When AWS Lambda executes your
Lambda function on your behalf, it takes care of provisioning and
managing resources needed to run your Lambda function. When you create a
Lambda function, you specify configuration information, such as the
amount of memory and maximum execution time that you want to allow for
your Lambda function. When a Lambda function is invoked, AWS Lambda
launches a container based on the configuration settings you provided.
Container creation and deletion are completely handling by AWS and there
is no way a user can manage it.
You can expect a delay when Lambda is invoked the first time or if there is a delay between subsequent requests because it takes time to set up the container and does other setups. AWS Lambda tries to reuse the container for subsequent invocations of the Lambda function.
After a Lambda function is executed, AWS Lambda maintains the container for some time. It means the service freeze the container for some time after a function execution completes and in that period if an invocation happens then Lambda will reuse the previous container.
The container reuse has few implications,
You can expect a delay when Lambda is invoked the first time or if there is a delay between subsequent requests because it takes time to set up the container and does other setups. AWS Lambda tries to reuse the container for subsequent invocations of the Lambda function.
After a Lambda function is executed, AWS Lambda maintains the container for some time. It means the service freeze the container for some time after a function execution completes and in that period if an invocation happens then Lambda will reuse the previous container.
The container reuse has few implications,
- Any declarations in your Lambda function code outside the handler
code remains initialized. For example, if your Lambda function
establishes a dynamoDB connection in the first run then instead of
re-establishing the connection, the original connection is used in
subsequent invocations.
- Background processes or callbacks initiated by your Lambda function that did not complete when the function ended resume if AWS Lambda chooses to reuse the container. You should make sure any background processes or callbacks in your code are complete before the code exits.
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