I am deploying a Python 3.7 Lambda function via Chalice. Because the code with its environment requirements, is larger than 50 MB limit, I am using the "automatic_layer" feature of Chalice to generate the layer with the requirements, which is awswrangler
.
Because the generated layer is > 50 MB, I am uploading the generated managed-layer-...-python3.7.zip
manually to s3 and create a Lambda layer. Then I re-deploy with chalice, removing the automatic_layer
option and setting the layers
to the generated ARN of the layer I manually created.
The function deployed this way worked OK for a couple of times, then started failing occasionally with "Segmentation Fault". The error rate increased shortly and now it is failing 100%.
Traceback:
> OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k
> START RequestId: 3b98bd4b-6cda-4d21-8090-1a49b17c06fc Version: $LATEST
> OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k
> END RequestId: 3b98bd4b-6cda-4d21-8090-1a49b17c06fc
> REPORT RequestId: 3b98bd4b-6cda-4d21-8090-1a49b17c06fc Duration: 7165.04 ms Billed Duration: 7166 ms Memory Size: 128 MB Max Memory Used: 41 MB
> RequestId: 3b98bd4b-6cda-4d21-8090-1a49b17c06fc Error: Runtime exited with error: signal: segmentation fault (core dumped)
> Runtime.ExitError
As awswrangler
itself requires boto3 & botocore, and they are already in the Lambda environment, I suspected that there might be a conflict of different versions of boto. I tried the same flow by explicitly including boto3 and botocore in the requirements but I am still receiving the same segmentation fault error.
Any help is much appreciated.