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### Summary - Closes https://github.com/elastic/observability-docs/issues/2632. - Closes https://github.com/elastic/observability-docs/issues/2631. - Closes https://github.com/elastic/observability-docs/issues/2633. - Closes https://github.com/elastic/observability-docs/issues/2630. - Closes https://github.com/elastic/observability-docs/issues/1339.
196 lines
7.3 KiB
Text
196 lines
7.3 KiB
Text
[role="xpack"]
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[[service-overview]]
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=== Service overview
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Selecting a non-mobile <<services,*service*>> brings you to the *Service overview*.
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The *Service overview* contains a wide variety of charts and tables that provide
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high-level visibility into how a service is performing across your infrastructure:
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* Service details like service version, runtime version, framework, and APM agent name and version
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* Container and orchestration information
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* Cloud provider, machine type, service name, region, and availability zone
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* Serverless function names and event trigger type
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* Latency, throughput, and errors over time
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* Service dependencies
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[discrete]
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[[service-time-comparison]]
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=== Time series and expected bounds comparison
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For insight into the health of your services, you can compare how a service
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performs relative to a previous time frame or to the expected bounds from the
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corresponding {anomaly-job}. For example, has latency been slowly increasing
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over time, did the service experience a sudden spike, is the throughput similar
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to what the {ml} job expects – enabling a comparison can provide the answer.
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[role="screenshot"]
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image::apm/images/time-series-expected-bounds-comparison.png[Time series and expected bounds comparison]
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Select the *Comparison* box to apply a time-based or expected bounds comparison.
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The time-based comparison options are based on the selected time filter range:
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[options="header"]
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|====
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|Time filter | Time comparison options
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|≤ 24 hours
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|One day or one week
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|> 24 hours and ≤ 7 days
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|One week
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|> 7 days
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|An identical amount of time immediately before the selected time range
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|====
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You can use the expected bounds comparison if {ml-jobs} exist in your selected
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environment and you have
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{ml-docs}/setup.html#kib-visibility-spaces[access to the {ml-features}].
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[discrete]
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[[service-latency]]
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=== Latency
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Response times for the service. You can filter the *Latency* chart to display the average,
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95th, or 99th percentile latency times for the service.
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[role="screenshot"]
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image::apm/images/latency.png[Service latency]
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[discrete]
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[[service-throughput-transactions]]
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=== Throughput and transactions
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// tag::throughput-transactions[]
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The *Throughput* chart visualizes the average number of transactions per minute for the selected service.
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The *Transactions* table displays a list of _transaction groups_ for the
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selected service and includes the latency, traffic, error rate, and the impact for each transaction.
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Transactions that share the same name are grouped, and only one entry is displayed for each group.
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By default, transaction groups are sorted by _Impact_ to show the most used and slowest endpoints in your
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service. If there is a particular endpoint you are interested in, click *View transactions* to view a
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list of similar transactions on the <<transactions, transactions overview>> page.
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[role="screenshot"]
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image::apm/images/traffic-transactions.png[Traffic and transactions]
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// end::throughput-transactions[]
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[discrete]
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[[service-error-rates]]
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=== Failed transaction rate and errors
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// tag::ftr[]
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The failed transaction rate represents the percentage of failed transactions from the perspective of the selected service.
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It's useful for visualizing unexpected increases, decreases, or irregular patterns in a service's transactions.
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[TIP]
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====
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HTTP **transactions** from the HTTP server perspective do not consider a `4xx` status code (client error) as a failure
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because the failure was caused by the caller, not the HTTP server. Thus, `event.outcome=success` and there will be no increase in failed transaction rate.
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HTTP **spans** from the client perspective however, are considered failures if the HTTP status code is ≥ 400.
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These spans will set `event.outcome=failure` and increase the failed transaction rate.
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If there is no HTTP status, both transactions and spans are considered successful unless an error is reported.
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====
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// end::ftr[]
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The *Errors* table provides a high-level view of each error message when it first and last occurred,
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along with the total number of occurrences. This makes it very easy to quickly see which errors affect
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your services and take actions to rectify them. To do so, click *View errors*.
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[role="screenshot"]
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image::apm/images/error-rate.png[failed transaction rate and errors]
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[discrete]
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[[service-span-duration]]
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=== Span types average duration and dependencies
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The *Time spent by span type* chart visualizes each span type's average duration and helps you determine
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which spans could be slowing down transactions. The "app" label displayed under the
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chart indicates that something was happening within the application. This could signal that the APM
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agent does not have auto-instrumentation for whatever was happening during that time or that the time was spent in the
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application code and not in database or external requests.
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// tag::dependencies[]
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The *Dependencies* table displays a list of downstream services or external connections relevant
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to the service at the selected time range. The table displays latency, throughput, failed transaction rate, and the impact of
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each dependency. By default, dependencies are sorted by _Impact_ to show the most used and the slowest dependency.
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If there is a particular dependency you are interested in, click *<<dependencies,View dependencies>>* to learn more about it.
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NOTE: Displaying dependencies for services instrumented with the Real User Monitoring (RUM) agent
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requires an agent version ≥ v5.6.3.
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[role="screenshot"]
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image::apm/images/spans-dependencies.png[Span type duration and dependencies]
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// end::dependencies[]
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[discrete]
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[[service-cold-start]]
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=== Cold start rate
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The cold start rate chart is specific to serverless services, and displays the
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percentage of requests that trigger a cold start of a serverless function.
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A cold start occurs when a serverless function has not been used for a certain period of time.
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Analyzing the cold start rate can be useful for deciding how much memory to allocate to a function,
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or when to remove a large dependency.
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The cold start rate chart is currently supported for <<apm-lambda-cold-start-info,AWS Lambda>>
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functions and Azure functions.
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[role="screenshot"]
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image::apm/images/lambda-cold-start.png[lambda cold start graph]
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[discrete]
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[[service-instances]]
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=== Instances
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The *Instances* table displays a list of all the available service instances within the selected time range.
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Depending on how the service runs, the instance could be a host or a container. The table displays latency, throughput,
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failed transaction, CPU usage, and memory usage for each instance. By default, instances are sorted by _Throughput_.
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[role="screenshot"]
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image::apm/images/all-instances.png[All instances]
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[discrete]
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[[service-metadata]]
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=== Service metadata
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To view metadata relating to the service agent, and if relevant, the container and cloud provider,
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click on each icon located at the top of the page beside the service name.
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[role="screenshot"]
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image::apm/images/metadata-icons.png[Service metadata]
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*Service information*
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* Service version
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* Runtime name and version
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* Framework name
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* APM agent name and version
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*Container information*
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* Operating system
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* Containerized - Yes or no.
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* Total number of instances
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* Orchestration
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*Cloud provider information*
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* Cloud provider
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* Cloud service name
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* Availability zones
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* Machine types
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* Project ID
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* Region
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*Serverless information*
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* Function name(s)
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* Event trigger type
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*Alerts*
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* Recently fired alerts
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