spcl / spcl/serverless-benchmarks

Merge existing workflows implementation into master

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aws azure enhancement gcp
Dominant language
Python
Stars
199
Forks
105
PR merge metrics
No merged PRs in 30d

Description

We have three branches with serverless workflows: original workflows, a newer branch workflow-benchmarks-updated that is up-to-date with branch, and feature/workflows

  • Finish linting
  • Support HTTP trigger for AWS step functions. Docs; issue
  • Pull Azure's error from URi
  • Fix HTTP's trigger implementation to have a separate path for Azure
  • Request ID for Azure - why is it generated?
  • What is the purpose of MY_FUNCTION_NAME on Gcp?
  • Proper naming of all functions involved in a workflow; keep the same format
  • Clean up usage of request id - how is it passed and is it always clean? Split between request id of workflow and functions?
  • Avoid reuploading the same code package when sharing function on Google Cloud (possibly also AWS).
  • Test container deployment
    • Apply Redis update of connection data for containerized functions
    • Move Redis into envs
  • Clean up usage of payload
    • Verify that we don't get double nesting of payload in functions; confirmed in the choice state after failure. There, we get a double-nested payload that we extract.
    • We should safely pass errors without worrying of the nesting.
    • If AWS doesn't all allow restructuring the JSON with an optional error field without involving intrinsic functions or additional states, then we should implement it ourselves.
  • Restructurize
    • Workflow should be a module of benchmark (storage, nosql).
    • Rename benchmark directories with better numbers
    • Move workflow classes (generator, workflow) to subdirectories
  • Failure handling
    • Implement AWS
    • Implement Azure
    • Implement GCP
  • Handling of Redis config
    • CLI script with auth
    • Don't do replacement of vars, just proper envs
    • Ensure that Redis config is provided
    • Move downloading results to proper class
    • Move Redis download to processing
  • Any change in the workflow redeploys all of the functions on AWS -> any way to make it more efficient?
  • Licensing for code and data.
  • Data source documentation
  • Add download of data for the 1000-genomes workflow; https://github.com/pegasus-isi/1000genome-workflow and ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/
  • Docs
    • Workflow definition for each platform and mapping
    • Workflow sources
    • Workflow data
    • Workflow implementation
    • Redis and profiling

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by comparing the workflows, workflow-benchmarks-updated, and feature/workflows branches, since no specific files or tests are named. Break the checklist into separately scoped workflow, platform, Redis, deployment, restructuring, and documentation tasks; completion is not defined until those open items are assigned clear outcomes.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, azure, gcp, python, redis
Domain
backend, cloud, devops
Issue type
Refactor
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
15/100

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