Added feedback from PM, put dimensions list in a table

Author: johannahp-splunk

apm/span-tags/metricsets.rst

@@ -14,35 +14,82 @@ MetricSets are key performance indicators, like request rate, error rate, and re
 Monitoring MetricSets
 =====================
 
-Monitoring MetricSets (MMS) are metric time series (MTS) that power the real-time monitoring capabilities in Splunk APM, including charts and dashboards. MMS power the APM landing page and the dashboard view. MMS are also the metrics that detectors monitor to generate alerts. 
+Monitoring MetricSets (MMS) are metric time series (MTS) that power the monitoring capabilities in Splunk APM, including charts and dashboards. MMS power the APM landing page and the dashboard view. MMS are also the metrics that detectors monitor to generate alerts. 
 
 MMS are available for a specific endpoint or for the aggregate of all endpoints in a service.
 
-Endpoint-level MMS reflect the activity of a single endpoint in a service, while service-level MMS aggregate the activity of all of the endpoints in the service. MMS are limited to spans where the ``span.kind`` has a value of ``SERVER`` or ``CONSUMER``.
+Endpoint-level MMS reflect the activity of a single endpoint in a service, while service-level MMS aggregate the activity of all of the endpoints in the service. MMS are created for spans where the ``span.kind`` has a value of ``SERVER`` or ``CONSUMER``.
 
 Spans might lack a ``kind`` value, or have a different ``kind`` value, in the following situations:
 
 * The span originates in self-initiating operations or inferred services
 * An error in instrumentation occurs.
 
-In addition to the following default MMS, you can create custom MMS. See :ref:`cmms`.
-
 .. _default-mms:
 
 Available default MMS metrics and dimensions
 -----------------------------------------------
 
-MMS are available for the following APM components:
+MMS are available for the APM components listed in the following table. Each MMS also has a set of dimensions you can use to monitor and alert on service performance. In addition to the following default MMS, you can create custom MMS to deep dive on your MMS. See :ref:`cmms`.
+
+.. _service-mms: 
+.. _inferred-service-mms-dimensions:
 
-- service.request
-- spans 
-- inferred.services
-- traces
-- workflows (Workflow metrics are created by default when you create a Business Workflow. Custom MMS are not available for Business Workflows.)
 
-Monitoring MetricSets in APM are generated as histogram metrics. Previously, MMS were classified as either a counter or gauge metric type. Histogram metrics represent a distribution of measurements or metrics, with complete percentile data available. Data is distributed into equally sized intervals, allowing you to compute percentiles across multiple services, and aggregate datapoints from multiple metric time series. Histogram metrics provide an advantage over other metric types when calculating percentiles, such as the p90 percentile for a single MTS. See more in :ref:`metric-types`.
+.. list-table::
+   :widths: 33 33 33
+   :width: 100
+   :header-rows: 1
 
-For each metric, there is 1 metric time series (MTS) with responses ``sf_error: true`` or ``sf_error: false``.
+   * - Metric name
+     - Dimensions 
+     - Custom dimension available? (Yes/No)
+   * - ``service.request`` - the requests to endpoints in a service 
+     - * ``sf_environment``
+       * ``deployment.environment`` - This dimension is only available for histogram MMS.
+       * ``sf_service``
+       * ``service.name`` - This dimension is only available for histogram MMS.
+       * ``sf_error``
+     - Yes
+   * - ``inferred.services`` - 
+     - * ``sf_service``
+       * ``service.name`` - This dimension is only available for histogram MMS.
+       * ``sf_environment``
+       * ``deployment.environment`` - This dimension is only available for histogram MMS.
+       * ``sf_error``
+       * ``sf.kind``
+       * ``sf_operation``
+       * ``sf_httpMethod``
+     - No
+   * - ``spans`` - the count of spans (a single operation)   
+     - * ``sf_environment``
+       * ``deployment.environment`` - This dimension is only available for histogram MMS.
+       * ``sf_service``
+       * ``service.name`` - This dimension is only available for histogram MMS.
+       * ``sf_operation``
+       * ``sf_kind``
+       * ``sf_error``
+       * ``sf_httpMethod``, where relevant
+     - Yes
+   * - ``traces`` - the count of traces (collection of spans that represents a transaction)
+     - * ``sf_environment``
+       * ``deployment.environment`` - This dimension is only available for histogram MMS.
+       * ``sf_service``
+       * ``service.name`` - This dimension is only available for histogram MMS.
+       * ``sf_operation``
+       * ``sf_httpMethod``
+       *  ``sf_error``
+     - No
+   * - ``workflows`` - created by default when you create a business workflow
+     - * ``sf_environment``
+       * ``deployment.environment`` - This dimension is only available for histogram MMS.
+       * ``sf_workflow``
+       * ``sf_error``
+     - No
+
+Monitoring MetricSets in APM are generated as histogram metrics. Histogram metrics represent a distribution of measurements or metrics, with complete percentile data available. Data is distributed into equally sized intervals, allowing you to compute percentiles across multiple services, and aggregate datapoints from multiple metric time series. Histogram metrics provide an advantage over other metric types when calculating percentiles, such as the p90 percentile for a single MTS. See more in :ref:`metric-types`. For histogram MMS, there is a single metric for each component.
+
+Previously, MMS were classified as either a counter or gauge metric type. The previous MMS included 6 metrics for each component. 
 
 .. list-table::
    :widths: 33 33 33
@@ -72,7 +119,7 @@ For each metric, there is 1 metric time series (MTS) with responses ``sf_error:
      - ``<component>.duration.ns.p99``
 
 
-Example histogram metrics in APM
+Example metrics in APM
 ---------------------------------------------
 
 A histogram MTS uses the following syntax using SignalFlow:
@@ -90,22 +137,26 @@ The following table displays example SignalFlow functions:
    :header-rows: 1
 
    * - Description
-     - Previous MMS function
-     - Histogram MMS function
+     - Histogram MMS 
+     - Previous MMS (deprecated)
    * - Aggregate count of all MTS
-     - ``A = data('spans.count').sum().publish(label='A')``
      - ``A = histogram('spans').count().publish(label='A')``
+     - ``A = data('spans.count').sum().publish(label='A')``
    * - P90 percentile for single MTS
-     - ``filter_ = filter('sf_environment', 'us1') and filter('sf_service', 'apm-api-peanuts') and filter('sf_operation', 'POST /api/autosuggest/tagvalues') and filter('sf_httpMethod', 'POST') and filter('sf_error', 'false') A = data('spans.duration.ns.p90', filter=filter_, rollup='sum').publish(label='A')``
-     - ``filter_ = filter('sf_environment', 'us1') and filter('sf_service', 'apm-api-peanuts') and filter('sf_operation', 'POST /api/autosuggest/tagvalues') and filter('sf_httpMethod', 'POST') and filter('sf_error', 'false') A = histogram('spans', filter=filter_).percentile(pct=90).publish(label='A')``
+     - ``filter_ = filter('sf_environment', 'environment1') and filter('sf_service', 'service 1') and filter('sf_operation', 'operation1') and filter('sf_httpMethod', 'POST') and filter('sf_error', 'false') A = data('spans.duration.ns.p90', filter=filter_, rollup='sum').publish(label='A')``
+     - ``filter_ = filter('sf_environment', 'us1') and filter('sf_service', 'service1') and filter('sf_operation', 'POST /api/autosuggest/tagvalues') and filter('sf_httpMethod', 'POST') and filter('sf_error', 'false') A = data('spans.duration.ns.p90', filter=filter_, rollup='sum').publish(label='A')``
    * - Combined p90 for multiple services
-     - ``A = data('service.request.duration.ns.p90', filter=filter('sf_service', 'apm-graphql', 'apm-api-peanuts'), rollup='average').mean().publish(label='A')``
-     - ``A = histogram('service.request', filter=filter('sf_service', 'apm-graphql', 'apm-api-peanuts')).percentile(pct=90).publish(label='A')``
+     - ``A = histogram('service.request', filter=filter('sf_service', 'service 2', 'service 1')).percentile(pct=90).publish(label='A')``
+     - ``A = data('service.request.duration.ns.p90', filter=filter('sf_service', 'service 2', 'service 1'), rollup='average').mean().publish(label='A')``
 
 .. note:: Because an aggregation is applied on histogram(), to display all of the metric sets separately, each dimension needs to be applied as a groupby. 
 
+Dimensions
+=====================
 Each MMS has a set of dimensions you can use to monitor and alert on service performance. 
 
+In addition to the following default MMS, you can create custom MMS to deep dive on your MMS. See :ref:`cmms`.
+
 .. _service-mms: 
 
 Service dimensions
@@ -128,6 +179,8 @@ Inferred service dimensions
 * ``deployment.environment`` - This dimension is only available for histogram MMS.
 * ``sf_error``
 * ``sf.kind``
+* ``sf_operation``
+* ``sf_httpMethod``
 
 .. _endpoint-mms: