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Serverless Project Streaming and Parsing S3 files

This repo illustrates how to stream a large file from S3 and split it into separate S3 files after removing prior files

Goals

  1. Parse a large file without loading the whole file into memory
  2. Remove old data when new data arrives
  3. Wait for all these secondary streams to finish uploading to s3

Managing Complex Timing

  • Writing to S3 is slow. You must ensure you wait until the S3 upload is complete
  • We can't start writing to S3 until all the old files are deleted.
  • We don't know how many output files will be created, so we must wait until the input file has finished processing before starting to waiting for the outputs to finish

Demonstration Problem Statement

  • A school district central computer uploads all the grades for the district for a semester
  • The data file is has the following headers:
    • School,Semester,Grade,Subject,Class,Student Name,Score
  • Process the uploaded file, splitting it into the following structure:
    • Semester/School/Grade
    • Create a file called Subject-Class.csv with all the grades for that class
  • For this simulation, the central computer can update an entire Semester by uploading a new file. This could be set differently based on the application: For instance, if the central computer could upload the grades for a specific Semester + School, then we could update this line with the revised criteria to only clear that block of data

Here's the general outline of the demo program flow:

  • Open the S3 file as a Stream (readStream)
  • Create a csvStream from the input readStream
  • Pipe readStream to csvStream
  • While we have New Lines
    • Is this line for a new school (i.e. new CSV file)?
      • Start a PassThru stream (passThruStream)
      • Does this line start a new Semester (top-level folder we're replacing) in S3?
        • Start deleting S3 folder
      • Are all files deleted?
        • Use s3.upload with Body=passThruStream to upload the file
    • Write New Line to the passThruStream
  • Loop thru all passThruStream streams and close/end
  • Wait for all passThruStream streams to finish writing to S3

Environment Variables

BUCKET=(your s3 bucket name)

yarn Commands:

  • yarn build:test: Build fake CSV data in fixtures/
  • yarn test: Run a local test outputing files to /tmp/output instead of S3
  • yarn deploy:dev: Run serverless deploy with (stage=dev) to deploy function to AWS Lambda
  • yarn deploy:prod: Run serverless deploy --stage prod to deploy function to AWS Lambda
  • yarn logs:dev: Pull the AWS CloudWatch logs for the latest stage=dev run
  • yarn logs:prod: Pull the AWS CloudWatch logs for the latest stage=prod run
  • yarn upload:small:tiny: Upload fixtures/master-data-tiny.csv to S3 ${BUCKET}/dev/uploads
  • yarn upload:small:dev: Upload fixtures/master-data-small.csv to S3 ${BUCKET}/dev/uploads
  • yarn upload:medium:dev: Upload fixtures/master-data-medium.csv to S3 ${BUCKET}/dev/uploads
  • yarn upload:large:dev: Upload fixtures/master-data-large.csv to S3 ${BUCKET}/dev/uploads
  • yarn upload:small:tiny: Upload fixtures/master-data-tiny.csv to S3 ${BUCKET}/prod/uploads
  • yarn upload:small:prod: Upload fixtures/master-data-small.csv to S3 ${BUCKET}/prod/uploads
  • yarn upload:medium:prod: Upload fixtures/master-data-medium.csv to S3 ${BUCKET}/prod/uploads
  • yarn upload:large:prod: Upload fixtures/master-data-large.csv to S3 ${BUCKET}/prod/uploads

Validating S3 files

The following commands will downlaod the S3 processed files and use the same validations as yarn test:

  • NOTE: This assumes you've already run yarn upload:small
md /tmp/s3files
aws s3 cp s3://${BUCKET}/dev/processed /tmp/s3files --recursive
ts-node test/fileValidators.ts fixtures/master-data-small.csv /tmp/s3files/