125 lines
3.0 KiB
Markdown
125 lines
3.0 KiB
Markdown
# Minimal Senior-Level Job Queue
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This is a deliberately small PostgreSQL-backed job queue for the interview assignment.
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The important parts are:
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- one internal priority queue
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- two tables: `jobs` and `job_events`
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- atomic claiming with PostgreSQL row locks and `SKIP LOCKED`
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- deterministic status transitions
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- automatic retries with exponential backoff
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- lease renewal for long-running jobs
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- lease timeout cleanup when workers die
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- idempotent job creation
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- demo UI with job state and event polling
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## Run
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```powershell
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copy .env.sample .env
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npm.cmd --prefix frontend install
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npm.cmd --prefix frontend run build
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docker compose up --build
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```
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Open:
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```text
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http://localhost:5173
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```
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API docs:
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```text
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http://localhost:8000/api/docs/
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```
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Admin panel:
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```text
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http://localhost:8000/admin/
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```
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Create an admin user:
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```powershell
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docker compose exec backend python manage.py createsuperuser
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```
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## Architecture
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```text
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React UI
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Django API ---- PostgreSQL
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Django worker process
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N worker threads from env
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```
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There is no queue table and no worker table. Workers are ephemeral process threads with generated ids. The queue is internal and ordered by:
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```text
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priority DESC, available_at ASC, created_at ASC, id ASC
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```
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## Statuses
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```text
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queued -> running
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running -> succeeded
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running -> queued retry after failure or timeout
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running -> failed attempts exhausted
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failed -> queued manual retry
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```
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The database also validates row shape:
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- queued jobs cannot have locks or finish timestamps
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- running jobs must have a lock owner and lease deadline
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- terminal jobs must have a finish timestamp and no lock
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## At-Least-Once Execution
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This queue provides at-least-once execution, not exactly-once execution.
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A worker can perform an external side effect and crash before marking a job succeeded. The lease will expire and the job can run again. Real handlers should therefore be idempotent.
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## Why PostgreSQL
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The assignment requires PostgreSQL, and PostgreSQL gives a compact solution for safe concurrent claiming through `SELECT ... FOR UPDATE SKIP LOCKED`. This keeps the implementation transactional, inspectable, and easy to demo.
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For a high-throughput distributed production queue, Redis-backed systems such as BullMQ or Sidekiq-style designs are common. That is documented as the next architecture, not implemented here.
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## Useful Commands
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Run backend tests locally with SQLite fallback:
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```powershell
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$env:TEST_DATABASE_ENGINE="sqlite"
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python manage.py test
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```
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Run pytest:
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```powershell
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$env:TEST_DATABASE_ENGINE="sqlite"
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python -m pytest
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```
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Run worker locally:
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```powershell
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python manage.py run_job_workers
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```
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## References
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- PostgreSQL `SKIP LOCKED`: https://www.postgresql.org/docs/current/sql-select.html
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- pg-boss: https://github.com/timgit/pg-boss
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- Solid Queue: https://github.com/rails/solid_queue
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- BullMQ concurrency: https://docs.bullmq.io/guide/workers/concurrency
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- Distributed task queue article: https://medium.com/@sindhukripa007/i-built-a-distributed-task-queue-from-scratch-to-actually-understand-how-they-work-37fa0452ff9b
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