Does AWS Lambda support multiprocessing?

Due to the Lambda execution environment not having /dev/shm (shared- memory for processes) support, you can’t use multiprocessing.

Can we use multithreading in Lambda?

Using multithreading in AWS Lambda can speed up your Lambda execution and reduce cost as Lambda charges in 100 ms unit.

How does Lambda handle multiple requests?

AWS Lambda is capable of serving multiple requests by horizontally scaling for multiple containers. Lambda can support up to 1000 parallel container executions by default. there are 1000 requests in 10 secs to the API.

How many threads can Lambda have?

In this doc Lambda Quotas it mentions that Execution processes/threads have an unmodifiable quota of 1024.

How many cores is AWS Lambda?

AWS Lambda now supports up to 10 GB of memory and 6 vCPU cores for Lambda Functions.

How many concurrent lambdas are there?

Each region in your AWS account has a Lambda concurrency limit. The limit applies to all functions in the same region and is set to 1000 by default.

How many transactions can Lambda handle?

Lambda can handle around 500 process when you allocate the max memory. Any other process will raise “OS Error 38: too many files open”.

How many lambdas can run concurrently?

How many cpus does Lambda have?

What are Lambda layers?

A Lambda layer is an archive containing additional code, such as libraries, dependencies, or even custom runtimes. By moving runtime dependencies from your function code to a layer, this can help reduce the overall size of the archive uploaded during a deployment.

How does Python multiprocessing queue work?

A simple way to communicate between process with multiprocessing is to use a Queue to pass messages back and forth. Any pickle-able object can pass through a Queue. This short example only passes a single message to a single worker, then the main process waits for the worker to finish.

Why do we use pipes instead of queue in AWS Lambda?

Note that we are using Pipes instead of Queue because, as mentioned in the Parallel Processing in Python with AWS Lambda blog, Due to the Lambda execution environment not having /dev/shm (shared- memory for processes) support, you can’t use multiprocessing.Queue or multiprocessing.Pool.

Why can’t I run two lambdas at the same time?

Due to the Lambda execution environment not having /dev/shm (shared- memory for processes) support, you can’t use multiprocessing.Queue or multiprocessing.Pool. Now with the two lambdas ready, I just needed to run them for different memory settings and compare the execution times to check if there was indeed any parallel execution happening.

How can two processes share a queue?

This example shows that a shared queue needs to originate from the master process, which is then passed to all of its subprocesses. In order for two completely unrelated processes to share data, they must communicate over some central or associated network device (sockets for example). Something has to coordinate the information.

What is the maximum memory for an AWS Lambda function?

UPDATE: At the time this post was written, the maximum memory possible for an AWS Lambda function was 3008 MB. In December 2020, AWS increased this upper limit to 10240 MB. This doesn’t change the fundamentals explained in this post, and only gives increased flexibility to the users.