elastic · dedemorton · Nov 14, 2024 · Nov 13, 2024 · Nov 13, 2024
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 // :keywords: serverless, observability, overview
 
-preview:[]
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 The AI Assistant uses generative AI to provide:
 
 * **Chat**: Have conversations with the AI Assistant. Chat uses function calling to request, analyze, and visualize your data.

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 // :description: Find and investigate the causes of unusual spikes or drops in log rates.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 // <DocCallOut template="technical preview" />
 
 {obs-serverless} provides built-in log rate analysis capabilities,

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 // :description: Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Editor
 :goal: create, run, and view {anomaly-job}s
 include::../partials/roles.asciidoc[]

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 // :description: Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 // <DocCallOut template="technical preview" />
 
 The change point detection feature in {obs-serverless} detects distribution changes,

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 // :description: Predict future behavior of your data by creating a forecast for an anomaly detection job.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Editor
 :goal: create a forecast for an {anomaly-job}
 include::../partials/roles.asciidoc[]

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 // :description: Tune your job by creating calendars, adding job rules, and defining custom URLs.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Editor
 :goal: create calendars, add job rules, and define custom URLs
 include::../partials/roles.asciidoc[]

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 // :description: Automate anomaly detection and accelerate root cause analysis with AIOps.
 // :keywords: serverless, observability, overview
 
-preview:[]
-
 The AIOps capabilities available in {obs-serverless} enable you to consume and process large observability data sets at scale, reducing the time and effort required to detect, understand, investigate, and resolve incidents.
 Built on predictive analytics and {ml}, our AIOps capabilities require no prior experience with {ml}.
 DevOps engineers, SREs, and security analysts can get started right away using these AIOps features with little or no advanced configuration:

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 // :description: Learn about aggregations available in alerting rules.
 // :keywords: serverless, observability, reference
 
-preview:[]
-
 Aggregations summarize your data to make it easier to analyze.
 In some alerting rules, you can specify aggregations to gather data for the rule.
 

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 <titleabbrev>Anomaly detection</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create anomaly detection rules
 include::../partials/roles.asciidoc[]

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 // :description: Get alerts based on rules you define for detecting complex conditions in your applications and services.
 // :keywords: serverless, observability, overview, alerting
 
-preview:[]
-
 Alerting enables you to define _rules_, which detect complex conditions within different apps and trigger actions when those conditions are met. Alerting provides a set of built-in connectors and rules for you to use. This page describes all of these elements and how they operate together.
 
 [discrete]

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 <titleabbrev>APM anomaly</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create anomaly rules
 include::../partials/roles.asciidoc[]

@@ -8,8 +8,6 @@
 <titleabbrev>Custom threshold</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create a custom threshold rule
 include::../partials/roles.asciidoc[]

@@ -8,8 +8,6 @@
 <titleabbrev>Elasticsearch query</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create Elasticsearch query rules
 include::../partials/roles.asciidoc[]
@@ -57,7 +55,7 @@ For example:
 If you use {kibana-ref}/kuery-query.html[KQL] or {kibana-ref}/lucene-query.html[Lucene], you must specify a data view then define a text-based query.
 For example, `http.request.referrer: "https://example.com"`.
 +
-preview:[] If you use {ref}/esql.html[ES|QL], you must provide a source command followed by an optional series of processing commands, separated by pipe characters (|).
+If you use {ref}/esql.html[ES|QL], you must provide a source command followed by an optional series of processing commands, separated by pipe characters (|).
 For example:
 +
 [source,sh]
@@ -106,7 +104,7 @@ image::images/alerting-rule-types-es-query-valid.png[Test {es} query returns num
 
 // NOTE: This is an autogenerated screenshot. Do not edit it directly.
 
-preview:[]  If you use an ES|QL query, a table is displayed. For example:
+If you use an ES|QL query, a table is displayed. For example:
 
 [role="screenshot"]
 image::images/alerting-rule-types-esql-query-valid.png[Test ES|QL query returns a table when valid]

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 <titleabbrev>Error count threshold</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create error count threshold rules
 include::../partials/roles.asciidoc[]

@@ -8,8 +8,6 @@
 <titleabbrev>Failed transaction rate threshold</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create failed transaction rate threshold rules
 include::../partials/roles.asciidoc[]

@@ -8,8 +8,6 @@
 <titleabbrev>Inventory</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create inventory threshold rules
 include::../partials/roles.asciidoc[]

@@ -8,8 +8,6 @@
 <titleabbrev>Latency threshold</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create latency threshold rules
 include::../partials/roles.asciidoc[]

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 // :description: Create and manage rules for alerting when conditions are met.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Editor
 :goal: create and manage rules for alerting
 include::../partials/roles.asciidoc[]
@@ -126,7 +124,7 @@ time, indefinitely, or schedule single or recurring downtimes.
 When a rule is in a snoozed state, you can cancel or change the duration of
 this state.
 
-preview:[] To temporarily suppress notifications for _all_ rules, create a <<maintenance-windows,maintenance window>>.
+To temporarily suppress notifications for _all_ rules, create a <<maintenance-windows,maintenance window>>.
 
 // Remove tech preview?
 

@@ -8,8 +8,6 @@
 <titleabbrev>SLO burn rate</titleabbrev>
 ++++
 
-preview:[]
-
 :role: Editor
 :goal: create rules for alerting
 include::../partials/roles.asciidoc[]

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 // :description: Analyze the rate at which a specific field changes over time.
 // :keywords: serverless, observability, reference
 
-preview:[]
-
 You can use a rate aggregation to analyze the rate at which a specific field changes over time.
 This type of aggregation is useful when you want to analyze fields like counters.
 

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 <titleabbrev>SLO burn rate breaches</titleabbrev>
 ++++
 
-preview:[]
-
 SLO burn rate breaches occur when the percentage of bad events over a specified time period exceeds the threshold set in your <<observability-create-slo-burn-rate-alert-rule,SLO burn rate rule>>.
 When this happens, you are at risk of exhausting your error budget and violating your SLO.
 

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 // :description: Track and manage alerts for your services and applications.
 // :keywords: serverless, observability, how-to, alerting
 
-preview:[]
-
 :role: Editor
 :goal: perform this task
 include::../partials/roles.asciidoc[]

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 // :description: Use Elastic APM to monitor your AWS Lambda functions.
 // :keywords: serverless, observability, overview
 
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-
 Elastic APM lets you monitor your AWS Lambda functions.
 The natural integration of <<observability-apm-distributed-tracing,distributed tracing>> into your AWS Lambda functions provides insights into each function's execution and runtime behavior as well as its relationships and dependencies to other services.
 

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 // :keywords: serverless, observability, overview
 
-preview:[]
-
 :role: Admin
 :goal: use APM agents
 include::../partials/roles.asciidoc[]

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 // :keywords: serverless, observability, reference
 
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 [IMPORTANT]
 ====
 When collecting metrics, please note that the https://www.javadoc.io/doc/io.opentelemetry/opentelemetry-api/latest/io/opentelemetry/api/metrics/DoubleValueRecorder.html[`DoubleValueRecorder`]

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 // :keywords: serverless, observability, overview
 
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 [discrete]
 [[observability-apm-agents-opentelemetry-limitations-opentelemetry-traces]]
 == OpenTelemetry traces

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 // :keywords: serverless, observability, overview
 
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 [NOTE]
 ====
 This is one of several approaches you can use to integrate Elastic with OpenTelemetry.

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 // :keywords: serverless, observability, how-to
 
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 A resource attribute is a key/value pair containing information about the entity producing telemetry.
 Resource attributes are mapped to Elastic Common Schema (ECS) fields like `service.*`, `cloud.*`, `process.*`, etc.
 These fields describe the service and the environment that the service runs in.

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 // :keywords: serverless, observability, overview
 
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 [NOTE]
 ====
 For a complete overview of using OpenTelemetry with Elastic, explore https://github.com/elastic/opentelemetry[Elastic Distributions of OpenTelemetry].

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 // :description: Compress similar or identical spans to reduce storage overhead, processing power needed, and clutter in the Applications UI.
 // :keywords: serverless, observability, how-to
 
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 In some cases, APM agents may collect large amounts of very similar or identical spans in a transaction.
 For example, this can happen if spans are captured inside a loop or in unoptimized SQL queries that use multiple
 queries instead of joins to fetch related data.

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 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Editor
 :goal: create and manage custom links
 include::../partials/roles.asciidoc[]

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 // :description: Learn about the various APM data types.
 // :keywords: serverless, observability, overview
 
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-
 Elastic APM agents capture different types of information from within their instrumented applications.
 These are known as events, and can be spans, transactions, errors, or metrics:
 

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 // :description: Understand how a single request that travels through multiple services impacts your application.
 // :keywords: serverless, observability, how-to
 
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 A `trace` is a group of <<observability-apm-data-types,transactions>> and <<observability-apm-data-types,spans>> with a common root.
 Each `trace` tracks the entirety of a single request.
 When a `trace` travels through multiple services, as is common in a microservice architecture,

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 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 Global filters are ways you can filter your APM data based on a specific
 time range or environment. When viewing a specific service, the filter persists
 as you move between tabs.

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 // :keywords: serverless, observability, how-to
 
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 Correlations surface attributes of your data that are potentially correlated
 with high-latency or erroneous transactions. For example, if you are a site
 reliability engineer who is responsible for keeping production systems up and

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 // :description: Learn how to collect Application Performance Monitoring (APM) data and visualize it in real time.
 // :keywords: serverless, observability, how-to
 
-preview:[]
-
 :role: Admin
 :goal: send APM data to Elastic
 include::../partials/roles.asciidoc[]

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 // :keywords: serverless, observability, how-to
 
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 The Machine learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations.
 With this integration, you can quickly pinpoint anomalous transactions and see the health of
 any upstream and downstream services.

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 // :description: Make sure APM data is sent to Elastic securely and sensitive data is protected.
 // :keywords: serverless, observability, overview
 
-preview:[]
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 :role: Editor
 :goal: create and manage API keys
 include::../partials/roles.asciidoc[]

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 // :keywords: serverless, observability, reference
 
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 :role: Editor
 :goal: modify settings
 include::../partials/roles.asciidoc[]

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 // :keywords: serverless, observability, how-to
 
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 Elastic APM provides performance and error monitoring for AWS Lambda functions.
 See how your Lambda functions relate to and depend on other services, and
 get insight into function execution and runtime behavior, like lambda duration, cold start rate, cold start duration, compute usage, memory usage, and more.

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 // :keywords: serverless, observability, how-to
 
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 Querying your APM data is an essential tool that can make finding bottlenecks in your code even more straightforward.
 
 Using the query bar, a powerful data query feature, you can pass advanced queries on your data