opensanctions / opensanctions/pravda

Define the Pravda Kubernetes deployment model

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Dominant language
Python
Stars
6
Forks
3
PR merge metrics
No merged PRs in 30d

Description

Define how Pravda should run on GCP Kubernetes.

Pravda consists of a FastAPI service and a separate headed-Chrome process exposed through playwright run-server. Decide whether these should run as two containers in one Pod or as separate Deployments connected through an internal Service.

Cover

  • Pravda and browser container responsibilities.
  • Required environment configuration.
  • /healthz and /readyz endpoints.
  • CPU and memory requirements.
  • Cloud SQL connectivity.
  • GCS access through Workload Identity.
  • Initial replica and scaling model.
  • Structured logging based on zavod’s logging setup.

Questions

  • Should each Pravda replica own a browser sidecar?
  • Should browsers run as a separately scaled pool?
  • Can playwright run-server safely serve concurrent captures?
  • Which dependency checks belong in /readyz and /healthz?

Acceptance criteria

  • The Pod/Deployment topology is selected and documented.
  • Required container and Kubernetes configuration is specified.
  • Health endpoint semantics are defined.
  • Resource and scaling assumptions are explicit.

Contributor guide

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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 reviewing the FastAPI service, the headed-Chrome process exposed through playwright run-server, and the linked zavod logging setup. Compare the sidecar and separately scaled Deployment options, then define the container configuration, health endpoint semantics, Cloud SQL and GCS access, resources, and scaling assumptions. Done means the selected topology and all acceptance criteria are documented.

Written by the indexing model from the issue text.

Assessment

Tech stack
fastapi, gcp, google-cloud, kubernetes, playwright, python
Domain
api, backend, cloud, databases, devops, infrastructure
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
30/100

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