raquel

raquel

Simple and elegant Job Queues for Python using SQL.

Tired of complex job queues for distributed computing or event based systems? Do you want full visibility and complete reliability of your job queue? Raquel is a perfect solution for a distributed task queue and background workers.

• Simple: Use any existing or standalone SQL database. Requires a single table!
• Flexible: Schedule whatever you want however you want. No frameworks, no restrictions.
• Reliable: Uses SQL transactions and handles exceptions, retries, and "at least once" execution. SQL guarantees persistent jobs.
• Transparent: Full visibility into which jobs are runnning, which failed and why, which are pending, etc.

Installation

pip install raquel

To install with async support, specify the asyncio extra. This simply adds the greenlet package as a dependency.

pip install raquel[asyncio]

Usage

Basic example

On the client side, schedule your jobs. Payload can be any JSON-serializable object or simply a string.

from raquel import Raquel

# Raquel uses SQLAlchemy to connect to most SQL databases
rq = Raquel("postgresql+psycopg2://postgres:postgres@localhost/postgres")
# Enqueing a job is as simple as this
rq.enqueue(queue="messages", payload="Hello, World!")
rq.enqueue(queue="tasks", payload={"data": [1, 2]})

On the worker side, pick jobs from the queue and process them:

import time
from raquel import Raquel

rq = Raquel("postgresql+psycopg2://postgres:postgres@localhost/postgres")

# This loop will run forever, picking up jobs from the "messages" and  "tasks"
# queues as their scheduled time comes.
for job in rq.subscribe("messages", "tasks"):
    do_work(job.payload)

Or, if you prefer, you can use a more hands-on approach, by manually dequeuing jobs. In fact, this is exactly what the subscribe() method does under the hood.

while True:
    # dequeue() is a context manager that yields a job object. It will
    # handle the job status and exceptions for you.
    with rq.dequeue("tasks") as job:
        if not job:
            time.sleep(1)
            continue
        do_work(job.payload)

How about async?

Everything in Raquel is designed to work with both sync and async code. You can use the AsyncRaquel class to enqueue and dequeue jobs in an async manner.

Client:

import asyncio
from raquel import AsyncRaquel

rq = AsyncRaquel("postgresql+asyncpg://postgres:postgres@localhost/postgres")

async def main():
    await rq.enqueue("default", {'my': {'name_is': 'Slim Shady'}})

asyncio.run(main())

Worker:

import asyncio
from raquel import AsyncRaquel

rq = AsyncRaquel("postgresql+asyncpg://postgres:postgres@localhost/postgres")

async def main():
    async for job in rq.subscribe():
        await do_work(job.payload)

asyncio.run(main())

The jobs table

Raquel uses a single database table called jobs. This is all it needs. Can you believe it?

Here is what this table consists of:

Column	Type	Description	Default	Nullable
id	UUID	Unique identifier of the job.		No
queue	TEXT	Name of the queue.	"default"	No
payload	TEXT	Payload of the job. It can by anything. Just needs to be serializable to text.	Null	Yes
status	TEXT	Status of the job.	"queued"	No
max_age	INTEGER	Maximum age of the job in milliseconds.	Null	Yes
max_retry_count	INTEGER	Maximum number of retries.	Null	Yes
min_retry_delay	INTEGER	Minimum delay between retries in milliseconds.	1000	Yes
max_retry_delay	INTEGER	Maximum delay between retries in milliseconds.	12 * 3600 * 1000	Yes
backoff_base	INTEGER	Base in milliseconds for exponential retry backoff.	1000	Yes
enqueued_at	BIGINT	Time when the job was enqueued in milliseconds since epoch (UTC).	now	No
scheduled_at	BIGINT	Time when the job is scheduled to run in milliseconds since epoch (UTC).	now	No
attempts	INTEGER	Number of attempts to run the job.	0	No
error	TEXT	Error message if the job failed.	Null	Yes
error_trace	TEXT	Error traceback if the job failed.	Null	Yes
claimed_by	TEXT	ID or name of the worked that claimed the job.	Null	Yes
claimed_at	BIGINT	Time when the job was claimed in milliseconds since epoch (UTC).	Null	Yes
finished_at	BIGINT	Time when the job was finished in milliseconds since epoch (UTC).	Null	Yes

Job status

Jobs can have the following statuses:

• queued - Job is waiting to be picked up by a worker.
• claimed - Job is currently locked and is being processed by a worker.
• success - Job was successfully executed.
• failed - Job failed to execute. This happens when an exception was caught by the dequeue() context manager. The last error message and traceback are stored in the error and error_trace columns. Job will be retried, mening it will be rescheduled with an exponential backoff delay.
• cancelled - Job was manually cancelled.
• expired - Job was not picked up by a worker in time (when max_age is set).
• exhausted - Job has reached the maximum number of retries (when max_retry_count is set).

How do we guarantee that same job is not picked up by multiple workers?

When a worker picks up a job, it first selects the job from the table and then updates the job status to claimed. This is done in a single transaction, so no other worker can pick up the same job at the same time.

In PostgreSQL, we use the SELECT FOR UPDATE SKIP LOCKED statement is used. In other databases that support this (such as Oracle, MySQL) a similar approach is used.

In extremely simple databases, such as SQLite, the fact that the whole database is locked during a write operation guarantees that no other worker will be able to set the job status to claimed at the same time.

Enqueue a job

All jobs are scheduled to run at a specific time. By default, this time is set to the current time when the job is enqueued. You can set the scheduled_at field to a future time to schedule the job to run at that time.

rq.enqueue(10_000, queue="my-jobs", at=datetime.now() + timedelta(hours=10))

Fancy SQL? You can schedule jobs by directly inserting them into the table. For example, in PostgreSQL:

INSERT INTO jobs 
    (id, queue, status, payload)
VALUES
    (uuid_generate_v4(), 'my-jobs', 'queued', '{"my": "payload"}'),
    (uuid_generate_v4(), 'my-jobs', 'queued', '101'),
    (uuid_generate_v4(), 'my-jobs', 'queued', 'Is this the real life?');

Reschedule a job

The reschedule() method is used to reprocess the job at a later time. The job will remain in the queue with a new scheduled execution time, and the current attempt won't count towards the maximum number of retries. This method should only be called inside the dequeue() or subscribe() context managers.

for job in rq.subscribe("my-queue"):
    # Check if we have everything ready to process the job, and if not,
    # reschedule the job to run 10 minutes from now
    if not is_everything_ready_to_process(job.payload):
        job.reschedule(delay=timedelta(minutes=10))
    else:
        # Otherwise, process the job
        do_work(job.payload)

The only things that change in the jobs table are the scheduled_at field, the claimed_by field (if claim_as was set), and the claimed_at field.

Thus, you can find all rescheduled jobs by filtering for those that were claimed, but not finished, and didn't have an error.

SELECT * FROM jobs WHERE status = 'claimed' AND finished_at IS NULL AND error IS NULL;

Other ways to reschedule a job using the same reschedule() method:

# Run when the next day starts
job.reschedule(
    at=datetime.now().replace(
        hour=0, minute=0, second=0, microsecond=0,
    ) + timedelta(days=1)
)

# Same but using the `delay` parameter
job.reschedule(
    at=datetime.now().replace(hour=0, minute=0, second=0, microsecond=0),
    delta=timedelta(days=1),
)

# Run in 500 milliseconds
job.reschedule(delay=500)

# Run in `min_retry_delay` milliseconds, as configured for this job
# (default is 1 second)
job.reschedule()

Reject the job

In case your worker can't process the job for some reason, you can reject it, allowing it to be immediately claimed by another worker. This method should only be called inside the dequeue() context manager.

The processing attempt won't count towards the maximum number of retries. Overall it will look like the job wasn't even attempted and the corresponding record in the jobs table will remain completely unchanged.

for job in rq.subscribe("my-queue"):
    # Maybe this worker doesn't have the necessary permissions to process
    # this job.
    if job.payload.get("requires_admin"):
        job.reject()
        continue

    # Otherwise, process the job
    do_work(job.payload)

Queue names

By default, all jobs are placed into the "default" queue. You can specify the queue name when enqueuing a job:

# Enqueue a payload into the "default" queue
rq.enqueue(payload="Hello, World!")

# Same as above
rq.enqueue("default", "Hello, World!")

# Enqueue a payload into the "my-queue" queue
rq.enqueue("my-queue", "Hello, World!")

When jobs are dequeued by a worker, they are dequeued from whatever queue, whichever job is scheduled to run first. If you want to dequeue jobs from specific queues, you can specify the queue name in the subscribe() method:

for job in rq.subscribe("my-queue", "another-queue", "as-many-as-you-want"):
    do_work(job.payload)

Job retries

Jobs are retried when they fail. When an exception is caught by the dequeue() context manager, the job is rescheduled with an exponential backoff delay.

Same works with subscribe() method, which is just a wrapper around dequeue().

By default, the job will be retried indefinitely. You can set the max_retry_count or max_age fields to limit the number of retries or the maximum age of the job.

for job in rq.subscribe():
    # Catch an exception manually
    try:
        do_work(job.payload)
    except Exception as e:
        # If you mark the job as failed, it will be retried.
        job.fail(str(e))

    # Or, you can just let the context manager handle the exception for you.
    # The exception will be caught and the job will be retried.
    # Under the hood, context manager will call `job.fail()` for you.
    raise Exception("Oh no")

Whenever job fails, the error and the traceback are stored in the error and error_trace columns. The job status is set to failed and the job will be retried.

The next retry time is calculated as follows:

• Take the current scheduled_at time.
• Add the time it took to process the job (aka duration).
• Add the retry delay.

Now, the retry delay is calculated as:

backoff_base * 2 ^ attempt

Whic is a planned retry delay. The actual retry delay is capped between the min_retry_delay and max_retry_delay. The min_retry_delay defaults to 1 second and the max_retry_delay defaults to 12 hours. The backoff_base defaults to 1 second.

In other words, here is how your job will be retried (assuming there is always a worker available and the job takes almost no time to process):

Retry	delay
1	1 second after 1'st attempt
2	2 seconds after 2'nd attempt
3	after 4 seconds
...	...
6	after ~2 minutes
...	...
10	after ~30 minutes
...	...
14	after ~9 hours
...	...

and so on with the maximum delay of 12 hours, or based on the maximum value you set for this job using the max_retry_delay setting.

For certain types of jobs, it makes sense to chill out for a bit before retrying. For example, an API might have a rate limit you've just hit or some data might not be ready yet. In such cases, you can set the min_retry_delay to a higher value, such as 10 or 30 seconds.

ℹ️ Remeber, that all durations and timestampts are in milliseconds. So 10 seconds is 10 * 1000 = 10000 milliseconds. 1 minute is 60 * 1000 = 60000 milliseconds.

Prepare your database

You can configure the table using the create_all() method, which will automatically use the supported syntax for the database you are using (it is safe to run it multiple times, it only creates the table once.).

# Works for all databases
rq.create_all()

Alternatively, you can create the table manually. For example, in Postgres:

CREATE TABLE IF NOT EXISTS jobs (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    queue TEXT NOT NULL,
    payload TEXT,
    status TEXT NOT NULL DEFAULT 'queued',
    max_age BIGINT,
    max_retry_count INTEGER,
    min_retry_delay INTEGER DEFAULT 1000,
    max_retry_delay INTEGER DEFAULT 43200000,
    backoff_base INTEGER DEFAULT 1000,
    enqueued_at BIGINT NOT NULL DEFAULT extract(epoch from now()) * 1000,
    scheduled_at BIGINT NOT NULL DEFAULT extract(epoch from now()) * 1000,
    attempts INTEGER NOT NULL DEFAULT 0,
    error TEXT,
    error_trace TEXT,
    claimed_by TEXT,
    claimed_at BIGINT,
    finished_at BIGINT
);
CREATE INDEX IF NOT EXISTS idx_jobs_queue ON jobs (queue);
CREATE INDEX IF NOT EXISTS idx_jobs_status ON jobs (status);
CREATE INDEX IF NOT EXISTS idx_jobs_scheduled_at ON jobs (scheduled_at);
CREATE INDEX IF NOT EXISTS idx_jobs_claimed_by ON jobs (claimed_by);

Or, when using Alembic, add this to your upgrade() and downgrade() functions in the appropriate migration file:

def upgrade() -> None:
    op.create_table('jobs',
    sa.Column('id', sa.UUID(), autoincrement=False, nullable=False),
    sa.Column('queue', sa.VARCHAR(length=255), autoincrement=False, nullable=False),
    sa.Column('payload', sa.VARCHAR(), autoincrement=False, nullable=True),
    sa.Column('status', sa.VARCHAR(length=30), autoincrement=False, nullable=False),
    sa.Column('max_age', sa.BIGINT(), autoincrement=False, nullable=True),
    sa.Column('max_retry_count', sa.INTEGER(), autoincrement=False, nullable=True),
    sa.Column('min_retry_delay', sa.INTEGER(), autoincrement=False, nullable=True),
    sa.Column('max_retry_delay', sa.INTEGER(), autoincrement=False, nullable=True),
    sa.Column('backoff_base', sa.INTEGER(), autoincrement=False, nullable=True),
    sa.Column('enqueued_at', sa.BIGINT(), autoincrement=False, nullable=False),
    sa.Column('scheduled_at', sa.BIGINT(), autoincrement=False, nullable=False),
    sa.Column('attempts', sa.INTEGER(), autoincrement=False, nullable=False),
    sa.Column('error', sa.VARCHAR(), autoincrement=False, nullable=True),
    sa.Column('error_trace', sa.VARCHAR(), autoincrement=False, nullable=True),
    sa.Column('claimed_by', sa.VARCHAR(length=255), autoincrement=False, nullable=True),
    sa.Column('claimed_at', sa.BIGINT(), autoincrement=False, nullable=True),
    sa.Column('finished_at', sa.BIGINT(), autoincrement=False, nullable=True),
    sa.PrimaryKeyConstraint('id', name='jobs_pkey')
    )
    op.create_index('ix_jobs_status', 'jobs', ['status'], unique=False)
    op.create_index('ix_jobs_scheduled_at', 'jobs', ['scheduled_at'], unique=False)
    op.create_index('ix_jobs_queue', 'jobs', ['queue'], unique=False)
    op.create_index('ix_jobs_claimed_by', 'jobs', ['claimed_by'], unique=False)

def downgrade() -> None:
    op.drop_index('ix_jobs_claimed_by', table_name='jobs')
    op.drop_index('ix_jobs_queue', table_name='jobs')
    op.drop_index('ix_jobs_scheduled_at', table_name='jobs')
    op.drop_index('ix_jobs_status', table_name='jobs')
    op.drop_table('jobs')

Fun facts

• Raquel is named after the famous actress Raquel Welch, who I have never seen in a movie. But her poster was one of the cutouts hanging in the shrine dedicated to Arnold Schwarzenegger in the local gym I used to attend. Don't ask me why and how.
• The name Raquel is also a combination of the words "queue" and "SQL".
• The payload can be empty or Null (None). You can use it to schedule jobs without any payload, for example, to send a notification or to run a periodic task.

Comparison to alternatives

Feature	Raquel	Celery	RQ	Dramatiq	arq	pgqueuer	pq
Special tooling to run workers	No ✅	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌
Needs message queue	No ✅	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌
Supports SQL	Yes ✅	No ❌	No ❌	No ❌	No ❌	Yes ✅	Yes ✅
Full visibility	Yes ✅	No ❌	No ❌	No ❌	No ❌	Yes ✅	Yes ✅
Reliable	Yes ✅	Yes ✅	Yes ✅	Yes ✅	Yes ✅	Yes ✅	Yes ✅
Supports async	Yes ✅	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌
Persistent jobs	Yes ✅	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ✅	Yes ✅
Schedule from anywhere	Yes ✅	No ❌	No ❌	Yes ✅	No ❌	Yes ✅	Yes ✅
Job payload size limit	No ✅	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌	Yes ❌

As you can clearly see, not only is Raquel a superior distributed task queue, job queue, and background worker system, but very likely the best software ever written in the history of mankind. And I claim this without any bias.

Thinking behind Raquel

• Random UUID (uuid4) is used as job ID, to facilitate migration between databases and HA setups.
• Payload is stored as text, to allow any kind of data to be stored.
• Job lock is performed by first selecting the first unclaimed job through SELECT... WHERE status = 'queued' and then doing the UPDATE... WHERE id = ? query in the same transaction, to ensure atomicity. When possible (in PostgreSQL or MySQL), SELECT FOR UPDATE SKIP LOCKED is used for more efficiency.
• All timestamps are stored as milliseconds since epoch (UTC timezone). This removes any confusion about timezones.