saq 0.13.2

Distributed Python job queue with asyncio and redis

SAQ

SAQ (Simple Async Queue) is a simple and performant job queueing framework built on top of asyncio and redis. It can be used for processing background jobs with workers. For example, you could use SAQ to schedule emails, execute long queries, or do expensive data analysis.

Documentation

It uses redis-py >= 4.2.

It is similar to RQ and heavily inspired by ARQ. Unlike RQ, it is async and thus significantly faster if your jobs are async. Even if they are not, SAQ is still considerably faster due to lower overhead.

SAQ optionally comes with a simple UI for monitor workers and jobs.

Install

# minimal install
pip install saq

# web + hiredis
pip install saq[web,hiredis]

Usage

usage: saq [-h] [--workers WORKERS] [--verbose] [--web]
           [--extra-web-settings EXTRA_WEB_SETTINGS]
           [--port PORT] [--check]
           settings

Start Simple Async Queue Worker

positional arguments:
  settings              Namespaced variable containing
                        worker settings eg: eg
                        module_a.settings

options:
  -h, --help            show this help message and exit
  --workers WORKERS     Number of worker processes
  --verbose, -v         Logging level: 0: ERROR, 1: INFO,
                        2: DEBUG
  --web                 Start web app. By default, this
                        only monitors the current
                        worker's queue. To monitor
                        multiple queues, see '--extra-
                        web-settings'
  --extra-web-settings EXTRA_WEB_SETTINGS, -e EXTRA_WEB_SETTINGS
                        Additional worker settings to
                        monitor in the web app
  --port PORT           Web app port, defaults to 8080
  --check               Perform a health check

environment variables:
  AUTH_USER     basic auth user, defaults to admin
  AUTH_PASSWORD basic auth password, if not specified, no auth will be used

Example

import asyncio

from saq import CronJob, Queue

# all functions take in context dict and kwargs
async def test(ctx, *, a):
    await asyncio.sleep(0.5)
    # result should be json serializable
    # custom serializers and deserializers can be used through Queue(dump=,load=)
    return {"x": a}

async def cron(ctx):
  print("i am a cron job")

async def startup(ctx):
    ctx["db"] = await create_db()

async def shutdown(ctx):
    await ctx["db"].disconnect()

async def before_process(ctx):
    print(ctx["job"], ctx["db"])

async def after_process(ctx):
    pass

queue = Queue.from_url("redis://localhost")

settings = {
    "queue": queue,
    "functions": [test],
    "concurrency": 10,
    "cron_jobs": [CronJob(cron, cron="* * * * * */5")], # run every 5 seconds
    "startup": startup,
    "shutdown": shutdown,
    "before_process": before_process,
    "after_process": after_process,
}

To start the worker, assuming the previous is available in the python path

saq module.file.settings

Note: module.file.settings can also be a callable returning the settings dictionary.

To enqueue jobs

# schedule a job normally
job = await queue.enqueue("test", a=1)

# wait 1 second for the job to complete
await job.refresh(1)
print(job.results)

# run a job and return the result
print(await queue.apply("test", a=2))

# Run multiple jobs concurrently and collect the results into a list
print(await queue.map("test", [{"a": 3}, {"a": 4}]))

# schedule a job in 10 seconds
await queue.enqueue("test", a=1, scheduled=time.time() + 10)

Demo

Start the worker

python -m saq examples.simple.settings --web

Navigate to the web ui

Enqueue jobs

python examples/simple.py

Comparison to ARQ

SAQ is heavily inspired by ARQ but has several enhancements.

1. Avoids polling by leveraging BLMOVE or RPOPLPUSH and NOTIFY
   1. SAQ has much lower latency than ARQ, with delays of < 5ms. ARQ's default polling frequency is 0.5 seconds
   2. SAQ is up to 8x faster than ARQ
2. Web interface for monitoring queues and workers
3. Heartbeat monitor for abandoned jobs
4. More robust failure handling
   1. Storage of stack traces
   2. Sweeping stuck jobs
   3. Handling of cancelled jobs different from failed jobs (machine redeployments)
5. Before and after job hooks
6. Easily run multiple workers to leverage more cores

Development

python -m venv env
source env/bin/activate
pip install -e ".[dev,web]"
docker run -p 6379:6379 redis
./run_checks.sh