Cross language enqueueing

Inserting jobs from Python

River supports inserting jobs from python and have them worked in Go, a feature that may be desirable in performance sensitive cases so that jobs can take advantage of Go's considerably faster runtime speed.

Insertion is supported through SQLAlchemy.

Basic usage

Your project should bundle the riverqueue package in its dependencies. How to go about this will depend on your toolchain, but for example in Rye, it'd look like:

rye add riverqueue

Initialize a client with:

import riverqueue
from riverqueue.driver import riversqlalchemy

engine = sqlalchemy.create_engine("postgresql://...")
client = riverqueue.Client(riversqlalchemy.Driver(engine))

Define a job and insert it:

@dataclass
class SortArgs:
    strings: list[str]

    kind: str = "sort"

    def to_json(self) -> str:
        return json.dumps({"strings": self.strings})

insert_res = client.insert(
    SortArgs(strings=["whale", "tiger", "bear"]),
)
insert_res.job # inserted job row

Job args should comply with the riverqueue.JobArgs protocol:

class JobArgs(Protocol):
    kind: str

    def to_json(self) -> str:
        pass
  • kind is a unique string that identifies them the job in the database, and which a Go worker will recognize.
  • to_json() defines how the job will serialize to JSON, which of course will have to be parseable as an object in Go.

They may also respond to insert_opts() with an instance of InsertOpts to define insertion options that'll be used for all jobs of the kind.

We recommend using dataclasses for job args since they should ideally be minimal sets of primitive properties with little other embellishment, and dataclasses provide a succinct way of accomplishing this.

Insertion options

Inserts take an insert_opts parameter to customize features of the inserted job:

insert_res = client.insert(
    SortArgs(strings=["whale", "tiger", "bear"]),
    insert_opts=riverqueue.InsertOpts(
        max_attempts=17,
        priority=3,
        queue="my_queue",
        tags=["custom"]
    ),
)

Inserting unique jobs

Unique jobs are supported through InsertOpts.unique_opts(), and can be made unique by args, period, queue, and state. If a job matching unique properties is found on insert, the insert is skipped and the existing job returned.

insert_res = client.insert(
    SortArgs(strings=["whale", "tiger", "bear"]),
    insert_opts=riverqueue.InsertOpts(
        unique_opts=riverqueue.UniqueOpts(
            by_args=True,
            by_period=15*60,
            by_queue=True,
            by_state=[riverqueue.JobState.AVAILABLE]
        )
    ),
)

# contains either a newly inserted job, or an existing one if insertion was skipped
insert_res.job

# true if insertion was skipped
insert_res.unique_skipped_as_duplicated

Custom advisory lock prefix

Unique job insertion takes a Postgres advisory lock to make sure that its uniqueness check still works even if two conflicting insert operations are occurring in parallel. Postgres advisory locks share a global 64-bit namespace, which is a large enough space that it's unlikely for two advisory locks to ever conflict, but to guarantee that River's advisory locks never interfere with an application's, River can be configured with a 32-bit advisory lock prefix which it will use for all its locks:

client = riverqueue.Client(riversqlalchemy.Driver(engine), advisory_lock_prefix=123456)

Doing so has the downside of leaving only 32 bits for River's locks (64 bits total - 32-bit prefix), making them somewhat more likely to conflict with each other.

Inserting jobs in bulk

Use #insert_many to bulk insert jobs as a single operation for improved efficiency:

num_inserted = client.insert_many([
    SimpleArgs(job_num=1),
    SimpleArgs(job_num=2)
])

Or with InsertManyParams, which may include insertion options:

num_inserted = client.insert_many([
    InsertManyParams(args=SimpleArgs.new(job_num=1), insert_opts=riverqueue.InsertOpts(max_attempts=5)),
    InsertManyParams(args=SimpleArgs.new(job_num=2), insert_opts=riverqueue.InsertOpts(queue="high_priority"))
])

Inserting in a transaction

To insert jobs in a transaction, open one in your driver, and pass it as the first argument to insert_tx() or insert_many_tx():

with engine.begin() as session:
    insert_res = client.insert_tx(
        session,
        SortArgs(strings=["whale", "tiger", "bear"]),
    )

Asynchronous I/O (asyncio)

The package supports River's asyncio (asynchronous I/O) through an alternate AsyncClient and riversqlalchemy.AsyncDriver. You'll need to make sure to use SQLAlchemy's alternative async engine and an asynchronous Postgres driver like asyncpg, but otherwise usage looks very similar to use without async:

engine = sqlalchemy.ext.asyncio.create_async_engine("postgresql+asyncpg://...")
client = riverqueue.AsyncClient(riversqlalchemy.AsyncDriver(engine))

insert_res = await client.insert(
    SortArgs(strings=["whale", "tiger", "bear"]),
)

With a transaction:

async with engine.begin() as session:
    insert_res = await client.insert_tx(
        session,
        SortArgs(strings=["whale", "tiger", "bear"]),
    )

MyPy and type checking

The package exports a py.typed file to indicate that it's typed, so you should be able to use MyPy to include it in static analysis.

Drivers

SQLAlchemy

Our read is that SQLAlchemy is the dominant ORM in the Python ecosystem, so it's the only driver available for River. Under the hood of SQLAlchemy, projects will also need a Postgres driver like psycopg2 or asyncpg (for async).

River's driver system should enable integration with other ORMs, so let us know if there's a good reason you need one, and we'll consider it.

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