AWS Lambda Memory Exhaustion scenarios When Processing Large S3 Files with Boto3
I've been researching this but I'm maintaining legacy code that Quick question that's been bugging me - I'm working with an scenario with my AWS Lambda function that processes large files from S3 using Boto3... The function works fine for smaller files, but when I try to process files larger than 100MB, I receive the following behavior message in CloudWatch logs: `MemoryError: Out of memory`. Here's a simplified version of my Lambda function: ```python import json import boto3 s3 = boto3.client('s3') def lambda_handler(event, context): bucket_name = event['Records'][0]['s3']['bucket']['name'] object_key = event['Records'][0]['s3']['object']['key'] # Trying to load the large file directly into memory response = s3.get_object(Bucket=bucket_name, Key=object_key) data = response['Body'].read() # Process the data (this is where it fails for large files) process_data(data) return { 'statusCode': 200, 'body': json.dumps('Processing complete') } ``` I have tried increasing the Lambda memory limit to 512 MB, but it still doesn't solve the question. I suspect that loading the entire file into memory is inefficient for large files. I've also seen suggestions about using streams with `boto3`, but Iām unsure how to implement that in my existing code. Can someone provide guidance on a more efficient way to handle large S3 files in AWS Lambda using Boto3 to avoid memory issues? This is part of a larger CLI tool I'm building. Has anyone else encountered this? This is part of a larger desktop app I'm building. Any ideas how to fix this? Any help would be greatly appreciated! Any help would be greatly appreciated!