Scatter gather with AWS lambda

The challenge

Implement batch processing on AWS:

• scatter: Split up a single file of records to be processed (the file has been uploaded via s3)
• process: Process records as parallel as possible
• gather: Detect completion of processing and aggregate a result summary report in s3

Prerequisites

• python 3.8
• GNU make
• docker
• awscli
• tfvm or terraform
• cw >= v3.3.0

Usage locally

Start localstack, deploy and run benchmark

make clean start_localstack deploy benchmark report

Stop and cleanup

make stop_localstack clean

Usage on aws

All resources will be prefixed with your current ${USER}-. Pass SCOPE=mycustomprefix- to make to override this default.

Build, package, deploy run benchmark, report on measurements

make ENV=aws clean deploy_resources deploy_service benchmark report

Undeploy

make ENV=aws destroy

Variants

The task has been implemented in various variants:

• s3-sqs-lambda-sync (with boto3 blocking io)
• s3-sqs-lambda-async (with aioboto3 async io)
• s3-sqs-lambda-async-chunked (with aioboto3 async io, records packed into chunks)
• s3-sqs-lambda-dynamodb (with aioboto3 async io, records stored in dynamodb)
• s3-notification-sqs-lambda (with aioboto3 async io, records stored in s3 in chunks, functions invoked by s3 notifications through sqs queues)

More/ alternative variants

• sfn?
• glue?
• emr (spark)?
• s3 athena?
• s3 batch
• single fat vm

Results

(Data)

Repository structure

• infra - Resources and service infrastructure
• src - Service sources
• tests - Service tests
• benchmark - Benchmark sources

Documentation

• Learnings
• Links
• Troubleshooting

License

Copyright 2020 by Cornelius Buschka. All rights reserved.

Apache Public License 2.0