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ORCHER Python SDK

Crash-proof workflows, written as plain async Python.


PyPI Python versions CI Apache 2.0

Write async functions; ORCHER journals each step and resumes interrupted runs where they stopped.

  • Durable: every step journaled; crashed runs resume on another worker
  • Plain Python: @workflow and @task on async functions, no DSL
  • Deterministic replay: step ids, time and randomness stay the same on every replay
  • Smart retries: per-task policies and never-retry error types
  • Timers and events: sleep for days or wait for an outside event
  • Child workflows: compose and run workflows in parallel
  • Actors: stateful objects with a single writer per key
  • Testing: run workflows in memory with mocks and a fake clock
  • Multi-tenant: API keys, organizations and namespaces built in
  • Native core: the engine protocol and state machine run in Rust

Install

pip install orcher-sdk

The package installs as orcher:

import orcher

Quick start

Decorate a task and a workflow that calls it. Both register themselves when their module is imported, and the worker runs everything registered:

import asyncio
import os

from orcher import TaskContext, Worker, WorkflowContext, task, workflow


@task(name="send-confirmation", retry=3)
async def send_confirmation(ctx: TaskContext, order_id: str, email: str) -> str:
    return f"sent confirmation for {order_id} to {email}"


@workflow(name="confirm-order")
async def confirm_order(ctx: WorkflowContext, order_id: str, email: str) -> str:
    return await ctx.execute_task(send_confirmation, order_id=order_id, email=email)


async def main() -> None:
    worker = (
        Worker.builder()
        .server_url("http://localhost:50051")
        .namespace("default")
        .task_queue("orders")
        .api_key(os.environ.get("ORCHER_API_KEY"))  # for an engine that requires one
        .build()
    )
    await worker.run()


if __name__ == "__main__":
    asyncio.run(main())

Tasks that need clients or connections can be methods of a @tasks class instead; hand the worker a built instance with worker.register_task_instance().

Start it from any process and wait for the result. A dict input reaches the workflow as keyword arguments:

import os

from orcher import Client, ClientConfig

config = ClientConfig(
    server_url="http://localhost:50051",
    api_key=os.environ.get("ORCHER_API_KEY"),  # for an engine that requires one
)
async with Client(config) as client:
    handle = await client.start_workflow(
        "confirm-order",
        task_queue="orders",
        args=({"order_id": "order-1", "email": "ada@example.com"},),
    )
    receipt = await handle.result()

Guide

Timers: sleep for minutes or months

A timer is recorded by the engine, so no worker is busy while it runs, and a restart doesn't reset it:

from datetime import timedelta

from orcher import WorkflowContext, workflow


@workflow(name="trial")
async def trial(ctx: WorkflowContext, email: str) -> None:
    await ctx.sleep(timedelta(days=14))
    await ctx.execute_task(send_trial_ended, email=email)
Events: wait for something outside the workflow

A workflow can park until a named event arrives, optionally with a deadline:

from datetime import timedelta

from orcher import WorkflowContext, workflow


@workflow(name="approval")
async def approval(ctx: WorkflowContext, request_id: str) -> str:
    try:
        approved = await ctx.wait_for_event_with_timeout("approved", timedelta(days=3))
    except TimeoutError:
        return f"{request_id} expired"
    return f"{request_id} approved" if approved else f"{request_id} rejected"

Send the event from a client:

handle = await client.get_workflow("approval-42")
await handle.send_event("approved", True)
Child workflows: compose workflows from workflows
from orcher import WorkflowContext, workflow


@workflow(name="ship-order")
async def ship_order(ctx: WorkflowContext, order_id: str) -> str:
    label = await ctx.execute_child_workflow("print-label", args=(order_id,))
    return f"{order_id} shipped with {label}"

ctx.start_child_workflow() returns a handle instead, to run several children at once and collect their results later.

Retries: decide which failures are worth retrying

A failure's type is the exception's class name (CardDeclined, not payments.CardDeclined). List the types never to retry in the task's policy. An exception whose non_retryable attribute is true is never retried, whatever the policy allows:

from orcher import RetryPolicy, TaskContext, task


class CardDeclined(Exception):
    pass


class AccountClosed(Exception):
    non_retryable = True


@task(
    name="charge",
    retry_policy=RetryPolicy(max_attempts=5, non_retryable_error_types=["CardDeclined"]),
)
async def charge(ctx: TaskContext, order_id: str) -> str:
    # Runs once:
    raise CardDeclined(f"card declined for {order_id}")
    # Also runs once, listed or not:
    # raise AccountClosed(f"account closed for {order_id}")

Any other exception is retried. non_retryable can also be set on a single instance before it is raised.

Large payloads: what fits, and what happens when it doesn't

Workers and clients send and receive gRPC messages of up to 32 MiB; set ORCHER_MAX_MESSAGE_BYTES to change that. The engine accepts one payload (an input, a result, an event) of up to 8 MiB unless configured otherwise. A task whose result is too large fails straight away with a PayloadTooLarge failure that says how large it was, and is not retried: store large data elsewhere and pass a reference.

Time and randomness: the replay-safe way

ctx.time reads the time the engine recorded, and ctx.random is seeded from the run, so both give the same answer every time the workflow is replayed:

from orcher import WorkflowContext, workflow


@workflow(name="invoice")
async def invoice(ctx: WorkflowContext, customer: str) -> str:
    number = ctx.random.uuid()
    issued_at = ctx.time.now()
    return f"invoice {number} for {customer}, issued at {issued_at.isoformat()}"
Testing: run workflows in memory

orcher.testing runs a workflow without an engine, with its tasks mocked by name. With pytest and pytest-asyncio:

import pytest

from orcher.testing import TestWorkflowEnvironment

from orders import confirm_order


@pytest.mark.asyncio
async def test_confirms_the_order() -> None:
    env = TestWorkflowEnvironment()
    env.mock_task("send-confirmation").returns("sent")

    receipt = await env.execute_workflow(
        confirm_order, {"order_id": "order-1", "email": "ada@example.com"}
    )

    assert receipt == "sent"
    env.assert_task_called_with(
        "send-confirmation", {"order_id": "order-1", "email": "ada@example.com"}
    )

Mocks can also raise, return a sequence or compute their result, and the environment controls the clock.


Contributing

Issues and pull requests are welcome. See CONTRIBUTING.md for how to build, test and propose a change.

License

Licensed under the Apache License, Version 2.0.

"Python" and the Python logos are trademarks or registered trademarks of the Python Software Foundation, shown here to indicate the language this SDK is for.

Metadata

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