Temporal.io Python SDK

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cretz dandavison7 swgillespie-temporal temporalio

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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:

Application Development Guide - Once you've tried our Quick Start, check out our guide on how to use Temporal in your Python applications, including information around Temporal core concepts.
Python Code Samples
API Documentation - Complete Temporal Python SDK Package reference.

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.

Contents

Quick Start
Usage
Development

Quick Start

We will guide you through the Temporal basics to create a "hello, world!" script on your machine. It is not intended as one of the ways to use Temporal, but in reality it is very simplified and decidedly not "the only way" to use Temporal. For more information, check out the docs references in "Next Steps" below the 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 in activities.py:

from temporalio import activity

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

Create the following in workflows.py:

from datetime import timedelta
from temporalio import workflow

# Import our activity, passing it through the sandbox
with workflow.unsafe.imports_passed_through():
    from .activities import say_hello

@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)
        )

Create the following in run_worker.py:

import asyncio
from temporalio.client import Client
from temporalio.worker import Worker

# Import the activity and workflow from our other files
from .activities import say_hello
from .workflows import SayHello

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 .workflows 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!

Next Steps

Temporal can be implemented in your code in many different ways, to suit your application's needs. The links below will give you much more information about how Temporal works with Python:

Code Samples - If you want to start with some code, we have provided some pre-built samples.
Application Development Guide Our Python specific Developer's Guide will give you much more information on how to build with Temporal in your Python applications than our SDK README ever could (or should).
API Documentation - Full Temporal Python SDK package documentation.

Usage

From here, you will find reference documentation about specific pieces of the Temporal Python SDK that were built around Temporal concepts. This section is not intended as a how-to guide -- For more how-to oriented information, check out the links in the Next Steps section above.

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, payload codecs, and failure converters. Payload converters convert Python values to/from serialized bytes. Payload codecs convert bytes to bytes (e.g. for compression or encryption). Failure converters convert exceptions to/from serialized failures.

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
  - The default data converter is deprecated for Pydantic models and will warn if used since not all fields work. See this sample for the recommended approach.
- IntEnum, StrEnum based enumerates
- UUID

This notably doesn't include any date, time, or datetime objects as they may not work across SDKs.

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.

Classes with generics may not have the generics properly resolved. The current implementation, similar to Pydantic, does not have generic type resolution. Users should use concrete types.

Custom Type Data Conversion

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.

Data converters contain a reference to a payload converter class that is used to convert to/from payloads/values. This is a class and not an instance because it is instantiated on every workflow run inside the sandbox. The payload converter is usually a CompositePayloadConverter which contains a multiple EncodingPayloadConverters it uses to try to serialize/deserialize payloads. Upon serialization, each EncodingPayloadConverter is tried until one succeeds. The EncodingPayloadConverter provides an "encoding" string serialized onto the payload so that, upon deserialization, the specific EncodingPayloadConverter for the given "encoding" is used.

The default data converter uses the DefaultPayloadConverter which is simply a CompositePayloadConverter with a known set of default EncodingPayloadConverters. To implement a custom encoding for a custom type, a new EncodingPayloadConverter can be created for the new type. For example, to support IPv4Address types:

class IPv4AddressEncodingPayloadConverter(EncodingPayloadConverter):
    @property
    def encoding(self) -> str:
        return "text/ipv4-address"

    def to_payload(self, value: Any) -> Optional[Payload]:
        if isinstance(value, ipaddress.IPv4Address):
            return Payload(
                metadata={"encoding": self.encoding.encode()},
                data=str(value).encode(),
            )
        else:
            return None

    def from_payload(self, payload: Payload, type_hint: Optional[Type] = None) -> Any:
        assert not type_hint or type_hint is ipaddress.IPv4Address
        return ipaddress.IPv4Address(payload.data.decode())

class IPv4AddressPayloadConverter(CompositePayloadConverter):
    def __init__(self) -> None:
        # Just add ours as first before the defaults
        super().__init__(
            IPv4AddressEncodingPayloadConverter(),
            *DefaultPayloadConverter.default_encoding_payload_converters,
        )

my_data_converter = dataclasses.replace(
    DataConverter.default,
    payload_converter_class=IPv4AddressPayloadConverter,
)

Imports are left off for brevity.

This is good for many custom types. However, sometimes you want to override the behavior of the just the existing JSON encoding payload converter to support a new type. It is already the last encoding data converter in the list, so it's the fall-through behavior for any otherwise unknown type. Customizing the existing JSON converter has the benefit of making the type work in lists, unions, etc.

The JSONPlainPayloadConverter uses the Python json library with an advanced JSON encoder by default and a custom value conversion method to turn json.loaded values to their type hints. The conversion can be customized for serialization with a custom json.JSONEncoder and deserialization with a custom JSONTypeConverter. For example, to support IPv4Address types in existing JSON conversion:

class IPv4AddressJSONEncoder(AdvancedJSONEncoder):
    def default(self, o: Any) -> Any:
        if isinstance(o, ipaddress.IPv4Address):
            return str(o)
        return super().default(o)
class IPv4AddressJSONTypeConverter(JSONTypeConverter):
    def to_typed_value(
        self, hint: Type, value: Any
    ) -> Union[Optional[Any], _JSONTypeConverterUnhandled]:
        if issubclass(hint, ipaddress.IPv4Address):
            return ipaddress.IPv4Address(value)
        return JSONTypeConverter.Unhandled

class IPv4AddressPayloadConverter(CompositePayloadConverter):
    def __init__(self) -> None:
        # Replace default JSON plain with our own that has our encoder and type
        # converter
        json_converter = JSONPlainPayloadConverter(
            encoder=IPv4AddressJSONEncoder,
            custom_type_converters=[IPv4AddressJSONTypeConverter()],
        )
        super().__init__(
            *[
                c if not isinstance(c, JSONPlainPayloadConverter) else json_converter
                for c in DefaultPayloadConverter.default_encoding_payload_converters
            ]
        )

my_data_converter = dataclasses.replace(
    DataConverter.default,
    payload_converter_class=IPv4AddressPayloadConverter,
)

Now IPv4Address can be used in type hints including collections, optionals, etc.

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 datetime import timedelta
from temporalio import workflow

# Pass the activities through the sandbox
with workflow.unsafe.imports_passed_through():
    from .my_activities import GreetingInfo, create_greeting_activity

@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(
                [
                    asyncio.create_task(self._greeting_info_update.wait()),
                    asyncio.create_task(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

This assumes there's an activity in my_activities.py like:

from dataclasses import dataclass
from temporalio import workflow

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

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

Some things to note about the above workflow code:

Workflows run in a sandbox by default.
- Users are encouraged to define workflows in files with no side effects or other complicated code or unnecessary imports to other third party libraries.
- Non-standard-library, non-temporalio imports should usually be "passed through" the sandbox. See the Workflow Sandbox section for more details.
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()
- ⚠️Local activities are currently experimental
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 or the "workflow task" (i.e. suspend the workflow retrying).
Exceptions that are instances of temporalio.exceptions.FailureError will fail the workflow with that exception
- For failing the workflow explicitly with a user exception, use temporalio.exceptions.ApplicationError. This can be marked non-retryable or include details as needed.
- Other exceptions that come from activity execution, child execution, cancellation, etc are already instances of FailureError and will fail the workflow when uncaught.
All other exceptions fail the "workflow task" which means the workflow will continually retry until the workflow is fixed. This is helpful for bad code or other non-predictable exceptions. To actually fail the workflow, use an ApplicationError as mentioned above.

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.

Workflow Sandbox

By default workflows are run in a sandbox to help avoid non-deterministic code. If a call that is known to be non-deterministic is performed, an exception will be thrown in the workflow which will "fail the task" which means the workflow will not progress until fixed.

The sandbox is not foolproof and non-determinism can still occur. It is simply a best-effort way to catch bad code early. Users are encouraged to define their workflows in files with no other side effects.

The sandbox offers a mechanism to pass through modules from outside the sandbox. By default this already includes all standard library modules and Temporal modules. For performance and behavior reasons, users are encouraged to pass through all third party modules whose calls will be deterministic. This includes modules containing the activities to be referenced in workflows. See "Passthrough Modules" below on how to do this.

If you are getting an error like:

temporalio.worker.workflow_sandbox._restrictions.RestrictedWorkflowAccessError: Cannot access http.client.IncompleteRead.__mro_entries__ from inside a workflow. If this is code from a module not used in a workflow or known to only be used deterministically from a workflow, mark the import as pass through.

Then you are either using an invalid construct from the workflow, this is a known limitation of the sandbox, or most commonly this is from a module that is safe to pass through (see "Passthrough Modules" section below).

How the Sandbox Works

The sandbox is made up of two components that work closely together:

Global state isolation
Restrictions preventing known non-deterministic library calls

Global state isolation is performed by using exec. Upon workflow start, the file that the workflow is defined in is imported into a new sandbox created for that workflow run. In order to keep the sandbox performant a known set of "passthrough modules" are passed through from outside of the sandbox when they are imported. These are expected to be side-effect free on import and have their non-deterministic aspects restricted. By default the entire Python standard library, temporalio, and a couple of other modules are passed through from outside of the sandbox. To update this list, see "Customizing the Sandbox".

Restrictions preventing known non-deterministic library calls are achieved using proxy objects on modules wrapped around the custom importer set in the sandbox. Many restrictions apply at workflow import time and workflow run time, while some restrictions only apply at workflow run time. A default set of restrictions is included that prevents most dangerous standard library calls. However it is known in Python that some otherwise-non-deterministic invocations, like reading a file from disk via open or using os.environ, are done as part of importing modules. To customize what is and isn't restricted, see "Customizing the Sandbox".

Avoiding the Sandbox

There are three increasingly-scoped ways to avoid the sandbox. Users are discouraged from avoiding the sandbox if possible.

To remove restrictions around a particular block of code, use with temporalio.workflow.unsafe.sandbox_unrestricted():. The workflow will still be running in the sandbox, but no restrictions for invalid library calls will be applied.

To run an entire workflow outside of a sandbox, set sandboxed=False on the @workflow.defn decorator when defining it. This will run the entire workflow outside of the workflow which means it can share global state and other bad things.

To disable the sandbox entirely for a worker, set the Worker init's workflow_runner keyword argument to temporalio.worker.UnsandboxedWorkflowRunner(). This value is defaulted to temporalio.worker.workflow_sandbox.SandboxedWorkflowRunner() so by changing it to the unsandboxed runner, the sandbox will not be used at all.

Customizing the Sandbox

⚠️ WARNING: APIs in the temporalio.worker.workflow_sandbox module are not yet considered stable and may change in future releases.

When creating the Worker, the workflow_runner is defaulted to temporalio.worker.workflow_sandbox.SandboxedWorkflowRunner(). The SandboxedWorkflowRunner's init accepts a restrictions keyword argument that is defaulted to SandboxRestrictions.default. The SandboxRestrictions dataclass is immutable and contains three fields that can be customized, but only two have notable value. See below.

Passthrough Modules

By default the sandbox completely reloads non-standard-library and non-Temporal modules for every workflow run. To make the sandbox quicker and use less memory when importing known-side-effect-free third party modules, they can be marked as passthrough modules.

For performance and behavior reasons, users are encouraged to pass through all third party modules whose calls will be deterministic.

One way to pass through a module is at import time in the workflow file using the imports_passed_through context manager like so:

# my_workflow_file.py

from temporalio import workflow

with workflow.unsafe.imports_passed_through():
    import pydantic

@workflow.defn
class MyWorkflow:
    ...

Alternatively, this can be done at worker creation time by customizing the runner's restrictions. For example:

my_worker = Worker(
  ...,
  workflow_runner=SandboxedWorkflowRunner(
    restrictions=SandboxRestrictions.default.with_passthrough_modules("pydantic")
  )
)

In both of these cases, now the pydantic module will be passed through from outside of the sandbox instead of being reloaded for every workflow run.

Invalid Module Members

SandboxRestrictions.invalid_module_members contains a root matcher that applies to all module members. This already has a default set which includes things like datetime.date.today() which should never be called from a workflow. To remove this restriction:

my_restrictions = dataclasses.replace(
    SandboxRestrictions.default,
    invalid_module_members=SandboxRestrictions.invalid_module_members_default.with_child_unrestricted(
      "datetime", "date", "today",
    ),
)
my_worker = Worker(..., workflow_runner=SandboxedWorkflowRunner(restrictions=my_restrictions))

Restrictions can also be added by |'ing together matchers, for example to restrict the datetime.date class from being used altogether:

my_restrictions = dataclasses.replace(
    SandboxRestrictions.default,
    invalid_module_members=SandboxRestrictions.invalid_module_members_default | SandboxMatcher(
      children={"datetime": SandboxMatcher(use={"date"})},
    ),
)
my_worker = Worker(..., workflow_runner=SandboxedWorkflowRunner(restrictions=my_restrictions))

See the API for more details on exact fields and their meaning.

Known Sandbox Issues

Below are known sandbox issues. As the sandbox is developed and matures, some may be resolved.

Global Import/Builtins

Currently the sandbox references/alters the global sys.modules and builtins fields while running workflow code. In order to prevent affecting other sandboxed code, thread locals are leveraged to only intercept these values during the workflow thread running. Therefore, technically if top-level import code starts a thread, it may lose sandbox protection.

Sandbox is not Secure

The sandbox is built to catch many non-deterministic and state sharing issues, but it is not secure. Some known bad calls are intercepted, but for performance reasons, every single attribute get/set cannot be checked. Therefore a simple call like setattr(temporalio.common, "__my_key", "my value") will leak across sandbox runs.

The sandbox is only a helper, it does not provide full protection.

Sandbox Performance

The sandbox does not add significant CPU or memory overhead for workflows that are in files which only import standard library modules. This is because they are passed through from outside of the sandbox. However, every non-standard-library import that is performed at the top of the same file the workflow is in will add CPU overhead (the module is re-imported every workflow run) and memory overhead (each module independently cached as part of the workflow run for isolation reasons). This becomes more apparent for large numbers of workflow runs.

To mitigate this, users should:

Define workflows in files that have as few non-standard-library imports as possible
Alter the max workflow cache and/or max concurrent workflows settings if memory grows too large
Set third-party libraries as passthrough modules if they are known to be side-effect free

Extending Restricted Classes

Extending a restricted class causes Python to instantiate the restricted metaclass which is unsupported. Therefore if you attempt to use a class in the sandbox that extends a restricted class, it will fail. For example, if you have a class MyZipFile(zipfile.ZipFile) and try to use that class inside a workflow, it will fail.

Classes used inside the workflow should not extend restricted classes. For situations where third-party modules need to at import time, they should be marked as pass through modules.

Certain Standard Library Calls on Restricted Objects

If an object is restricted, internal C Python validation may fail in some cases. For example, running dict.items(os.__dict__) will fail with:

descriptor 'items' for 'dict' objects doesn't apply to a '_RestrictedProxy' object

This is a low-level check that cannot be subverted. The solution is to not use restricted objects inside the sandbox. For situations where third-party modules need to at import time, they should be marked as pass through modules.

is_subclass of ABC-based Restricted Classes

Due to https://bugs.python.org/issue44847, classes that are wrapped and then checked to see if they are subclasses of another via is_subclass may fail (see also this wrapt issue).

Compiled Pydantic Sometimes Using Wrong Types

If the Pydantic dependency is in compiled form (the default) and you are using a Pydantic model inside a workflow sandbox that uses a datetime type, it will grab the wrong validator and use date instead. This is because our patched form of issubclass is bypassed by compiled Pydantic.

To work around, either don't use datetime-based Pydantic model fields in workflows, or mark datetime library as passthrough (means you lose protection against calling the non-deterministic now()), or use non-compiled Pydantic dependency.

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). A non-local 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.

All long running, non-local activities should heartbeat so they can be cancelled. Cancellation in threaded activities throws but multiprocess/other activities does not. The sections below on each synchronous type explain further. There are also calls on the context that can check for cancellation. For more information, see "Activity Context" and "Heartbeating and Cancellation" sections 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.

If any activity ever throws a concurrent.futures.BrokenExecutor, the failure is consisted unrecoverable and the worker will fail and shutdown.

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.

By default, cancellation of a synchronous multithreaded activity is done via a temporalio.exceptions.CancelledError thrown into the activity thread. Activities that do not wish to have cancellation thrown can set no_thread_cancel_exception=True in the @activity.defn decorator.

Code that wishes to be temporarily shielded from the cancellation exception can run inside with activity.shield_thread_cancel_exception():. But once the last nested form of that block is finished, even if there is a return statement within, it will throw the cancellation if there was one. A try + except temporalio.exceptions.CancelledError would have to surround the with to handle the cancellation explicitly.

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
shield_thread_cancel_exception() - Context manager for use in with clauses by synchronous multithreaded activities to prevent cancel exception from being thrown during the block of code
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 a non-local activity to be notified of cancellation requests, it must be given a heartbeat_timeout at invocation time and invoke temporalio.activity.heartbeat() inside the activity. 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.

Heartbeating has no effect on local activities.

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_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.client import WorkflowHistory
from temporalio.worker import Replayer

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

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

Replaying from workflow history is a powerful concept that many use to test that workflow alterations won't cause non-determinisms with past-complete workflows. The following code will make sure that all workflow histories for a certain workflow type (i.e. workflow class) are safe with the current code.

from temporalio.client import Client, WorkflowHistory
from temporalio.worker import Replayer

async def check_past_histories(my_client: Client):
  replayer = Replayer(workflows=[SayHello])
  await replayer.replay_workflows(
    await my_client.list_workflows("WorkflowType = 'SayHello'").map_histories(),
  )

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.

Protobuf 3.x vs 4.x

Python currently has two somewhat-incompatible protobuf library versions - the 3.x series and the 4.x series. Python currently recommends 4.x and that is the primary supported version. Some libraries like Pulumi require 4.x. Other libraries such as ONNX and Streamlit, for one reason or another, have/will not leave 3.x.

To support these, Temporal Python SDK allows any protobuf library >= 3.19. However, the C extension in older Python versions can cause issues with the sandbox due to global state sharing. Temporal strongly recommends using the latest protobuf 4.x library unless you absolutely cannot at which point some proto libraries may have to be marked as Passthrough Modules.

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

To execute tests:

poe test

This runs against Temporalite. To run against the time-skipping test server, pass --workflow-environment time-skipping. To run against the default namespace of an already-running server, pass the host:port to --workflow-environment. Can also use regular pytest arguments. For example, here's how to run a single test with debug logs on the console:

poe test -s --log-cli-level=DEBUG -k test_sync_activity_thread_cancel_caught

Proto Generation and Testing

To allow for backwards compatibility, protobuf code is generated on the 3.x series of the protobuf library. To generate protobuf code, you must be on Python <= 3.10, and then run poetry add "protobuf<4". Then the protobuf files can be generated via poe gen-protos. Tests can be run for protobuf version 3 by setting the TEMPORAL_TEST_PROTO3 env var to 1 prior to running tests.

Do not commit poetry.lock or pyproject.toml changes. To go back from this downgrade, restore pyproject.toml and run poetry update protobuf grpcio-tools.

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

Project details

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Maintainers

cretz dandavison7 swgillespie-temporal temporalio

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