TaskScheduler — Python SDK

Async Python client for TaskScheduler. A Python service becomes a first-class participant in the same job system as your Kotlin services: it enqueues work, runs handlers, and shows up on the same dashboard with the same retry, cancellation and progress semantics.

from dataclasses import dataclass
from taskscheduler import Scheduler, SchedulerConfig, job_type

@job_type
@dataclass
class SendInvoice:
    order_id: int

async with Scheduler(SchedulerConfig(dsn="postgresql://scheduler:scheduler@localhost/scheduler")) as s:
    await s.enqueue(SendInvoice(order_id=42))

How it fits together

The SDK speaks the same wire protocol as the Kotlin client — it is not a proxy in front of it. PostgreSQL holds all job state; RabbitMQ carries nothing but a 16-byte job id as a delivery hint.

┌──────────────────────────┐        ┌──────────────────────────┐
│  scheduler-infra (Kotlin) │        │  your Python service      │
│  • owns the schema        │        │  • Scheduler.enqueue()    │
│  • outbox → RabbitMQ      │  PG +  │  • WorkerPool runs jobs   │
│  • recurring cron         │ Rabbit │  • heartbeats its lease   │
│  • orphan recovery        │◄──────►│                           │
│  • dashboard :8080        │        │                           │
└──────────────────────────┘        └──────────────────────────┘
             └────────► PostgreSQL ◄────────┘
             └────────► RabbitMQ   ◄────────┘

scheduler-infra is required. It owns the Flyway migrations and the background loops that neither client implements: publishing the outbox to RabbitMQ, firing cron definitions, recovering jobs whose worker died, and retention. This SDK verifies the schema version at startup and refuses to run against a database that is too old.

Install

pip install taskscheduler-client        # or: uv pip install taskscheduler-client

Requires Python 3.10+, and a RabbitMQ with the rabbitmq_delayed_message_exchange plugin enabled (the same requirement the Kotlin side has — it is how delays and retry backoff work).

Versioning. The client shares one version with the rest of the project: taskscheduler-client 0.7.0 is the client for scheduler-infra 0.7.0, and the two are released together. Run a client older than your infra and it may not know about a newer column; newer, and it fails fast on the schema check. CI enforces that pyproject.toml, taskscheduler.__version__ and Gradle's schedulerVersion agree.

On Windows, select the other event loop before starting anything — psycopg cannot run on the ProactorEventLoop that asyncio uses by default there:

if sys.platform == "win32":
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

The SDK raises a ConfigurationError naming this if you forget, rather than hanging on a connection-pool timeout.

Producing jobs

A payload is a plain dataclass. @job_type gives it a stable name that is stored in job.payload_type:

@job_type                       # -> "billing.jobs.SendInvoice"
@dataclass
class SendInvoice:
    order_id: int
    dry_run: bool = False

@job_type("billing.SendInvoice.v2")   # pin it before renaming or moving the class
@dataclass
class SendInvoiceV2:
    ...

# now
await scheduler.enqueue(SendInvoice(order_id=42), queue="billing", priority=7)

# at a moment
await scheduler.schedule_at(SendReminder(order_id=42), at=datetime(2026, 6, 1, 10, tzinfo=timezone.utc))
await scheduler.schedule_in(SendReminder(order_id=42), delay=timedelta(days=1))

# at most one active job per key
await scheduler.enqueue_once(f"sync-user-{user_id}", SyncUser(user_id))

# strictly in order
await scheduler.chain(ExtractData(), TransformData(), LoadData())

# after a fan-out finishes
a = await scheduler.enqueue(LoadProductCache())
b = await scheduler.enqueue(LoadUserCache())
await scheduler.enqueue_after(StartPricingEngine(), wait_for=[a, b])

# on a cron
await scheduler.recurring("nightly-rollup", "0 3 * * *", NightlyRollup(), timezone_name="Europe/Berlin")

Every enqueue writes the job row and its outbox row in one transaction, so a job becomes visible only if your surrounding business transaction commits.

Consuming jobs

from taskscheduler import HandlerRegistry, JobContext, RabbitConfig, WorkerConfig, WorkerPool

registry = HandlerRegistry()

@registry.handler(SendInvoice, retry_policy=ExponentialBackoff(max_attempts=5))
async def send_invoice(ctx: JobContext, job: SendInvoice) -> None:
    await billing.send(job.order_id, idempotency_key=str(ctx.job_id))

worker = WorkerPool(
    scheduler_config=SchedulerConfig(dsn=DSN, node_id="billing-1"),
    worker_config=WorkerConfig(node_id="billing-1").queue("billing", concurrency=8),
    rabbit_config=RabbitConfig(url=AMQP_URL, queues=["billing"]),
    registry=registry,
)

async with worker:
    await asyncio.Event().wait()      # run until the process is stopped

Class-based handlers work too, when you need on_final_failure or dependency injection:

class SendInvoiceHandler(JobHandler[SendInvoice]):
    payload = SendInvoice
    retry_policy = ExponentialBackoff(max_attempts=5)

    def __init__(self, billing: BillingClient) -> None:
        self._billing = billing

    async def execute(self, ctx: JobContext, job: SendInvoice) -> None:
        await self._billing.send(job.order_id)

    async def on_final_failure(self, ctx: JobContext, job: SendInvoice, error: BaseException) -> None:
        await alerts.page(f"invoice {job.order_id} failed permanently")

registry.register(SendInvoiceHandler(billing))

Progress and cancellation

@registry.handler(ReindexCatalog)
async def reindex(ctx: JobContext, job: ReindexCatalog) -> None:
    bar = ctx.progress_bar(len(job.product_ids))
    for product_id in job.product_ids:
        if await ctx.is_cancellation_requested():
            raise JobCancellationError()      # ends as CANCELLED, not FAILED
        await index(product_id)
        await bar.succeeded()

Progress writes are throttled to one per second, so calling them in a tight loop is fine. A cancelled job that ignores the flag is cancelled outright after WorkerConfig.cancel_grace_seconds.

Failing

Configuration

SchedulerConfig(
    dsn="postgresql://user:pass@host:5432/scheduler",   # or dsn_from_jdbc(...)
    node_id="billing-1",
    default_queue="default",
    default_max_attempts=3,
    default_timeout_seconds=300,
    default_retry_policy=ExponentialBackoff(max_attempts=3),
)

WorkerConfig(
    node_id="billing-1",
    node_tags=["eu-west"],
    heartbeat_interval_seconds=30,     # must be <= lock_duration / 3
    lock_duration_seconds=90,
    shutdown_timeout_seconds=30,
).queue("billing", concurrency=8, prefetch=8)

RabbitConfig(url="amqp://scheduler:scheduler@localhost:5672/", queues=["billing"])

Sharing POSTGRES_URL with the Kotlin services:

dsn = dsn_from_jdbc(os.environ["POSTGRES_URL"], os.environ["POSTGRES_USER"], os.environ["POSTGRES_PASSWORD"])

Running Python and Kotlin side by side

Both clients read and write the same tables, and the dashboard shows their jobs together. What does not cross the language boundary is the payload itself: a payload_type is a Python class name here and a Kotlin FQN there, and a worker that picks up a type it does not know marks the job FAILED rather than passing it on.

So give each language its own queues. Point Python handlers at python, ml, or whichever names you like, and keep Kotlin workers on theirs:

RabbitConfig(url=AMQP_URL, queues=["ml"])
WorkerConfig(node_id="ml-1").queue("ml", concurrency=4)

schedulerRabbitModule { queues = listOf("default", "ml") }   // infra must declare every queue
schedulerWorkerModule { queue("default", concurrency = 8) }  // but only consumes its own

The infra process needs every queue name in its schedulerRabbitModule.queues so the topology exists; it does not need to consume them.

If you do want the two languages to run each other's jobs, pin @job_type("<kotlin FQN>") and keep the JSON field names identical to the Kotlin data class — this SDK will encode and decode it, but nothing checks that the two definitions still agree.

Guarantees

At-least-once. A job can run twice — a lease expiring during a long GC pause, a broker redelivery, a network partition. ctx.job_id is stable across every attempt, so use it as the idempotency key for anything with side effects:

await payments.charge(order_id, idempotency_key=str(ctx.job_id))

Leases, not locks. A claimed job is held by locked_until, extended every heartbeat_interval_seconds. If this process dies, the lease lapses and infra re-enqueues the job — that is the recovery path, and it is why heartbeat_interval must stay at or below a third of lock_duration.

Schema evolution. Adding a field with a default is safe. Removing one is safe — unknown keys are ignored on decode. Renaming or retyping is not: version the payload (SendInvoiceV2) and keep both handlers until the old jobs have drained. A payload that cannot be decoded fails terminally without burning retries, since the stored bytes will never change.

Development

uv venv && uv pip install -e ".[dev]"
pytest tests/unit                                    # no infrastructure needed
ruff check src tests examples scripts && mypy src

Integration tests need a database with the migrations applied and a broker with the delayed-message plugin. The whole suite runs in CI on every change under clients/python (.github/workflows/python-client.yml); locally, bring the two up yourself. The commands below assume a checkout of the repository, run from clients/python:

docker run -d --name ts-pg -e POSTGRES_USER=scheduler -e POSTGRES_PASSWORD=scheduler \
  -e POSTGRES_DB=scheduler -p 5432:5432 postgres:16-alpine

docker build -t taskscheduler-rabbit ../../docker/rabbitmq
docker run -d --name ts-rabbit -e RABBITMQ_DEFAULT_USER=scheduler \
  -e RABBITMQ_DEFAULT_PASS=scheduler -p 5672:5672 taskscheduler-rabbit

python scripts/apply_migrations.py "postgresql://scheduler:scheduler@localhost:5432/scheduler"

TASKSCHEDULER_TEST_DSN="postgresql://scheduler:scheduler@localhost:5432/scheduler" \
TASKSCHEDULER_TEST_AMQP="amqp://scheduler:scheduler@localhost:5672/" \
  pytest tests/integration

scripts/apply_migrations.py replays the project's Flyway migrations without a JVM, so the tests don't need a built scheduler-infra image. It is a development shortcut — in a real deployment scheduler-infra owns the schema.

No Kotlin process runs during the tests: an outbox_pump fixture stands in for the infra leader that would otherwise drain the outbox into RabbitMQ. Tests that expect a job to be delivered more than once (retries, DAG promotions, paused-type redelivery) request it.

Alternatively docker compose up -d at the repo root brings up Postgres, RabbitMQ and a real scheduler-infra — closer to production, but it needs the Gradle-built image (./gradlew :standalone-runner:dockerImage).