenshot: ::: diff --git a/explore-analyze/visualize/legacy-editors/tsvb.md b/explore-analyze/visualize/legacy-editors/tsvb.md index 770251a134..00fa2971fa 100644 --- a/explore-analyze/visualize/legacy-editors/tsvb.md +++ b/explore-analyze/visualize/legacy-editors/tsvb.md @@ -17,7 +17,7 @@ With **TSVB**, you can: * Use custom functions and some math on aggregations. * Customize the data with labels and colors. -:::{image} ../../../images/kibana-tsvb-screenshot.png +:::{image} /explore-analyze/images/kibana-tsvb-screenshot.png :alt: TSVB overview :screenshot: ::: @@ -232,7 +232,7 @@ Performing math across data series is unsupported in **TSVB**. To calculate the 1. Click **Clone Series**, then choose a color for the new series. - :::{image} ../../../images/kibana-tsvb_clone_series.png + :::{image} /explore-analyze/images/kibana-tsvb_clone_series.png :alt: Clone Series action :screenshot: ::: diff --git a/explore-analyze/visualize/lens.md b/explore-analyze/visualize/lens.md index 6a35ee8d7c..03947fce63 100644 --- a/explore-analyze/visualize/lens.md +++ b/explore-analyze/visualize/lens.md @@ -43,11 +43,11 @@ Choose the data you want to visualize. Edit and delete. -1. Hover over the panel and click ![Edit visualization icon](../../images/kibana-edit-visualization-icon.png "") to edit the visualization. The **Edit visualization** flyout appears. +1. Hover over the panel and click ![Edit visualization icon](/explore-analyze/images/kibana-edit-visualization-icon.png "") to edit the visualization. The **Edit visualization** flyout appears. 2. To change the aggregation **Quick function**, click the field in the flyout. -3. To delete a field, click ![Actions menu icon to delete a field](../../images/kibana-trash_can.png "") next to the field. -4. To duplicate a layer, click ![Actions menu to duplicate Lens visualization layers](../../images/kibana-vertical-actions-menu.png "") in the flyout, then select **Duplicate layer**. -5. To clear the layer configuration, click ![Actions menu to clear Lens visualization layers](../../images/kibana-vertical-actions-menu.png "") in the flyout, then select **Clear layer**. +3. To delete a field, click ![Actions menu icon to delete a field](/explore-analyze/images/kibana-trash_can.png "") next to the field. +4. To duplicate a layer, click ![Actions menu to duplicate Lens visualization layers](/explore-analyze/images/kibana-vertical-actions-menu.png "") in the flyout, then select **Duplicate layer**. +5. To clear the layer configuration, click ![Actions menu to clear Lens visualization layers](/explore-analyze/images/kibana-vertical-actions-menu.png "") in the flyout, then select **Clear layer**. 6. Click **Apply and close**. ::::{tip} @@ -119,7 +119,7 @@ To use a keyboard instead of a mouse, use the **Lens** fully accessible and cont 1. Select the field in the fields list or layer pane. Most fields have an inner and outer select state. The inner state opens a panel with detailed information or options. The outer state allows you to drag the field. Tab through the fields until you get the outer state on the field. - :::{image} ../../images/kibana-lens_drag_drop_2.png + :::{image} /explore-analyze/images/kibana-lens_drag_drop_2.png :alt: Lens drag and drop focus state :screenshot: ::: @@ -131,7 +131,7 @@ To use a keyboard instead of a mouse, use the **Lens** fully accessible and cont * To reorder the fields on the layer pane, use the Up and Down arrows. * To duplicate an action, use the Left and Right arrows, then select the **Drop a field or click to add** field you want to use. - :::{image} ../../images/kibana-lens_drag_drop_3.gif + :::{image} /explore-analyze/images/kibana-lens_drag_drop_3.gif :alt: Using drag and drop to reorder :screenshot: ::: @@ -173,7 +173,7 @@ Formulas allow you to perform math on aggregated data. The most common formulas ``` ::::{tip} - For detailed information on formulas, click ![Formula reference icon](../../images/kibana-formula_reference.png ""). + For detailed information on formulas, click ![Formula reference icon](/explore-analyze/images/kibana-formula_reference.png ""). :::: 3. To accurately display the formula, select **Percent** from the **Value format** dropdown. @@ -350,7 +350,7 @@ The following component menus are available: #### Customize a visualization legend [customize-visualization-legend] -When creating or editing a visualization, you can customize the way the legend gets displayed, and the data it displays. To do that, look for the ![Legend icon](../../images/kibana-legend-icon.svg "") icon. +When creating or editing a visualization, you can customize the way the legend gets displayed, and the data it displays. To do that, look for the ![Legend icon](/explore-analyze/images/kibana-legend-icon.svg "") icon. :::{image} ../../images/kibana-legend-popover-8.16.0.png :alt: Menu with options to customize the legend of a visualization @@ -397,7 +397,7 @@ All statistics are computed based on the selected time range and the aggregated For example, if the metric plotted in the chart is `Median(system.memory)` and the time range is **last 24 hours**, when you show the **Max** statistic in the Legend, the value that shows corresponds to the `Max[Median(system.memory)]` for the last 24 hours. -:::{image} ../../images/kibana-statistics-in-legends.png +:::{image} /explore-analyze/images/kibana-statistics-in-legends.png :alt: Additional statistics shown in the legend of a memory consumption bar chart ::: @@ -637,7 +637,7 @@ In the color palette editor, if you select **Value type: Number** the colors are The **Primary metric** refers to the large number displayed in each tile. -![Illustration of where to find the primary metric in a metric visualization.](../../images/kibana-lens_primaryMetric.png "") +![Illustration of where to find the primary metric in a metric visualization.](/explore-analyze/images/kibana-lens_primaryMetric.png "") If you select **Value type: Percent**, the primary metric values are mapped to a range between 0 and 100 percent. The bounds of the range depend on your configuration. diff --git a/explore-analyze/visualize/link-panels.md b/explore-analyze/visualize/link-panels.md index c4f49fc8c0..fa47a11dea 100644 --- a/explore-analyze/visualize/link-panels.md +++ b/explore-analyze/visualize/link-panels.md @@ -10,7 +10,7 @@ mapped_pages: You can use **Links** panels to create links to other dashboards or external websites. When creating links to other dashboards, you have the option to carry the time range, query, and filters to apply over to the linked dashboard. Links to external websites follow the [`externalUrl.policy`](kibana://reference/configuration-reference/url-drilldown-settings.md#external-url-policy) settings. **Links** panels support vertical and horizontal layouts and may be saved to the **Library** for use in other dashboards. -:::{image} ../../images/kibana-dashboard_links_panel.png +:::{image} /explore-analyze/images/kibana-dashboard_links_panel.png :alt: A screenshot displaying the new links panel :screenshot: ::: @@ -51,10 +51,10 @@ To add a previously saved links panel to another dashboard: To edit links panels: -1. Hover over the panel and click ![Edit links icon](../../images/kibana-edit-visualization-icon.png "") to edit the link. The **Edit links panel** flyout appears. -2. Click ![Edit link icon](../../images/kibana-edit-link-icon.png "") next to the link. +1. Hover over the panel and click ![Edit links icon](/explore-analyze/images/kibana-edit-visualization-icon.png "") to edit the link. The **Edit links panel** flyout appears. +2. Click ![Edit link icon](/explore-analyze/images/kibana-edit-link-icon.png "") next to the link. - :::{image} ../../images/kibana-edit-links-panel.png + :::{image} /explore-analyze/images/kibana-edit-links-panel.png :alt: A screenshot displaying the Edit icon next to the link :screenshot: ::: diff --git a/explore-analyze/visualize/maps.md b/explore-analyze/visualize/maps.md index f371c2f504..81fdeb5608 100644 --- a/explore-analyze/visualize/maps.md +++ b/explore-analyze/visualize/maps.md @@ -18,14 +18,14 @@ Create beautiful maps from your geographical data. With **Maps**, you can: * Symbolize features using data values. * Focus on only the data that’s important to you. -On Elastic Cloud Serverless, Maps is only available for Security projects [![Security](../../images/serverless-sec-badge.svg "")](../../solutions/security/elastic-security-serverless.md). +On Elastic Cloud Serverless, Maps is only available for Security projects [![Security](/explore-analyze/images/serverless-sec-badge.svg "")](../../solutions/security/elastic-security-serverless.md). ## Build maps with multiple layers and indices [_build_maps_with_multiple_layers_and_indices] Use multiple layers and indices to show all your data in a single map. Show how data sits relative to physical features like weather patterns, human-made features like international borders, and business-specific features like sales regions. Plot individual documents or use aggregations to plot any data set, no matter how large. -:::{image} ../../images/kibana-sample_data_ecommerce.png +:::{image} /explore-analyze/images/kibana-sample_data_ecommerce.png :alt: sample data ecommerce :screenshot: ::: @@ -39,7 +39,7 @@ Data comes to life with animation. Hard to detect patterns in static data pop ou This animated map uses the time slider to show Portland buses over a period of 15 minutes. The routes come alive as the bus locations update with time. -:::{image} ../../images/kibana-timeslider.gif +:::{image} /explore-analyze/images/kibana-timeslider.gif :alt: timeslider :screenshot: ::: @@ -57,7 +57,7 @@ Viewing data from different angles provides better insights. Dimensions that are This choropleth map shows the density of non-emergency service requests in San Diego by council district. The map is embedded in a dashboard, so users can better understand when services are requested and gain insight into the top requested services. -:::{image} ../../images/kibana-embed_in_dashboard.jpeg +:::{image} /explore-analyze/images/kibana-embed_in_dashboard.jpeg :alt: embed in dashboard :screenshot: ::: diff --git a/explore-analyze/visualize/maps/asset-tracking-tutorial.md b/explore-analyze/visualize/maps/asset-tracking-tutorial.md index 72a46d3073..aa9100579c 100644 --- a/explore-analyze/visualize/maps/asset-tracking-tutorial.md +++ b/explore-analyze/visualize/maps/asset-tracking-tutorial.md @@ -22,7 +22,7 @@ You’ll learn to: When you complete this tutorial, you’ll have a map that looks like this: -:::{image} ../../../images/kibana-construction_zones.png +:::{image} /explore-analyze/images/kibana-construction_zones.png :alt: construction zones :screenshot: ::: @@ -367,12 +367,12 @@ To get to the fun of visualizing and alerting on Portland public transport vehic ::::::{tab-item} Existing agent policy If you already have an agent policy, get its identifier from the `View policy` action fly out -:::{image} ../../../images/kibana-agent-policy-id.png +:::{image} /explore-analyze/images/kibana-agent-policy-id.png :alt: agent policy id :screenshot: ::: -:::{image} ../../../images/kibana-policy_id.png +:::{image} /explore-analyze/images/kibana-policy_id.png :alt: policy id :screenshot: ::: @@ -468,7 +468,7 @@ POST kbn:/api/data_views/data_view {{kib}} shows the fields in your data view. -:::{image} ../../../images/kibana-data_view.png +:::{image} /explore-analyze/images/kibana-data_view.png :alt: data view :screenshot: ::: @@ -486,7 +486,7 @@ You may want to tweak this Data View to adjust the field names and number or dat 3. Open the [time filter](../../query-filter/filtering.md), and set the time range to the last 15 minutes. 4. Expand a document and explore some of the fields that you will use later in this tutorial: `trimet.bearing`, `trimet.inCongestion`, `trimet.location`, and `trimet.vehicleID`. -:::{image} ../../../images/kibana-discover.png +:::{image} /explore-analyze/images/kibana-discover.png :alt: discover :screenshot: ::: @@ -532,7 +532,7 @@ Add a layer to show the vehicle routes for the last 15 minutes. At this point, you have a map with lines that represent the routes of the TriMet vehicles as they move around the city. -:::{image} ../../../images/kibana-tracks_layer.png +:::{image} /explore-analyze/images/kibana-tracks_layer.png :alt: tracks layer :screenshot: ::: @@ -568,7 +568,7 @@ Add a layer that uses attributes in the data to set the style and orientation of 4. Set **Border width** to 0. 5. Change **Symbol orientation** to use **By value** and the `trimet.bearing` field. - :::{image} ../../../images/kibana-top_hits_layer_style.png + :::{image} /explore-analyze/images/kibana-top_hits_layer_style.png :alt: top hits layer style :screenshot: ::: @@ -578,7 +578,7 @@ Add a layer that uses attributes in the data to set the style and orientation of Your map should automatically refresh every 10 seconds to show the latest vehicle positions and tracks. -:::{image} ../../../images/kibana-tracks_and_top_hits.png +:::{image} /explore-analyze/images/kibana-tracks_and_top_hits.png :alt: tracks and top hits :screenshot: ::: @@ -599,7 +599,7 @@ Add a layer for construction zones, which you will draw on the map. The construc 4. Click **Create index**. 5. Draw 2 or 3 construction zones on your map: - 1. In the toolbar on left side of the map, select the bounding box icon ![bounding box icon](../../../images/kibana-bounding_box_icon.png ""). + 1. In the toolbar on left side of the map, select the bounding box icon ![bounding box icon](/explore-analyze/images/kibana-bounding_box_icon.png ""). 2. To draw a construction zone, click a start point on the map and drag. 3. Click an endpoint to finish. @@ -618,7 +618,7 @@ The map now represents an operational view of live public transport traffic. Yo Your map is now complete for now, congratulations! -:::{image} ../../../images/kibana-construction_zones.png +:::{image} /explore-analyze/images/kibana-construction_zones.png :alt: construction zones :screenshot: ::: @@ -692,7 +692,7 @@ For this example, you will set the rule to check every minute. However, when run 9. Set **Check every** to **1 minute**. 10. Notify **Only on status change**. - :::{image} ../../../images/kibana-rule_configuration.png + :::{image} /explore-analyze/images/kibana-rule_configuration.png :alt: rule configuration :screenshot: ::: @@ -718,7 +718,7 @@ For this example, you will set the rule to check every minute. However, when run } ``` - :::{image} ../../../images/kibana-alert_connector.png + :::{image} /explore-analyze/images/kibana-alert_connector.png :alt: alert connector :screenshot: ::: @@ -741,7 +741,7 @@ With the alert configured and running, in a few minutes your `trimet_alerts` ind * Enable the **Label** option with the `vehicleId` field * Add the `vehicleId`, `boundaryId`, `detectionTime`, and `vehicleTime` fields to the tooltip configuration to allow viewing alert details on the map. - :::{image} ../../../images/kibana-vehicle_alerts.png + :::{image} /explore-analyze/images/kibana-vehicle_alerts.png :alt: vehicle alerts :screenshot: ::: diff --git a/explore-analyze/visualize/maps/heatmap-layer.md b/explore-analyze/visualize/maps/heatmap-layer.md index d470a9c714..8cd6612bc9 100644 --- a/explore-analyze/visualize/maps/heatmap-layer.md +++ b/explore-analyze/visualize/maps/heatmap-layer.md @@ -10,7 +10,7 @@ mapped_pages: Heat map layers cluster point data to show locations with higher densities. -:::{image} ../../../images/kibana-heatmap_layer.png +:::{image} /explore-analyze/images/kibana-heatmap_layer.png :alt: heatmap layer :screenshot: ::: diff --git a/explore-analyze/visualize/maps/import-geospatial-data.md b/explore-analyze/visualize/maps/import-geospatial-data.md index 5864cee08f..da64573aa6 100644 --- a/explore-analyze/visualize/maps/import-geospatial-data.md +++ b/explore-analyze/visualize/maps/import-geospatial-data.md @@ -96,7 +96,7 @@ To open an existing index for drawing: When feature editing is open, a feature editing toolbox is displayed on the left side of the map. -:::{image} ../../../images/kibana-drawing_layer.png +:::{image} /explore-analyze/images/kibana-drawing_layer.png :alt: drawing layer :screenshot: ::: diff --git a/explore-analyze/visualize/maps/indexing-geojson-data-tutorial.md b/explore-analyze/visualize/maps/indexing-geojson-data-tutorial.md index 3f0cacd4be..ad4d77152a 100644 --- a/explore-analyze/visualize/maps/indexing-geojson-data-tutorial.md +++ b/explore-analyze/visualize/maps/indexing-geojson-data-tutorial.md @@ -35,7 +35,7 @@ The data represents two real airports, two fictitious flight routes, and fictiti You’re adding flight paths to this area, and this sets up the map for a good view of the data. - :::{image} ../../../images/kibana-fu_gs_new_england_map.png + :::{image} /explore-analyze/images/kibana-fu_gs_new_england_map.png :alt: fu gs new england map :screenshot: ::: @@ -63,7 +63,7 @@ For each GeoJSON file you downloaded, complete the following steps: At this point, you could consider the map complete, but there are a few additions and tweaks that you can make to tell a better story with your data. - :::{image} ../../../images/kibana-fu_gs_flight_paths.png + :::{image} /explore-analyze/images/kibana-fu_gs_flight_paths.png :alt: fu gs flight paths :screenshot: ::: @@ -92,7 +92,7 @@ Looking at the `Lightning detected` layer, it’s clear where lightning has stru With your new lightning heat map layer, your map should look like this: - :::{image} ../../../images/kibana-fu_gs_lightning_intensity.png + :::{image} /explore-analyze/images/kibana-fu_gs_lightning_intensity.png :alt: fu gs lightning intensity :screenshot: ::: @@ -112,7 +112,7 @@ When you’ve finished, again be sure to [save your work ](maps-getting-started. Your final map might look like this: -:::{image} ../../../images/kibana-fu_gs_final_map.png +:::{image} /explore-analyze/images/kibana-fu_gs_final_map.png :alt: fu gs final map :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-aggregations.md b/explore-analyze/visualize/maps/maps-aggregations.md index 955c26b7b3..a58f94045f 100644 --- a/explore-analyze/visualize/maps/maps-aggregations.md +++ b/explore-analyze/visualize/maps/maps-aggregations.md @@ -31,7 +31,7 @@ Use aggregated layers with document layers to show aggregated views when the map In the following example, the Grid aggregation layer is only visible when the map is at zoom levels 0 through 5. The Documents layer is only visible when the map is at zoom levels 4 through 24. See the [Getting started](maps-getting-started.md#maps-add-elasticsearch-layer) tutorial for more details on configuring the layers. -:::{image} ../../../images/kibana-grid_to_docs.gif +:::{image} /explore-analyze/images/kibana-grid_to_docs.gif :alt: grid to docs :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-connect-to-ems.md b/explore-analyze/visualize/maps/maps-connect-to-ems.md index 47a53e4761..af6988e0f2 100644 --- a/explore-analyze/visualize/maps/maps-connect-to-ems.md +++ b/explore-analyze/visualize/maps/maps-connect-to-ems.md @@ -519,7 +519,7 @@ If you cannot connect to Elastic Maps Service from the {{kib}} server or browser Once {{hosted-ems}} is running, follow instructions from the webpage at `localhost:8080` to define a configuration file and optionally download a more detailed basemaps database. - :::{image} ../../../images/kibana-elastic-maps-server-instructions.png + :::{image} /explore-analyze/images/kibana-elastic-maps-server-instructions.png :alt: Set-up instructions :screenshot: ::: @@ -601,7 +601,7 @@ services: {{hosted-ems}} hosts vector layer boundaries and vector tile basemaps for the entire planet. Boundaries include world countries, global administrative regions, and specific country regions. Basemaps up to zoom level 8 are bundled in the Docker image. These basemaps are sufficient for maps and dashboards at the country level. To present maps with higher detail, follow the instructions of the front page to download and configure the appropriate basemaps database. The most detailed basemaps at zoom level 14 are good for street level maps, but require ~90GB of disk space. -:::{image} ../../../images/kibana-elastic-maps-server-basemaps.png +:::{image} /explore-analyze/images/kibana-elastic-maps-server-basemaps.png :alt: Basemaps download options :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-create-filter-from-map.md b/explore-analyze/visualize/maps/maps-create-filter-from-map.md index b8f6703a59..46a5f492a8 100644 --- a/explore-analyze/visualize/maps/maps-create-filter-from-map.md +++ b/explore-analyze/visualize/maps/maps-create-filter-from-map.md @@ -22,7 +22,7 @@ To filter your dashboard by your map bounds as you pan and zoom your map: 1. Go to **Dashboards**. 2. Select your dashboard from the list or click **Create dashboard**. 3. If your dashboard does not have a map, add a map panel. -4. Click the gear icon ![gear icon](../../../images/kibana-gear_icon.png "") to open the map panel menu. +4. Click the gear icon ![gear icon](/explore-analyze/images/kibana-gear_icon.png "") to open the map panel menu. 5. Select **More** to view all panel options. 6. Select **Filter dashboard by map bounds**. 7. Select the checkbox for your map panel. @@ -45,10 +45,10 @@ Spatial filters have the following properties: You can create spatial filters in two ways: -* Click the tool icon ![tool icon](../../../images/kibana-tools_icon.png ""), and then draw a shape, bounding box, or distance on the map to define the spatial filter. +* Click the tool icon ![tool icon](/explore-analyze/images/kibana-tools_icon.png ""), and then draw a shape, bounding box, or distance on the map to define the spatial filter. * Click **Filter by geometry** in a [locked tooltip](vector-tooltip.md#maps-vector-tooltip-locking), and then use the feature’s geometry for the spatial filter. -:::{image} ../../../images/kibana-create_spatial_filter.png +:::{image} /explore-analyze/images/kibana-create_spatial_filter.png :alt: create spatial filter :screenshot: ::: @@ -56,5 +56,5 @@ You can create spatial filters in two ways: ## Phrase filters [maps-phrase-filter] -A phrase filter narrows search results to documents that contain the specified text. You can create a phrase filter by clicking the plus icon ![gs plus icon](../../../images/kibana-gs_plus_icon.png "") in a [locked tooltip](vector-tooltip.md#maps-vector-tooltip-locking). If the map is a dashboard panel with drilldowns, you can apply a phrase filter to a drilldown by selecting the drilldown action. +A phrase filter narrows search results to documents that contain the specified text. You can create a phrase filter by clicking the plus icon ![gs plus icon](/explore-analyze/images/kibana-gs_plus_icon.png "") in a [locked tooltip](vector-tooltip.md#maps-vector-tooltip-locking). If the map is a dashboard panel with drilldowns, you can apply a phrase filter to a drilldown by selecting the drilldown action. diff --git a/explore-analyze/visualize/maps/maps-getting-started.md b/explore-analyze/visualize/maps/maps-getting-started.md index a67d03f044..4c10a7a205 100644 --- a/explore-analyze/visualize/maps/maps-getting-started.md +++ b/explore-analyze/visualize/maps/maps-getting-started.md @@ -19,7 +19,7 @@ You will learn to: When you complete this tutorial, you’ll have a map that looks like this: -:::{image} ../../../images/kibana-sample_data_web_logs.png +:::{image} /explore-analyze/images/kibana-sample_data_web_logs.png :alt: sample data web logs :screenshot: ::: @@ -37,7 +37,7 @@ When you complete this tutorial, you’ll have a map that looks like this: 1. Go to **Dashboards**. 2. Click **Create dashboard**. 3. Set the time range to **Last 7 days**. -4. Click the **Create new Maps** icon ![app gis icon](../../../images/kibana-app_gis_icon.png ""). +4. Click the **Create new Maps** icon ![app gis icon](/explore-analyze/images/kibana-app_gis_icon.png ""). ## Step 2. Add a choropleth layer [maps-add-choropleth-layer] @@ -73,7 +73,7 @@ The first layer you’ll add is a choropleth layer to shade world countries by w Your map now looks like this: - :::{image} ../../../images/kibana-gs_add_cloropeth_layer.png + :::{image} /explore-analyze/images/kibana-gs_add_cloropeth_layer.png :alt: Map showing the Total Requests by Destination layer :screenshot: ::: @@ -105,7 +105,7 @@ This layer displays web log documents as points. The layer is only visible when Your map will look like this from zoom level 9 to 24: - :::{image} ../../../images/kibana-gs_add_es_document_layer.png + :::{image} /explore-analyze/images/kibana-gs_add_es_document_layer.png :alt: Map showing what zoom level looks like a level 9 :screenshot: ::: @@ -140,7 +140,7 @@ You’ll create a layer for [aggregated data](../../query-filter/aggregations.md Your map will look like this between zoom levels 0 and 9: - :::{image} ../../../images/kibana-sample_data_web_logs.png + :::{image} /explore-analyze/images/kibana-sample_data_web_logs.png :alt: Map showing what zoom level 3 looks like :screenshot: ::: @@ -161,7 +161,7 @@ View your geospatial data alongside a heat map and pie chart, and then filter th 1. Click **Add from library** to open a list of panels that you can add to the dashboard. 2. Add **[Logs] Unique Destination Heatmap** and **[Logs] Bytes distribution** to the dashboard. - :::{image} ../../../images/kibana-gs_dashboard_with_map.png + :::{image} /explore-analyze/images/kibana-gs_dashboard_with_map.png :alt: Map in a dashboard with 2 other panels :screenshot: ::: @@ -171,16 +171,16 @@ View your geospatial data alongside a heat map and pie chart, and then filter th 5. Set a filter from the map: 1. Open a tooltip by clicking anywhere in the United States vector. - 2. To show only documents where **geo.src** is **US**, click the filter icon ![filter icon](../../../images/kibana-gs-filter-icon.png "")in the row for **ISO 3066-1 alpha-2**. + 2. To show only documents where **geo.src** is **US**, click the filter icon ![filter icon](/explore-analyze/images/kibana-gs-filter-icon.png "")in the row for **ISO 3066-1 alpha-2**. - :::{image} ../../../images/kibana-gs_tooltip_filter.png + :::{image} /explore-analyze/images/kibana-gs_tooltip_filter.png :alt: Tooltip on map :screenshot: ::: Your filtered map should look similar to this: - :::{image} ../../../images/kibana-gs_map_filtered.png + :::{image} /explore-analyze/images/kibana-gs_map_filtered.png :alt: Map showing filtered data :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-layer-based-filtering.md b/explore-analyze/visualize/maps/maps-layer-based-filtering.md index 282f1fa5da..3abb148111 100644 --- a/explore-analyze/visualize/maps/maps-layer-based-filtering.md +++ b/explore-analyze/visualize/maps/maps-layer-based-filtering.md @@ -15,7 +15,7 @@ Layer filters are not applied to the right side of **term joins**. You can apply :::: -:::{image} ../../../images/kibana-layer_search.png +:::{image} /explore-analyze/images/kibana-layer_search.png :alt: layer search :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md b/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md index 6b181616c8..cb88fed893 100644 --- a/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md +++ b/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md @@ -32,7 +32,7 @@ machine.os.keyword : "osx" the `kibana_sample_data_flights` layer is empty because the index `kibana_sample_data_flights` does not contain the field `machine.os.keyword` and no documents match the query. -:::{image} ../../../images/kibana-global_search_multiple_indices_query1.png +:::{image} /explore-analyze/images/kibana-global_search_multiple_indices_query1.png :alt: global search multiple indices query1 :screenshot: ::: @@ -45,7 +45,7 @@ machine.os.keyword : "osx" or _index : "kibana_sample_data_flights" the `kibana_sample_data_flights` layer includes data. -:::{image} ../../../images/kibana-global_search_multiple_indices_query2.png +:::{image} /explore-analyze/images/kibana-global_search_multiple_indices_query2.png :alt: global search multiple indices query2 :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-search.md b/explore-analyze/visualize/maps/maps-search.md index 84effd1047..21e9faf756 100644 --- a/explore-analyze/visualize/maps/maps-search.md +++ b/explore-analyze/visualize/maps/maps-search.md @@ -12,7 +12,7 @@ Search across the layers in your map to focus on just the data you want. Combine This image shows an example of global search and global time narrowing results. -:::{image} ../../../images/kibana-global_search_bar.png +:::{image} /explore-analyze/images/kibana-global_search_bar.png :alt: global search and global time narrowing results :screenshot: ::: @@ -30,7 +30,7 @@ Only layers requesting data from {{es}} are narrowed by global search and global ## Narrow layers by global search [maps-narrow-layer-by-global-search] -Layers that request data from {{es}} are narrowed when you submit a search. Layers narrowed by semi-structured search and filters contain the filter icon ![filter icon](../../../images/kibana-filter_icon.png "") next to the layer name in the legend. +Layers that request data from {{es}} are narrowed when you submit a search. Layers narrowed by semi-structured search and filters contain the filter icon ![filter icon](/explore-analyze/images/kibana-filter_icon.png "") next to the layer name in the legend. To prevent the global search from applying to a layer, configure the following: @@ -40,11 +40,11 @@ To prevent the global search from applying to a layer, configure the following: ## Narrow layers by global time [maps-narrow-layer-by-global-time] -Layers that request data from {{es}} using a [data view](../../find-and-organize/data-views.md) with a configured time field are narrowed by the [global time](../../query-filter/filtering.md). These layers contain the clock icon ![clock icon](../../../images/kibana-clock_icon.png "") next to the layer name in the legend. +Layers that request data from {{es}} using a [data view](../../find-and-organize/data-views.md) with a configured time field are narrowed by the [global time](../../query-filter/filtering.md). These layers contain the clock icon ![clock icon](/explore-analyze/images/kibana-clock_icon.png "") next to the layer name in the legend. Use the time slider to quickly select time slices within the global time range: -1. Click ![timeslider icon](../../../images/kibana-timeslider_toggle_icon.png "") to open the time slider. +1. Click ![timeslider icon](/explore-analyze/images/kibana-timeslider_toggle_icon.png "") to open the time slider. 2. Click previous and next buttons to advance the time slice backward or forward. 3. Click play to animate your spatial temporal data. diff --git a/explore-analyze/visualize/maps/maps-settings.md b/explore-analyze/visualize/maps/maps-settings.md index b7449efc2d..57df8c9d1c 100644 --- a/explore-analyze/visualize/maps/maps-settings.md +++ b/explore-analyze/visualize/maps/maps-settings.md @@ -47,7 +47,7 @@ Add, edit, or delete custom icons for the map. Icons added to the map can be use Use spatial filter settings to configure how [spatial filters](maps-create-filter-from-map.md#maps-spatial-filters) are displayed. -:::{image} ../../../images/kibana-spatial_filters.png +:::{image} /explore-analyze/images/kibana-spatial_filters.png :alt: spatial filters ::: diff --git a/explore-analyze/visualize/maps/maps-top-hits-aggregation.md b/explore-analyze/visualize/maps/maps-top-hits-aggregation.md index 506b385419..05ba4f80e6 100644 --- a/explore-analyze/visualize/maps/maps-top-hits-aggregation.md +++ b/explore-analyze/visualize/maps/maps-top-hits-aggregation.md @@ -17,7 +17,7 @@ To enable top hits: 3. Set **Entity** to the field that identifies entities in your documents. This field will be used in the terms aggregation to group your documents into entity buckets. 4. Set **Documents per entity** to configure the maximum number of documents accumulated per entity. This setting is limited to the `index.max_inner_result_window` index setting, which defaults to 100. -:::{image} ../../../images/kibana-top_hits.png +:::{image} /explore-analyze/images/kibana-top_hits.png :alt: top hits :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-troubleshooting.md b/explore-analyze/visualize/maps/maps-troubleshooting.md index 58a5f5484b..67b3f5aa2e 100644 --- a/explore-analyze/visualize/maps/maps-troubleshooting.md +++ b/explore-analyze/visualize/maps/maps-troubleshooting.md @@ -19,12 +19,12 @@ Use the information in this section to inspect Elasticsearch requests and find s Maps uses the [{{es}} vector tile search API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search-mvt) and the [{{es}} search API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search) to get documents and aggregation results from {{es}}. Use **Vector tiles** inspector to view {{es}} vector tile search API requests. Use **Requests** inspector to view {{es}} search API requests. -:::{image} ../../../images/kibana-vector_tile_inspector.png +:::{image} /explore-analyze/images/kibana-vector_tile_inspector.png :alt: vector tile inspector :screenshot: ::: -:::{image} ../../../images/kibana-requests_inspector.png +:::{image} /explore-analyze/images/kibana-requests_inspector.png :alt: requests inspector :screenshot: ::: diff --git a/explore-analyze/visualize/maps/maps-vector-style-properties.md b/explore-analyze/visualize/maps/maps-vector-style-properties.md index 4fc226901f..e14c3027d4 100644 --- a/explore-analyze/visualize/maps/maps-vector-style-properties.md +++ b/explore-analyze/visualize/maps/maps-vector-style-properties.md @@ -48,7 +48,7 @@ Use **Icon** to symbolize Points as icons. Available icons -:::{image} ../../../images/kibana-maki-icons.png +:::{image} /explore-analyze/images/kibana-maki-icons.png :alt: maki icons :screenshot: ::: diff --git a/explore-analyze/visualize/maps/point-to-point.md b/explore-analyze/visualize/maps/point-to-point.md index 7806015b20..e5a505a495 100644 --- a/explore-analyze/visualize/maps/point-to-point.md +++ b/explore-analyze/visualize/maps/point-to-point.md @@ -19,7 +19,7 @@ Point-to-point layers are used in several common use cases: * Origin-destination flows for import/export/migration * Origin-destination for pick-up/drop-off data -:::{image} ../../../images/kibana-point_to_point.png +:::{image} /explore-analyze/images/kibana-point_to_point.png :alt: point to point ::: diff --git a/explore-analyze/visualize/maps/reverse-geocoding-tutorial.md b/explore-analyze/visualize/maps/reverse-geocoding-tutorial.md index 1085d6b736..9b099f8e17 100644 --- a/explore-analyze/visualize/maps/reverse-geocoding-tutorial.md +++ b/explore-analyze/visualize/maps/reverse-geocoding-tutorial.md @@ -22,7 +22,7 @@ You’ll learn to: When you complete this tutorial, you’ll have a map that looks like this: -:::{image} ../../../images/kibana-csa_regions_by_web_traffic.png +:::{image} /explore-analyze/images/kibana-csa_regions_by_web_traffic.png :alt: Map showing custom regions :screenshot: ::: @@ -67,7 +67,7 @@ To get the CSA boundary data: Looking at the map, you get a sense of what constitutes a metro area in the eyes of the Census Bureau. -:::{image} ../../../images/kibana-csa_regions.png +:::{image} /explore-analyze/images/kibana-csa_regions.png :alt: Map showing metro area :screenshot: ::: @@ -147,12 +147,12 @@ To visualize CSA regions by web log traffic, the web log traffic must contain a 8. Set the data view to **Kibana Sample Data Logs**. 9. Open the [time filter](../../query-filter/filtering.md), and set the time range to the last 30 days. 10. Scan through the list of **Available fields** until you find the `csa.GEOID` field. You can also search for the field by name. -11. Click ![Add icon](../../../images/kibana-add-icon.png "") to toggle the field into the document table. +11. Click ![Add icon](/explore-analyze/images/kibana-add-icon.png "") to toggle the field into the document table. 12. Find the *csa.NAME* field and add it to your document table. Your web log data now contains `csa.GEOID` and `csa.NAME` fields from the matching **csa** region. Web log traffic not contained in a CSA region does not have values for `csa.GEOID` and `csa.NAME` fields. -:::{image} ../../../images/kibana-discover_enriched_web_log.png +:::{image} /explore-analyze/images/kibana-discover_enriched_web_log.png :alt: View of data in Discover :screenshot: ::: @@ -187,7 +187,7 @@ Now that our web traffic contains CSA region identifiers, you’ll visualize CSA 3. Click **Save and add to library**. -:::{image} ../../../images/kibana-csa_regions_by_web_traffic.png +:::{image} /explore-analyze/images/kibana-csa_regions_by_web_traffic.png :alt: Final map showing custom regions :screenshot: ::: diff --git a/explore-analyze/visualize/maps/terms-join.md b/explore-analyze/visualize/maps/terms-join.md index 369d64c9af..188f5ac56a 100644 --- a/explore-analyze/visualize/maps/terms-join.md +++ b/explore-analyze/visualize/maps/terms-join.md @@ -20,7 +20,7 @@ Term joins are available for the following [vector layers](vector-layer.md): The [choropleth layer example](maps-getting-started.md#maps-add-choropleth-layer) uses a term join to shade world countries by web log traffic. Darker shades symbolize countries with more web log traffic, and lighter shades symbolize countries with less traffic. -:::{image} ../../../images/kibana-gs_add_cloropeth_layer.png +:::{image} /explore-analyze/images/kibana-gs_add_cloropeth_layer.png :alt: gs add cloropeth layer :screenshot: ::: @@ -31,7 +31,7 @@ A term join uses a shared key to combine vector features, the left source, with The cloropeth example uses the shared key, [ISO 3166-1 alpha-2 code](https://wikipedia.org/wiki/ISO_3166-1_alpha-2), to join world countries and web log traffic. ISO 3166-1 alpha-2 code is an international standard that identifies countries by a two-letter country code. For example, **Sweden** has an ISO 3166-1 alpha-2 code of **SE**. -:::{image} ../../../images/kibana-terms_join_shared_key_config.png +:::{image} /explore-analyze/images/kibana-terms_join_shared_key_config.png :alt: terms join shared key config :screenshot: ::: @@ -69,7 +69,7 @@ The METRICS configuration defines two metric aggregations: * The count of all documents in the terms bucket. * The average of the field "bytes" for all documents in the terms bucket. -:::{image} ../../../images/kibana-terms_join_metric_config.png +:::{image} /explore-analyze/images/kibana-terms_join_metric_config.png :alt: terms join metric config :screenshot: ::: diff --git a/explore-analyze/visualize/maps/tile-layer.md b/explore-analyze/visualize/maps/tile-layer.md index 24b54dc329..706e28c418 100644 --- a/explore-analyze/visualize/maps/tile-layer.md +++ b/explore-analyze/visualize/maps/tile-layer.md @@ -10,7 +10,7 @@ mapped_pages: Tile layers display image tiles served from a tile server. -:::{image} ../../../images/kibana-tile_layer.png +:::{image} /explore-analyze/images/kibana-tile_layer.png :alt: tile layer :screenshot: ::: diff --git a/explore-analyze/visualize/maps/vector-layer.md b/explore-analyze/visualize/maps/vector-layer.md index 0e43a8758b..ab504b17a7 100644 --- a/explore-analyze/visualize/maps/vector-layer.md +++ b/explore-analyze/visualize/maps/vector-layer.md @@ -10,7 +10,7 @@ mapped_pages: Vector layers display points, lines, and polygons. -:::{image} ../../../images/kibana-vector_layer.png +:::{image} /explore-analyze/images/kibana-vector_layer.png :alt: vector layer :screenshot: ::: diff --git a/explore-analyze/visualize/maps/vector-style.md b/explore-analyze/visualize/maps/vector-style.md index 0d2daf51c6..34fea9dd25 100644 --- a/explore-analyze/visualize/maps/vector-style.md +++ b/explore-analyze/visualize/maps/vector-style.md @@ -17,7 +17,7 @@ Use static styling to specify a constant value for a style property. This image shows an example of static styling using the [Kibana sample web logs](/explore-analyze/index.md) data set. The **kibana_sample_data_logs** layer uses static styling for all properties. -:::{image} ../../../images/kibana-vector_style_static.png +:::{image} /explore-analyze/images/kibana-vector_style_static.png :alt: vector style static :screenshot: ::: @@ -32,7 +32,7 @@ This image shows an example of data driven styling using the [Kibana sample web * The `hour_of_day` property determines the fill color for each feature based on where the value fits on a linear scale. Light green circles symbolize documents that occur earlier in the day, and dark green circles symbolize documents that occur later in the day. * The `bytes` property determines the size of each symbol based on where the value fits on a linear scale. Smaller circles symbolize documents with smaller payloads, and larger circles symbolize documents with larger payloads. -:::{image} ../../../images/kibana-vector_style_dynamic.png +:::{image} /explore-analyze/images/kibana-vector_style_dynamic.png :alt: vector style dynamic :screenshot: ::: @@ -76,7 +76,7 @@ Qualitative data driven styling is available for the following styling propertie This image shows an example of quantitative data driven styling using the [Kibana sample web logs](/explore-analyze/index.md) data set. The `machine.os.keyword` property determines the color of each symbol based on category. -:::{image} ../../../images/kibana-quantitative_data_driven_styling.png +:::{image} /explore-analyze/images/kibana-quantitative_data_driven_styling.png :alt: quantitative data driven styling :screenshot: ::: @@ -91,7 +91,7 @@ This image shows an example of class styling using the [Kibana sample web logs]( * The **Mac OS requests** layer applies the filter `machine.os : osx` so the layer only contains Mac OS requests. The fill color is a static value of green. * The **Window OS requests** layer applies the filter `machine.os : win*` so the layer only contains Window OS requests. The fill color is a static value of red. -:::{image} ../../../images/kibana-vector_style_class.png +:::{image} /explore-analyze/images/kibana-vector_style_class.png :alt: vector style class :screenshot: ::: diff --git a/explore-analyze/visualize/maps/vector-tooltip.md b/explore-analyze/visualize/maps/vector-tooltip.md index e0426e6570..49fbbac728 100644 --- a/explore-analyze/visualize/maps/vector-tooltip.md +++ b/explore-analyze/visualize/maps/vector-tooltip.md @@ -12,7 +12,7 @@ Vector tooltips display attributes for the features at your mouse location. Thes If more than one feature exists at a location, the tooltip displays the attributes for the top feature, and notes the number of features at that location. The following image has a tooltip with three features at the current location: a green circle from the **Total Sales Revenue** layer, a blue New York State polygon from **United States** layer, and a red United States Country polygon from the **World Countries** layer. The tooltip displays attributes for the top feature, the green circle, from the **Total Sales Revenue** layer. -:::{image} ../../../images/kibana-multifeature_tooltip.png +:::{image} /explore-analyze/images/kibana-multifeature_tooltip.png :alt: multifeature tooltip :screenshot: ::: @@ -33,7 +33,7 @@ You can lock a tooltip in place by clicking a location on the map. With locked t This image shows a locked tooltip with features from three layers. The tooltip displays attributes for the second feature, the blue New York State polygon. The tooltip includes controls for paging through the features and a dropdown menu for filtering the features by layer. -:::{image} ../../../images/kibana-locked_tooltip.png +:::{image} /explore-analyze/images/kibana-locked_tooltip.png :alt: locked tooltip :screenshot: ::: diff --git a/explore-analyze/visualize/text-panels.md b/explore-analyze/visualize/text-panels.md index 21dc0d0fa8..9977604a46 100644 --- a/explore-analyze/visualize/text-panels.md +++ b/explore-analyze/visualize/text-panels.md @@ -16,28 +16,28 @@ To provide context to your dashboard panels, add **Text** panels that display im For example, when you enter: -:::{image} ../../images/kibana-markdown_example_1.png +:::{image} /explore-analyze/images/kibana-markdown_example_1.png :alt: Markdown text with links :screenshot: ::: The following instructions are displayed: -:::{image} ../../images/kibana-markdown_example_2.png +:::{image} /explore-analyze/images/kibana-markdown_example_2.png :alt: Panel with markdown link text :screenshot: ::: Or when you enter: -:::{image} ../../images/kibana-markdown_example_3.png +:::{image} /explore-analyze/images/kibana-markdown_example_3.png :alt: Markdown text with image file :screenshot: ::: The following image is displayed: -:::{image} ../../images/kibana-markdown_example_4.png +:::{image} /explore-analyze/images/kibana-markdown_example_4.png :alt: Panel with markdown image :screenshot: :::