dispatch-py 0.7.0

Develop reliable distributed systems on the Dispatch platform.

dispatch-py

Python package to develop applications with the Dispatch platform.

• What is Dispatch?
• Installation
  • Installing the Dispatch CLI
  • Installing the Dispatch SDK
• Usage
  • Writing Dispatch Applications
  • Running Dispatch Applications
  • Writing Transactional Applications with Dispatch
  • Integration with FastAPI
  • Configuration
  • Serialization
• Examples
• Contributing

What is Dispatch?

Dispatch is a platform for developing scalable & reliable distributed systems.

To get started, follow the instructions to sign up for Dispatch 🚀.

Installation

Installing the Dispatch CLI

As a pre-requisite, we recommend installing the Dispatch CLI to simplify the configuration and execution of applications that use Dispatch. On macOS, this can be done easily using Homebrew:

brew tap dispatchrun/dispatch
brew install dispatch

Alternatively, you can download the latest dispatch binary from the Releases page.

Note that this step is optional, applications that use Dispatch can run without the CLI, passing configuration through environment variables or directly in the code. However, the CLI automates the onboarding flow and simplifies the configuration, so we recommend starting with it.

Installing the Dispatch SDK

The Python package is published on PyPI as dispatch-py, to install:

pip install dispatch-py

Usage

Writing Dispatch Applications

The following snippet shows how to write a very simple Dispatch application that does the following:

1. declare a dispatch function named greet which can run asynchronously
2. schedule a call to greet with the argument World
3. run until all dispatched calls have completed

# main.py
import dispatch

@dispatch.function
def greet(msg: str):
    print(f"Hello, ${msg}!")

dispatch.run(lambda: greet.dispatch('World'))

Obviously, this is just an example, a real application would perform much more interesting work, but it's a good start to get a sense of how to use Dispatch.

Running Dispatch Applications

The simplest way to run a Dispatch application is to use the Dispatch CLI, first we need to login:

dispatch login

Then we are ready to run the example program we wrote above:

dispatch run -- python3 main.py

Writing Transactional Applications with Dispatch

The @dispatch.function decorator can also be applied to Python coroutines (a.k.a. async functions), in which case each await point becomes a durability step in the execution. If the awaited operation fails, it is automatically retried, and the parent function is paused until the result are available or a permanent error is raised.

@dispatch.function
async def pipeline(msg):
    # Each await point is a durability step, the functions can be run across the
    # fleet of service instances and retried as needed without losing track of
    # progress through the function execution.
    msg = await transform1(msg)
    msg = await transform2(msg)
    await publish(msg)

@dispatch.function
async def publish(msg):
    # Each dispatch function runs concurrently to the others, even if it does
    # blocking operations like this POST request, it does not prevent other
    # concurrent operations from carrying on in the program.
    r = requests.post("https://somewhere.com/", data=msg)
    r.raise_for_status()

@dispatch.function
async def transform1(msg):
    ...

@dispatch.function
async def transform2(msg):
    ...

This model is composable and can be used to create fan-out/fan-in control flows. gather can be used to wait on multiple concurrent calls:

from dispatch import gather

@dispatch.function
async def process(msgs):
    concurrent_calls = [transform(msg) for msg in msgs]
    return await gather(*concurrent_calls)

@dispatch.function
async def transform(msg):
    ...

Dispatch converts Python coroutines to Distributed Coroutines, which can be suspended and resumed on any instance of a service across a fleet. For a deep dive on these concepts, read our blog post on Distributed Coroutines with a Native Python Extension and Dispatch.

Integration with FastAPI

Many web applications written in Python are developed using FastAPI. Dispatch can integrate with these applications by instantiating a dispatch.fastapi.Dispatch object. When doing so, the Dispatch functions declared by the program can be invoked remotely over the same HTTP interface used for the FastAPI handlers.

The following code snippet is a complete example showing how to install a Dispatch instance on a FastAPI server:

from fastapi import FastAPI
from dispatch.fastapi import Dispatch
import requests

app = FastAPI()
dispatch = Dispatch(app)

@dispatch.function
def publish(url, payload):
    r = requests.post(url, data=payload)
    r.raise_for_status()

@app.get('/')
def root():
    publish.dispatch('https://httpstat.us/200', {'hello': 'world'})
    return {'answer': 42}

In this example, GET requests on the HTTP server dispatch calls to the publish function. The function runs concurrently to the rest of the program, driven by the Dispatch SDK.

Configuration

The Dispatch CLI automatically configures the SDK, so manual configuration is usually not required when running Dispatch applications. However, in some advanced cases, it might be useful to explicitly set configuration options.

In order for Dispatch to interact with functions remotely, the SDK needs to be configured with the address at which the server can be reached. The Dispatch API Key must also be set, and optionally, a public signing key should be configured to verify that requests originated from Dispatch. These configuration options can be passed as arguments to the the Dispatch constructor, but by default they will be loaded from environment variables:

Environment Variable	Value Example
DISPATCH_API_KEY	d4caSl21a5wdx5AxMjdaMeWehaIyXVnN
DISPATCH_ENDPOINT_URL	https://service.domain.com
DISPATCH_VERIFICATION_KEY	-----BEGIN PUBLIC KEY-----...

Serialization

Dispatch uses the pickle library to serialize coroutines.

Serialization of coroutines is enabled by a CPython extension.

The user must ensure that the contents of their stack frames are serializable. That is, users should avoid using variables inside coroutines that cannot be pickled.

If a pickle error is encountered, serialization tracing can be enabled with the DISPATCH_TRACE=1 environment variable to debug the issue. The stacks of coroutines and generators will be printed to stdout before the pickle library attempts serialization.

For help with a serialization issues, please submit a GitHub issue.

Examples

Check out the examples directory for code samples to help you get started with the SDK.

Contributing

Contributions are always welcome! Would you spot a typo or anything that needs to be improved, feel free to send a pull request.

Pull requests need to pass all CI checks before getting merged. Anything that isn't a straightforward change would benefit from being discussed in an issue before submitting a change.

Remember to be respectful and open minded!