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Copy file name to clipboardExpand all lines: deploy-manage/cloud-organization/billing/billing-models.md
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@@ -71,13 +71,13 @@ Based on these four key concepts, the prepaid consumption lifecycle is as follow
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1. You purchase credits expressed in ECU, typically at a discount.
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2. You begin using {{ecloud}} resources.
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3. At every billing cycle (which takes place on the first of each month), the previous month's usage, expressed in ECU, is deducted from your ECU balance.
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4. If your ECU balance is depleted before the credit expiration date, you are invoiced for on-demand usage in arrears at list price. On-demand usage is expressed in ECU, and is converted to currency amounts for invoicing purposes.¹
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4. If your ECU balance is depleted before the credit expiration date, you are invoiced for on-demand usage in arrears at list price. On-demand usage is expressed in ECU, and is converted to currency amounts for invoicing purposes.[^1^](#footnote-1)
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5. At the end of the contract period, any credits remaining in your balance are forfeited.
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6. During the contract period, you can purchase additional credits at any time (as an add-on). This can be done with the same discount as the original purchase. Credits purchased through an add-on have the same expiration as the original purchase.²
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6. During the contract period, you can purchase additional credits at any time (as an add-on). This can be done with the same discount as the original purchase. Credits purchased through an add-on have the same expiration as the original purchase.[^2^](#footnote-2)
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¹ When you renew your contract or commit to a multi-year contract, any on-demand usage incurred in the years other than the last are billed with the same discount as the original purchase.
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^1^ $$$footnote-1$$$ When you renew your contract or commit to a multi-year contract, any on-demand usage incurred in the years other than the last are billed with the same discount as the original purchase.
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² Purchasing credits through early renewals, or through add-ons with different expiration dates will be available in the near future.
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^2^ $$$footnote-2$$$ Purchasing credits through early renewals, or through add-ons with different expiration dates will be available in the near future.
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::::{note}
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Existing annual+overages customers will be able to switch to prepaid consumption when they renew or sign a new contract. Existing manual burndown customers will be migrated gradually to prepaid consumption in the near future. Exceptions apply.
Copy file name to clipboardExpand all lines: deploy-manage/security/encrypt-deployment-with-customer-managed-encryption-key.md
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@@ -24,12 +24,12 @@ Using a customer-managed key allows you to strengthen the security of your deplo
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Using a customer-managed key helps protect against threats related to the management and control of encryption keys. It does not directly protect against any specific types of attacks or threats. However, the ability to keep control over your own keys can help mitigate certain types of threats such as:
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***Insider threats.** By using a customer-managed key, Elastic does not have access to your encryption keys [1]. This can help prevent unauthorized access to data by insiders with malicious intent.
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***Insider threats.** By using a customer-managed key, Elastic does not have access to your encryption keys [^1^](#footnote-1). This can help prevent unauthorized access to data by insiders with malicious intent.
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***Compromised physical infrastructure.** If a data center is physically compromised, the hosts are shut off. With customer-managed key encryption, that’s a second layer of protection that any malicious intruder would have to bypass, in addition to the existing built-in hardware encryption.
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Using a customer-managed key can help comply with regulations or security requirements, but it is not a complete security solution by itself. There are other types of threats that it does not protect against.
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[1] You set up your customer-managed keys and their access in your key management service. When you provide a customer-managed key identifier to {{ecloud}}, we do not access or store the cryptographic material associated with that key. Customer-managed keys are not directly used to encrypt deployment or snapshot data. {{ecloud}} accesses your customer-managed keys to encrypt and decrypt data encryption keys, which, in turn, are used to encrypt the data.
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^1^ $$$footnote-1$$$ You set up your customer-managed keys and their access in your key management service. When you provide a customer-managed key identifier to {{ecloud}}, we do not access or store the cryptographic material associated with that key. Customer-managed keys are not directly used to encrypt deployment or snapshot data. {{ecloud}} accesses your customer-managed keys to encrypt and decrypt data encryption keys, which, in turn, are used to encrypt the data.
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When a deployment encrypted with a customer-managed key is deleted or terminated, its data is locked first before being deleted, ensuring a fully secure deletion process.
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@@ -427,15 +427,15 @@ When {{ecloud}} can’t reach the encryption key, your deployment may become ina
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Within 30 minutes maximum, {{ecloud}} locks the directories in which your deployment data live and prompts you to delete your deployment as an increased security measure.<br>
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While it is locked, the deployment retains all data but is not readable or writable[¹](#footnote-1):
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While it is locked, the deployment retains all data but is not readable or writable[^2^](#footnote-2):
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* If access to the key is never restored, the deployment data does not become accessible again
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* When restoring access to the key, the deployment becomes operational again:
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* If Elastic didn’t have to perform any platform operations on your instances during the locked period, operations are restored with minimum downtime.
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* If Elastic performed some platform operations on your instances during the locked period, restoring operations can require some downtime. It’s also possible that some data can’t be restored[²](#footnote-1) depending on the available snapshots.
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* If Elastic performed some platform operations on your instances during the locked period, restoring operations can require some downtime. It’s also possible that some data can’t be restored[^3^](#footnote-3) depending on the available snapshots.
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¹ $$$footnote-1$$$ During the locked directory period, Elastic may need to perform platform operations on the machines hosting your instances that result in data loss on the {{es}} data nodes but not the deployment snapshots.
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^2^ $$$footnote-2$$$ During the locked directory period, Elastic may need to perform platform operations on the machines hosting your instances that result in data loss on the {{es}} data nodes but not the deployment snapshots.
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² $$$footnote-2$$$ Elastic recommends that you keep snapshots of your deployment in custom snapshot repositories in your own CSP account for data recovery purposes.
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^3^ $$$footnote-3$$$ Elastic recommends that you keep snapshots of your deployment in custom snapshot repositories in your own CSP account for data recovery purposes.
Copy file name to clipboardExpand all lines: explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md
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@@ -78,7 +78,7 @@ The {{regression}} evaluation type offers the following metrics to evaluate the
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* Mean squared error (MSE)
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* Mean squared logarithmic error (MSLE)
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* Pseudo-Huber loss
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* R-squared (R²)
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* R-squared (R^2^)
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### Mean squared error [ml-dfanalytics-mse]
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@@ -94,7 +94,7 @@ MSLE is a variation of mean squared error. It can be used for cases when the tar
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### R-squared [ml-dfanalytics-r-squared]
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R-squared (R²) represents the goodness of fit and measures how much of the variation in the data the predictions are able to explain. The value of R² are less than or equal to 1, where 1 indicates that the predictions and true values are equal. A value of 0 is obtained when all the predictions are set to the mean of the true values. A value of 0.5 for R² would indicate that the predictions are 1 - 0.5 ^(1/2) (about 30%) closer to true values than their mean.
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R-squared (R^2^) represents the goodness of fit and measures how much of the variation in the data the predictions are able to explain. The value of R^2^ are less than or equal to 1, where 1 indicates that the predictions and true values are equal. A value of 0 is obtained when all the predictions are set to the mean of the true values. A value of 0.5 for R^2^ would indicate that the predictions are 1 - 0.5 ^(1/2) (about 30%) closer to true values than their mean.
: Log files for {{agent}} and {{beats}} shippers[¹](#footnote-1)
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: Log files for {{agent}} and {{beats}} shippers[^1^](#footnote-1)
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You can install {{agent}} in a custom base path other than `C:\Program Files`. When installing {{agent}} with the `.\elastic-agent.exe install` command, use the `--base-path` CLI option to specify the custom base path.
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: Main {{agent}} {{fleet}} encrypted configuration
: Log files for {{agent}} and {{beats}} shippers[¹](#footnote-1)
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: Log files for {{agent}} and {{beats}} shippers[^1^](#footnote-1)
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`/usr/bin/elastic-agent`
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: Shell wrapper installed into PATH
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::::::
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:::::::
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¹$$$footnote-1$$$ Logs file names end with a date `(YYYYMMDD)` and optional number: `elastic-agent-YYYYMMDD.ndjson`, `elastic-agent-YYYYMMDD-1.ndjson`, and so on as new files are created during rotation.
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^1^$$$footnote-1$$$ Logs file names end with a date `(YYYYMMDD)` and optional number: `elastic-agent-YYYYMMDD.ndjson`, `elastic-agent-YYYYMMDD-1.ndjson`, and so on as new files are created during rotation.
: Log files for {{agent}} and {{beats}} shippers[¹](#footnote-1)
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: Log files for {{agent}} and {{beats}} shippers[^1^](#footnote-1)
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You can install {{agent}} in a custom base path other than `C:\Program Files`. When installing {{agent}} with the `.\elastic-agent.exe install` command, use the `--base-path` CLI option to specify the custom base path.
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: Main {{agent}} {{fleet}} encrypted configuration
: Log files for {{agent}} and {{beats}} shippers[¹](#footnote-1)
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: Log files for {{agent}} and {{beats}} shippers[^1^](#footnote-1)
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`/usr/bin/elastic-agent`
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: Shell wrapper installed into PATH
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:::::::
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$$$footnote-1$$$
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¹ Logs file names end with a date (`YYYYMMDD`) and optional number: `elastic-agent-YYYYMMDD.ndjson`, `elastic-agent-YYYYMMDD-1.ndjson`, and so on as new files are created during rotation.
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^1^ $$$footnote-1$$$ Logs file names end with a date (`YYYYMMDD`) and optional number: `elastic-agent-YYYYMMDD.ndjson`, `elastic-agent-YYYYMMDD-1.ndjson`, and so on as new files are created during rotation.
Copy file name to clipboardExpand all lines: solutions/observability/apm/transaction-sampling.md
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@@ -194,7 +194,7 @@ stack:
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serverless:
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```
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A sampled trace retains all data associated with it. A non-sampled trace drops all [span](/solutions/observability/apm/spans.md) and [transaction](/solutions/observability/apm/transactions.md) data.[¹](#footnote-1) Regardless of the sampling decision, all traces retain [error](/solutions/observability/apm/errors.md) data.
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A sampled trace retains all data associated with it. A non-sampled trace drops all [span](/solutions/observability/apm/spans.md) and [transaction](/solutions/observability/apm/transactions.md) data.[^1^](#footnote-1) Regardless of the sampling decision, all traces retain [error](/solutions/observability/apm/errors.md) data.
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Some visualizations in the {{apm-app}}, like latency, are powered by aggregated transaction and span [metrics](/solutions/observability/apm/metrics.md). The way these metrics are calculated depends on the sampling method used:
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These calculation methods ensure that the APM app provides the most accurate metrics possible given the sampling strategy in use, while also accounting for the head-based sampling rate to estimate the full population of traces.
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¹$$$footnote-1$$$ Real User Monitoring (RUM) traces are an exception to this rule. The {{kib}} apps that utilize RUM data depend on transaction events, so non-sampled RUM traces retain transaction data — only span data is dropped.
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^1^$$$footnote-1$$$ Real User Monitoring (RUM) traces are an exception to this rule. The {{kib}} apps that utilize RUM data depend on transaction events, so non-sampled RUM traces retain transaction data — only span data is dropped.
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