- Observability: other versions:
- Get started
- What is Elastic Observability?
- What’s new in 8.17
- Quickstart: Monitor hosts with Elastic Agent
- Quickstart: Monitor your Kubernetes cluster with Elastic Agent
- Quickstart: Monitor hosts with OpenTelemetry
- Quickstart: Unified Kubernetes Observability with Elastic Distributions of OpenTelemetry (EDOT)
- Quickstart: Collect data with AWS Firehose
- Add data from Splunk
- Applications and services
- Application performance monitoring (APM)
- Get started
- Learn about data types
- Collect application data
- View and analyze data
- Act on data
- Use APM securely
- Manage storage
- Configure APM Server
- Monitor APM Server
- APM APIs
- Troubleshooting
- Upgrade
- Release notes
- Known issues
- Synthetic monitoring
- Get started
- Scripting browser monitors
- Configure lightweight monitors
- Manage monitors
- Work with params and secrets
- Analyze monitor data
- Monitor resources on private networks
- Use the CLI
- Configure projects
- Multi-factor Authentication
- Configure Synthetics settings
- Grant users access to secured resources
- Manage data retention
- Use Synthetics with traffic filters
- Migrate from the Elastic Synthetics integration
- Scale and architect a deployment
- Synthetics support matrix
- Synthetics Encryption and Security
- Troubleshooting
- Real user monitoring
- Uptime monitoring (deprecated)
- Tutorial: Monitor a Java application
- Application performance monitoring (APM)
- CI/CD
- Cloud
- Infrastructure and hosts
- Logs
- Troubleshooting
- Incident management
- Data set quality
- Observability AI Assistant
- Reference
Observe Lambda functions
editObserve Lambda functions
editElastic 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.
To set up Lambda monitoring, see the relevant quick start guide.
Cold starts
editA cold start occurs when a Lambda function has not been used for a certain period of time. A lambda worker receives a request to run the function and prepares an execution environment.
Cold starts are an unavoidable byproduct of the serverless world, but visibility into how they impact your services can help you make better decisions about factors like how much memory to allocate to a function, whether to enable provisioned concurrency, or if it’s time to consider removing a large dependency.
Cold start rate
editThe cold start rate (i.e. proportion of requests that experience a cold start) is displayed per service and per transaction.
Cold start is also displayed in the trace waterfall, where you can drill-down into individual traces and see trace metadata like AWS request ID, trigger type, and trigger request ID.
Latency distribution correlation
editThe latency correlations feature can be used to visualize the impact of Lambda cold starts on latency—just select the faas.coldstart
field.
AWS Lambda function grouping
editThe default APM agent configuration results in one APM service per AWS Lambda function, where the Lambda function name is the service name.
In some use cases, it makes more sense to logically group multiple lambda functions under a single
APM service. You can achieve this by setting the ELASTIC_APM_SERVICE_NAME
environment variable
on related Lambda functions to the same value.
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