pybgworker 0.3.0

Lightweight production-ready background task worker with cron, rate limiting and JSON observability

PyBgWorker

A lightweight, production-ready background task library for Python.

PyBgWorker provides a durable SQLite-backed task queue, scheduling (cron and countdown/ETA), rate limiting, retries, and structured observability without external infrastructure.

It is designed to be simple, reliable, and easy to deploy.

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Features

• Persistent SQLite task queue
• Multi-worker safe execution
• Task scheduling: cron + countdown/ETA
• Retry + failure handling
• Task cancellation support
• Crash isolation via subprocess
• Task priority execution
• Task status tracking
• Result storage and retrieval
• Worker statistics and monitoring
• JSON structured logging
• Task duration tracking
• Rate limiting (overload protection)
• Heartbeat monitoring
• Configurable single-worker concurrency
• CLI tools: inspect, retry, failed, purge, cancel, stats
• Production-safe worker loop

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Installation

pip install pybgworker

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Basic Usage

Define a task

from pybgworker.task import task

@task(name="add")
def add(a, b):
    return a + b

Enqueue a task

add.delay(1, 2)

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Run worker

python -m pybgworker.cli run --app example

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Worker Concurrency

Run multiple tasks in parallel within a single worker process:

PYBGWORKER_CONCURRENCY=4 python -m pybgworker.cli run --app example

Or with a CLI flag:

python -m pybgworker.cli run --app example --concurrency 4

Defaults to 1 for backward-compatible behavior.

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Cron Scheduler

Run recurring tasks:

from pybgworker.scheduler import cron
from pybgworker.task import task

@task(name="heartbeat_task")
@cron("*/1 * * * *")
def heartbeat():
    print("alive")

Cron runs automatically inside the worker.

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JSON Logging

All worker events are structured JSON:

{"event":"task_start","task_id":"..."}
{"event":"task_success","duration":0.12}

This enables:

• monitoring
• analytics
• alerting
• observability pipelines

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Rate Limiting

Protect infrastructure from overload:

RATE_LIMIT = 5  # tasks per second

Ensures predictable execution under heavy load.

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Retry Backoff + Jitter

Enable exponential backoff and jitter to spread retry load:

from pybgworker.task import task

@task(
    name="api_call",
    retries=5,
    retry_delay=2,
    retry_backoff=True,
    retry_backoff_factor=2,
    retry_max_delay=60,
    retry_jitter=0.2,
)
def api_call():
    ...

• retry_backoff: enable exponential backoff
• retry_backoff_factor: multiplier per attempt (default 2)
• retry_max_delay: cap delay in seconds
• retry_jitter: randomize delay (ratio <=1 or seconds if >1)

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CLI Commands

Inspect queue:

python -m pybgworker.cli inspect

Retry failed task:

python -m pybgworker.cli retry <task_id>

Cancel task:

python -m pybgworker.cli cancel <task_id>

Purge queued tasks:

python -m pybgworker.cli purge

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Observability

PyBgWorker logs:

• worker start
• cron events
• task start
• success
• retry
• failure
• timeout
• crash
• heartbeat errors

All machine-readable.

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Database Cleanup

Enable automatic retention cleanup for completed tasks:

python -m pybgworker.cli run --app example --retention-days 30

Environment variable alternative:

PYBGWORKER_RETENTION_DAYS=30 python -m pybgworker.cli run --app example

Optional cleanup interval (hours, default 24):

python -m pybgworker.cli run --app example --cleanup-interval-hours 12

When enabled, PyBgWorker prunes finished tasks older than the retention window and runs a VACUUM after deletions.

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Failed vs Dead

• failed: a task failed but may still be retried (or was manually marked failed).
• dead: a task exhausted all retries and was moved to a terminal state for inspection.

Use pybgworker failed to see both failed + dead, or pybgworker dead for dead-only.

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Design Goals

• zero external dependencies
• SQLite durability
• safe multiprocessing
• operator-friendly CLI
• production observability
• infrastructure protection

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Roadmap

Planned but not yet included:

• Task/result TTL cleanup (beyond retention of finished tasks)
• Multiple named queues + routing
• Pluggable backends (Redis first)
• Cluster coordination / leader election for scheduler
• Metrics endpoint and health checks
• Dashboard API + web UI
• Workflow pipelines / DAGs

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Feedback and Issues

For feedback, enhancement requests, or error reports, please use this form:

Submit feedback / report an issue

Copy/paste link:

https://forms.gle/bUFRximzAGN6bCBQA

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License

MIT License