village-temporalio 0.1b2

Temporal.io Python SDK

Temporal Python SDK

Temporal is a distributed, scalable, durable, and highly available orchestration engine used to execute asynchronous long-running business logic in a scalable and resilient way.

"Temporal Python SDK" is the framework for authoring workflows and activities using the Python programming language.

Also see:

• Code Samples
• API Documentation
• Application Development Guide

In addition to features common across all Temporal SDKs, the Python SDK also has the following interesting features:

Type Safe

This library uses the latest typing and MyPy support with generics to ensure all calls can be typed. For example, starting a workflow with an int parameter when it accepts a str parameter would cause MyPy to fail.

Different Activity Types

The activity worker has been developed to work with async def, threaded, and multiprocess activities. While async def activities are the easiest and recommended, care has been taken to make heartbeating and cancellation also work across threads/processes.

Custom asyncio Event Loop

The workflow implementation basically turns async def functions into workflows backed by a distributed, fault-tolerant event loop. This means task management, sleep, cancellation, etc have all been developed to seamlessly integrate with asyncio concepts.

⚠️ UNDER DEVELOPMENT

The Python SDK is under development. There are no compatibility guarantees at this time.

Quick Start

Installation

Install the temporalio package from PyPI.

These steps can be followed to use with a virtual environment and pip:

• Create a virtual environment
• Update pip - python -m pip install -U pip
  • Needed because older versions of pip may not pick the right wheel
• Install Temporal SDK - python -m pip install temporalio

The SDK is now ready for use. To build from source, see "Building" near the end of this documentation.

NOTE: This README is for the current branch and not necessarily what's released on PyPI.

Implementing a Workflow

Create the following script at run_worker.py:

import asyncio
from datetime import datetime, timedelta
from temporalio import workflow, activity
from temporalio.client import Client
from temporalio.worker import Worker

@activity.defn
async def say_hello(name: str) -> str:
    return f"Hello, {name}!"

@workflow.defn
class SayHello:
    @workflow.run
    async def run(self, name: str) -> str:
        return await workflow.execute_activity(
            say_hello, name, schedule_to_close_timeout=timedelta(seconds=5)
        )

async def main():
    # Create client connected to server at the given address
    client = await Client.connect("localhost:7233")

    # Run the worker
    worker = Worker(client, task_queue="my-task-queue", workflows=[SayHello], activities=[say_hello])
    await worker.run()

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

Assuming you have a Temporal server running on localhost, this will run the worker:

python run_worker.py

Running a Workflow

Create the following script at run_workflow.py:

import asyncio
from temporalio.client import Client

# Import the workflow from the previous code
from run_worker import SayHello

async def main():
    # Create client connected to server at the given address
    client = await Client.connect("localhost:7233")

    # Execute a workflow
    result = await client.execute_workflow(SayHello.run, "my name", id="my-workflow-id", task_queue="my-task-queue")

    print(f"Result: {result}")

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

Assuming you have run_worker.py running from before, this will run the workflow:

python run_workflow.py

The output will be:

Result: Hello, my-name!

Usage

Client

A client can be created and used to start a workflow like so:

from temporalio.client import Client

async def main():
    # Create client connected to server at the given address and namespace
    client = await Client.connect("localhost:7233", namespace="my-namespace")

    # Start a workflow
    handle = await client.start_workflow(MyWorkflow.run, "some arg", id="my-workflow-id", task_queue="my-task-queue")

    # Wait for result
    result = await handle.result()
    print(f"Result: {result}")

Some things to note about the above code:

• A Client does not have an explicit "close"
• To enable TLS, the tls argument to connect can be set to True or a TLSConfig object
• A single positional argument can be passed to start_workflow. If there are multiple arguments, only the non-type-safe form of start_workflow can be used (i.e. the one accepting a string workflow name) and it must be in the args keyword argument.
• The handle represents the workflow that was started and can be used for more than just getting the result
• Since we are just getting the handle and waiting on the result, we could have called client.execute_workflow which does the same thing
• Clients can have many more options not shown here (e.g. data converters and interceptors)
• A string can be used instead of the method reference to call a workflow by name (e.g. if defined in another language)

Clients also provide a shallow copy of their config for use in making slightly different clients backed by the same connection. For instance, given the client above, this is how to have a client in another namespace:

config = client.config()
config["namespace"] = "my-other-namespace"
other_ns_client = Client(**config)

Data Conversion

Data converters are used to convert raw Temporal payloads to/from actual Python types. A custom data converter of type temporalio.converter.DataConverter can be set via the data_converter client parameter. Data converters are a combination of payload converters and payload codecs. The former converts Python values to/from serialized bytes, and the latter converts bytes to bytes (e.g. for compression or encryption).

The default data converter supports converting multiple types including:

• None
• bytes
• google.protobuf.message.Message - As JSON when encoding, but has ability to decode binary proto from other languages
• Anything that can be converted to JSON including:
  • Anything that json.dump supports natively
  • dataclasses
  • Iterables including ones JSON dump may not support by default, e.g. set
  • Any class with a dict() method and a static parse_obj() method, e.g. Pydantic models
  • IntEnum based enumerates

For converting from JSON, the workflow/activity type hint is taken into account to convert to the proper type. Care has been taken to support all common typings including Optional, Union, all forms of iterables and mappings, NewType, etc in addition to the regular JSON values mentioned before.

Users are strongly encouraged to use a single dataclass for parameter and return types so fields with defaults can be easily added without breaking compatibility.

Workers

Workers host workflows and/or activities. Here's how to run a worker:

import asyncio
import logging
from temporalio.client import Client
from temporalio.worker import Worker
# Import your own workflows and activities
from my_workflow_package import MyWorkflow, my_activity

async def run_worker(stop_event: asyncio.Event):
    # Create client connected to server at the given address
    client = await Client.connect("localhost:7233", namespace="my-namespace")

    # Run the worker until the event is set
    worker = Worker(client, task_queue="my-task-queue", workflows=[MyWorkflow], activities=[my_activity])
    async with worker:
        await stop_event.wait()

Some things to note about the above code:

• This creates/uses the same client that is used for starting workflows
• While this example accepts a stop event and uses async with, run() and shutdown() may be used instead
• Workers can have many more options not shown here (e.g. data converters and interceptors)

Workflows

Definition

Workflows are defined as classes decorated with @workflow.defn. The method invoked for the workflow is decorated with @workflow.run. Methods for signals and queries are decorated with @workflow.signal and @workflow.query respectively. Here's an example of a workflow:

import asyncio
from dataclasses import dataclass
from datetime import timedelta
from temporalio import activity, workflow
from temporalio.client import Client
from temporalio.worker import Worker

@dataclass
class GreetingInfo:
    salutation: str = "Hello"
    name: str = "<unknown>"

@workflow.defn
class GreetingWorkflow:
    def __init__() -> None:
        self._current_greeting = "<unset>"
        self._greeting_info = GreetingInfo()
        self._greeting_info_update = asyncio.Event()
        self._complete = asyncio.Event()

    @workflow.run
    async def run(self, name: str) -> str:
        self._greeting_info.name = name
        while True:
            # Store greeting
            self._current_greeting = await workflow.execute_activity(
                create_greeting_activity,
                self._greeting_info,
                start_to_close_timeout=timedelta(seconds=5),
            )
            workflow.logger.debug("Greeting set to %s", self._current_greeting)

            # Wait for salutation update or complete signal (this can be
            # cancelled)
            await asyncio.wait(
                [self._greeting_info_update.wait(), self._complete.wait()],
                return_when=asyncio.FIRST_COMPLETED,
            )
            if self._complete.is_set():
                return self._current_greeting
            self._greeting_info_update.clear()

    @workflow.signal
    async def update_salutation(self, salutation: str) -> None:
        self._greeting_info.salutation = salutation
        self._greeting_info_update.set()

    @workflow.signal
    async def complete_with_greeting(self) -> None:
        self._complete.set()

    @workflow.query
    async def current_greeting(self) -> str:
        return self._current_greeting

@activity.defn
async def create_greeting_activity(info: GreetingInfo) -> str:
    return f"{info.salutation}, {info.name}!"

Some things to note about the above code:

• This workflow continually updates the queryable current greeting when signalled and can complete with the greeting on a different signal
• Workflows are always classes and must have a single @workflow.run which is an async def function
• Workflow code must be deterministic. This means no threading, no randomness, no external calls to processes, no network IO, and no global state mutation. All code must run in the implicit asyncio event loop and be deterministic.
• @activity.defn is explained in a later section. For normal simple string concatenation, this would just be done in the workflow. The activity is for demonstration purposes only.
• workflow.execute_activity(create_greeting_activity, ... is actually a typed signature, and MyPy will fail if the self._greeting_info parameter is not a GreetingInfo

Here are the decorators that can be applied:

• @workflow.defn - Defines a workflow class
  • Must be defined on the class given to the worker (ignored if present on a base class)
  • Can have a name param to customize the workflow name, otherwise it defaults to the unqualified class name
• @workflow.run - Defines the primary workflow run method
  • Must be defined on the same class as @workflow.defn, not a base class (but can also be defined on the same method of a base class)
  • Exactly one method name must have this decorator, no more or less
  • Must be defined on an async def method
  • The method's arguments are the workflow's arguments
  • The first parameter must be self, followed by positional arguments. Best practice is to only take a single argument that is an object/dataclass of fields that can be added to as needed.
• @workflow.signal - Defines a method as a signal
  • Can be defined on an async or non-async function at any hierarchy depth, but if decorated method is overridden, the override must also be decorated
  • The method's arguments are the signal's arguments
  • Can have a name param to customize the signal name, otherwise it defaults to the unqualified method name
  • Can have dynamic=True which means all otherwise unhandled signals fall through to this. If present, cannot have name argument, and method parameters must be self, a string signal name, and a *args varargs param.
  • Non-dynamic method can only have positional arguments. Best practice is to only take a single argument that is an object/dataclass of fields that can be added to as needed.
  • Return value is ignored
• @workflow.query - Defines a method as a query
  • All the same constraints as @workflow.signal but should return a value
  • Temporal queries should never mutate anything in the workflow

Running

To start a locally-defined workflow from a client, you can simply reference its method like so:

from temporalio.client import Client
from my_workflow_package import GreetingWorkflow

async def create_greeting(client: Client) -> str:
    # Start the workflow
    handle = await client.start_workflow(GreetingWorkflow.run, "my name", id="my-workflow-id", task_queue="my-task-queue")
    # Change the salutation
    await handle.signal(GreetingWorkflow.update_salutation, "Aloha")
    # Tell it to complete
    await handle.signal(GreetingWorkflow.complete_with_greeting)
    # Wait and return result
    return await handle.result()

Some things to note about the above code:

• This uses the GreetingWorkflow from the previous section
• The result of calling this function is "Aloha, my name!"
• id and task_queue are required for running a workflow
• client.start_workflow is typed, so MyPy would fail if "my name" were something besides a string
• handle.signal is typed, so MyPy would fail if "Aloha" were something besides a string or if we provided a parameter to the parameterless complete_with_greeting
• handle.result is typed to the workflow itself, so MyPy would fail if we said this create_greeting returned something besides a string

Invoking Activities

• Activities are started with non-async workflow.start_activity() which accepts either an activity function reference or a string name.
• A single argument to the activity is positional. Multiple arguments are not supported in the type-safe form of start/execute activity and must be supplied via the args keyword argument.
• Activity options are set as keyword arguments after the activity arguments. At least one of start_to_close_timeout or schedule_to_close_timeout must be provided.
• The result is an activity handle which is an asyncio.Task and supports basic task features
• An async workflow.execute_activity() helper is provided which takes the same arguments as workflow.start_activity() and awaits on the result. This should be used in most cases unless advanced task capabilities are needed.
• Local activities work very similarly except the functions are workflow.start_local_activity() and workflow.execute_local_activity()
• Activities can be methods of a class. Invokers should use workflow.start_activity_method(), workflow.execute_activity_method(), workflow.start_local_activity_method(), and workflow.execute_local_activity_method() instead.
• Activities can callable classes (i.e. that define __call__). Invokers should use workflow.start_activity_class(), workflow.execute_activity_class(), workflow.start_local_activity_class(), and workflow.execute_local_activity_class() instead.

Invoking Child Workflows

• Child workflows are started with async workflow.start_child_workflow() which accepts either a workflow run method reference or a string name. The arguments to the workflow are positional.
• A single argument to the child workflow is positional. Multiple arguments are not supported in the type-safe form of start/execute child workflow and must be supplied via the args keyword argument.
• Child workflow options are set as keyword arguments after the arguments. At least id must be provided.
• The await of the start does not complete until the start has been accepted by the server
• The result is a child workflow handle which is an asyncio.Task and supports basic task features. The handle also has some child info and supports signalling the child workflow
• An async workflow.execute_child_workflow() helper is provided which takes the same arguments as workflow.start_child_workflow() and awaits on the result. This should be used in most cases unless advanced task capabilities are needed.

Timers

• A timer is represented by normal asyncio.sleep()
• Timers are also implicitly started on any asyncio calls with timeouts (e.g. asyncio.wait_for)
• Timers are Temporal server timers, not local ones, so sub-second resolution rarely has value

Conditions

• workflow.wait_condition is an async function that doesn't return until a provided callback returns true
• A timeout can optionally be provided which will throw a asyncio.TimeoutError if reached (internally backed by asyncio.wait_for which uses a timer)

Asyncio and Cancellation

Workflows are backed by a custom asyncio event loop. This means many of the common asyncio calls work as normal. Some asyncio features are disabled such as:

• Thread related calls such as to_thread(), run_coroutine_threadsafe(), loop.run_in_executor(), etc
• Calls that alter the event loop such as loop.close(), loop.stop(), loop.run_forever(), loop.set_task_factory(), etc
• Calls that use a specific time such as loop.call_at()
• Calls that use anything external such as networking, subprocesses, disk IO, etc

Cancellation is done the same way as asyncio. Specifically, a task can be requested to be cancelled but does not necessarily have to respect that cancellation immediately. This also means that asyncio.shield() can be used to protect against cancellation. The following tasks, when cancelled, perform a Temporal cancellation:

• Activities - when the task executing an activity is cancelled, a cancellation request is sent to the activity
• Child workflows - when the task starting or executing a child workflow is cancelled, a cancellation request is sent to cancel the child workflow
• Timers - when the task executing a timer is cancelled (whether started via sleep or timeout), the timer is cancelled

When the workflow itself is requested to cancel, Task.cancel is called on the main workflow task. Therefore, asyncio.CancelledError can be caught in order to handle the cancel gracefully.

Workflows follow asyncio cancellation rules exactly which can cause confusion among Python developers. Cancelling a task doesn't always cancel the thing it created. For example, given task = asyncio.create_task(workflow.start_child_workflow(..., calling task.cancel does not cancel the child workflow, it only cancels the starting of it, which has no effect if it has already started. However, cancelling the result of handle = await workflow.start_child_workflow(... or task = asyncio.create_task(workflow.execute_child_workflow(... does cancel the child workflow.

Also, due to Temporal rules, a cancellation request is a state not an event. Therefore, repeated cancellation requests are not delivered, only the first. If the workflow chooses swallow a cancellation, it cannot be requested again.

Workflow Utilities

While running in a workflow, in addition to features documented elsewhere, the following items are available from the temporalio.workflow package:

• continue_as_new() - Async function to stop the workflow immediately and continue as new
• info() - Returns information about the current workflow
• logger - A logger for use in a workflow (properly skips logging on replay)
• now() - Returns the "current time" from the workflow's perspective

Exceptions

• Workflows can raise exceptions to fail the workflow
• Using temporalio.exceptions.ApplicationError, exceptions can be marked as non-retryable or include details

External Workflows

• workflow.get_external_workflow_handle() inside a workflow returns a handle to interact with another workflow
• workflow.get_external_workflow_handle_for() can be used instead for a type safe handle
• await handle.signal() can be called on the handle to signal the external workflow
• await handle.cancel() can be called on the handle to send a cancel to the external workflow

Testing

Workflow testing can be done in an integration-test fashion against a real server, however it is hard to simulate timeouts and other long time-based code. Using the time-skipping workflow test environment can help there.

The time-skipping temporalio.testing.WorkflowEnvironment can be created via the static async start_time_skipping(). This internally downloads the Temporal time-skipping test server to a temporary directory if it doesn't already exist, then starts the test server which has special APIs for skipping time.

Automatic Time Skipping

Anytime a workflow result is waited on, the time-skipping server automatically advances to the next event it can. To manually advance time before waiting on the result of a workflow, the WorkflowEnvironment.sleep method can be used.

Here's a simple example of a workflow that sleeps for 24 hours:

import asyncio
from temporalio import workflow

@workflow.defn
class WaitADayWorkflow:
    @workflow.run
    async def run(self) -> str:
        await asyncio.sleep(24 * 60 * 60)
        return "all done"

An integration test of this workflow would be way too slow. However the time-skipping server automatically skips to the next event when we wait on the result. Here's a test for that workflow:

from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker

async def test_wait_a_day_workflow():
    async with await WorkflowEnvironment.start_time_skipping() as env:
        async with Worker(env.client, task_queue="tq1", workflows=[WaitADayWorkflow]):
            assert "all done" == await env.client.execute_workflow(WaitADayWorkflow.run, id="wf1", task_queue="tq1")

That test will run almost instantly. This is because by calling execute_workflow on our client, we have asked the environment to automatically skip time as much as it can (basically until the end of the workflow or until an activity is run).

To disable automatic time-skipping while waiting for a workflow result, run code inside a with env.auto_time_skipping_disabled(): block.

Manual Time Skipping

Until a workflow is waited on, all time skipping in the time-skipping environment is done manually via WorkflowEnvironment.sleep.

Here's workflow that waits for a signal or times out:

import asyncio
from temporalio import workflow

@workflow.defn
class SignalWorkflow:
    def __init__(self) -> None:
        self.signal_received = False

    @workflow.run
    async def run(self) -> str:
        # Wait for signal or timeout in 45 seconds
        try:
            await workflow.wait_condition(lambda: self.signal_received, timeout=45)
            return "got signal"
        except asyncio.TimeoutError:
            return "got timeout"

    @workflow.signal
    def some_signal(self) -> None:
        self.signal_received = True

To test a normal signal, you might:

from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker

async def test_signal_workflow():
    async with await WorkflowEnvironment.start_time_skipping() as env:
        async with Worker(env.client, task_queue="tq1", workflows=[SignalWorkflow]):
            # Start workflow, send signal, check result
            handle = await env.client.start_workflow(SignalWorkflow.run, id="wf1", task_queue="tq1")
            await handle.signal(SignalWorkflow.some_signal)
            assert "got signal" == await handle.result()

But how would you test the timeout part? Like so:

from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker

async def test_signal_workflow_timeout():
    async with await WorkflowEnvironment.start_time_skipping() as env:
        async with Worker(env.client, task_queue="tq1", workflows=[SignalWorkflow]):
            # Start workflow, advance time past timeout, check result
            handle = await env.client.start_workflow(SignalWorkflow.run, id="wf1", task_queue="tq1")
            await env.sleep(50)
            assert "got timeout" == await handle.result()

Also, the current time of the workflow environment can be obtained via the async WorkflowEnvironment.get_current_time method.

Mocking Activities

Activities are just functions decorated with @activity.defn. Simply write different ones and pass those to the worker to have different activities called during the test.

Activities

Definition

Activities are decorated with @activity.defn like so:

from temporalio import activity

@activity.defn
async def say_hello_activity(name: str) -> str:
    return f"Hello, {name}!"

Some things to note about activity definitions:

• The say_hello_activity is async which is the recommended activity type (see "Types of Activities" below)
• A custom name for the activity can be set with a decorator argument, e.g. @activity.defn(name="my activity")
• Long running activities should regularly heartbeat and handle cancellation
• Activities can only have positional arguments. Best practice is to only take a single argument that is an object/dataclass of fields that can be added to as needed.
• Activities can be defined on methods instead of top-level functions. This allows the instance to carry state that an activity may need (e.g. a DB connection). The instance method should be what is registered with the worker.
• Activities can also be defined on callable classes (i.e. classes with __call__). An instance of the class should be what is registered with the worker.

Types of Activities

There are 3 types of activity callables accepted and described below: asynchronous, synchronous multithreaded, and synchronous multiprocess/other. Only positional parameters are allowed in activity callables.

Asynchronous Activities

Asynchronous activities, i.e. functions using async def, are the recommended activity type. When using asynchronous activities no special worker parameters are needed.

Cancellation for asynchronous activities is done via asyncio.Task.cancel. This means that asyncio.CancelledError will be raised (and can be caught, but it is not recommended). An activity must heartbeat to receive cancellation and there are other ways to be notified about cancellation (see "Activity Context" and "Heartbeating and Cancellation" later).

Synchronous Activities

Synchronous activities, i.e. functions that do not have async def, can be used with workers, but the activity_executor worker parameter must be set with a concurrent.futures.Executor instance to use for executing the activities.

Cancellation for synchronous activities is done in the background and the activity must choose to listen for it and react appropriately. An activity must heartbeat to receive cancellation and there are other ways to be notified about cancellation (see "Activity Context" and "Heartbeating and Cancellation" later).

Note, all calls from an activity to functions in the temporalio.activity package are powered by contextvars. Therefore, new threads starting inside of activities must copy_context() and then .run() manually to ensure temporalio.activity calls like heartbeat still function in the new threads.

Synchronous Multithreaded Activities

If activity_executor is set to an instance of concurrent.futures.ThreadPoolExecutor then the synchronous activities are considered multithreaded activities. Besides activity_executor, no other worker parameters are required for synchronous multithreaded activities.

Synchronous Multiprocess/Other Activities

If activity_executor is set to an instance of concurrent.futures.Executor that is not concurrent.futures.ThreadPoolExecutor, then the synchronous activities are considered multiprocess/other activities.

These require special primitives for heartbeating and cancellation. The shared_state_manager worker parameter must be set to an instance of temporalio.worker.SharedStateManager. The most common implementation can be created by passing a multiprocessing.managers.SyncManager (i.e. result of multiprocessing.managers.Manager()) to temporalio.worker.SharedStateManager.create_from_multiprocessing().

Also, all of these activity functions must be "picklable".

Activity Context

During activity execution, an implicit activity context is set as a context variable. The context variable itself is not visible, but calls in the temporalio.activity package make use of it. Specifically:

• in_activity() - Whether an activity context is present
• info() - Returns the immutable info of the currently running activity
• heartbeat(*details) - Record a heartbeat
• is_cancelled() - Whether a cancellation has been requested on this activity
• wait_for_cancelled() - async call to wait for cancellation request
• wait_for_cancelled_sync(timeout) - Synchronous blocking call to wait for cancellation request
• is_worker_shutdown() - Whether the worker has started graceful shutdown
• wait_for_worker_shutdown() - async call to wait for start of graceful worker shutdown
• wait_for_worker_shutdown_sync(timeout) - Synchronous blocking call to wait for start of graceful worker shutdown
• raise_complete_async() - Raise an error that this activity will be completed asynchronously (i.e. after return of the activity function in a separate client call)

With the exception of in_activity(), if any of the functions are called outside of an activity context, an error occurs. Synchronous activities cannot call any of the async functions.

Heartbeating and Cancellation

In order for an activity to be notified of cancellation requests, they must invoke temporalio.activity.heartbeat(). It is strongly recommended that all but the fastest executing activities call this function regularly. "Types of Activities" has specifics on cancellation for asynchronous and synchronous activities.

In addition to obtaining cancellation information, heartbeats also support detail data that is persisted on the server for retrieval during activity retry. If an activity calls temporalio.activity.heartbeat(123, 456) and then fails and is retried, temporalio.activity.info().heartbeat_details will return an iterable containing 123 and 456 on the next run.

Worker Shutdown

An activity can react to a worker shutdown. Using is_worker_shutdown or one of the wait_for_worker_shutdown functions an activity can react to a shutdown.

When the graceful_shutdown_timeout worker parameter is given a datetime.timedelta, on shutdown the worker will notify activities of the graceful shutdown. Once that timeout has passed (or if wasn't set), the worker will perform cancellation of all outstanding activities.

The shutdown() invocation will wait on all activities to complete, so if a long-running activity does not at least respect cancellation, the shutdown may never complete.

Testing

Unit testing an activity or any code that could run in an activity is done via the temporalio.testing.ActivityEnvironment class. Simply instantiate this and any callable + params passed to run will be invoked inside the activity context. The following are attributes/methods on the environment that can be used to affect calls activity code might make to functions on the temporalio.activity package.

• info property can be set to customize what is returned from activity.info()
• on_heartbeat property can be set to handle activity.heartbeat() calls
• cancel() can be invoked to simulate a cancellation of the activity
• worker_shutdown() can be invoked to simulate a worker shutdown during execution of the activity

Workflow Replay

Given a workflow's history, it can be replayed locally to check for things like non-determinism errors. For example, assuming history_json_str is populated with a JSON string history either exported from the web UI or from tctl, the following function will replay it:

from temporalio.worker import Replayer

async def run_replayer(history_json_str: str):
  replayer = Replayer(workflows=[SayHello])
  await replayer.replay_workflow(history_json_str)

This will throw an error if any non-determinism is detected.

OpenTelemetry Support

OpenTelemetry support requires the optional opentelemetry dependencies which are part of the opentelemetry extra. When using pip, running

pip install temporalio[opentelemetry]

will install needed dependencies. Then the temporalio.contrib.opentelemetry.TracingInterceptor can be created and set as an interceptor on the interceptors argument of Client.connect. When set, spans will be created for all client calls and for all activity and workflow invocations on the worker, spans will be created and properly serialized through the server to give one proper trace for a workflow execution.

Development

The Python SDK is built to work with Python 3.7 and newer. It is built using SDK Core which is written in Rust.

Building

Prepare

To build the SDK from source for use as a dependency, the following prerequisites are required:

• Python >= 3.7
• Rust
• poetry (e.g. python -m pip install poetry)
• poe (e.g. python -m pip install poethepoet)

macOS note: If errors are encountered, it may be better to install Python and Rust as recommended from their websites instead of via brew.

With the prerequisites installed, first clone the SDK repository recursively:

git clone --recursive https://github.com/temporalio/sdk-python.git
cd sdk-python

Use poetry to install the dependencies with --no-root to not install this package (because we still need to build it):

poetry install --no-root

Build

Now perform the release build:

This will take a while because Rust will compile the core project in release mode (see Local SDK development environment for the quicker approach to local development).

poetry build

The compiled wheel doesn't have the exact right tags yet for use, so run this script to fix it:

poe fix-wheel

The whl wheel file in dist/ is now ready to use.

Use

The wheel can now be installed into any virtual environment.

For example, create a virtual environment somewhere and then run the following inside the virtual environment:

pip install wheel

pip install /path/to/cloned/sdk-python/dist/*.whl

Create this Python file at example.py:

import asyncio
from temporalio import workflow, activity
from temporalio.client import Client
from temporalio.worker import Worker

@workflow.defn
class SayHello:
    @workflow.run
    async def run(self, name: str) -> str:
        return f"Hello, {name}!"

async def main():
    client = await Client.connect("localhost:7233")
    async with Worker(client, task_queue="my-task-queue", workflows=[SayHello]):
        result = await client.execute_workflow(SayHello.run, "Temporal",
            id="my-workflow-id", task_queue="my-task-queue")
        print(f"Result: {result}")

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

Assuming there is a local Temporal server running, execute the file with python (or python3 if necessary):

python example.py

It should output:

Result: Hello, Temporal!

Local SDK development environment

For local development, it is often quicker to use debug builds and a local virtual environment.

While not required, it often helps IDEs if we put the virtual environment .venv directory in the project itself. This can be configured system-wide via:

poetry config virtualenvs.in-project true

Now perform the same steps as the "Prepare" section above by installing the prerequisites, cloning the project, installing dependencies, and generating the protobuf code:

git clone --recursive https://github.com/temporalio/sdk-python.git
cd sdk-python
poetry install --no-root

Now compile the Rust extension in develop mode which is quicker than release mode:

poe build-develop

That step can be repeated for any Rust changes made.

The environment is now ready to develop in.

Testing

Tests currently require Go to be installed since they use an embedded Temporal server as a library. With Go installed, run the following to execute tests:

poe test

Style

• Mostly Google Style Guide. Notable exceptions:
  • We use Black for formatting, so that takes precedence
  • In tests and example code, can import individual classes/functions to make it more readable. Can also do this for rarely in library code for some Python common items (e.g. dataclass or partial), but not allowed to do this for any temporalio packages (except temporalio.types) or any classes/functions that aren't clear when unqualified.
  • We allow relative imports for private packages
  • We allow @staticmethod