Lambda programming model - Amazon Lambda
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Lambda programming model

Lambda provides a programming model that is common to all of the runtimes. The programming model defines the interface between your code and the Lambda system. You tell Lambda the entry point to your function by defining a handler in the function configuration. The runtime passes in objects to the handler that contain the invocation event and the context, such as the function name and request ID.

When the handler finishes processing the first event, the runtime sends it another. The function's class stays in memory, so clients and variables that are declared outside of the handler method in initialization code can be reused. To save processing time on subsequent events, create reusable resources like Amazon SDK clients during initialization. Once initialized, each instance of your function can process thousands of requests.

Your function also has access to local storage in the /tmp directory. The directory content remains when the execution environment is frozen, providing a transient cache that can be used for multiple invocations. For more information, see Lambda execution environment.

When Amazon X-Ray tracing is enabled, the runtime records separate subsegments for initialization and execution.

The runtime captures logging output from your function and sends it to Amazon CloudWatch Logs. In addition to logging your function's output, the runtime also logs entries when function invocation starts and ends. This includes a report log with the request ID, billed duration, initialization duration, and other details. If your function throws an error, the runtime returns that error to the invoker.


Logging is subject to CloudWatch Logs quotas. Log data can be lost due to throttling or, in some cases, when an instance of your function is stopped.

Lambda scales your function by running additional instances of it as demand increases, and by stopping instances as demand decreases. This model leads to variations in application architecture, such as:

  • Unless noted otherwise, incoming requests might be processed out of order or concurrently.

  • Do not rely on instances of your function being long lived, instead store your application's state elsewhere.

  • Use local storage and class-level objects to increase performance, but keep to a minimum the size of your deployment package and the amount of data that you transfer onto the execution environment.

For a hands-on introduction to the programming model in your preferred programming language, see the following chapters.