Introducing Faust







distributed, stream, async, processing, data, queue

Table of Contents

What can it do?


Process infinite streams in a straightforward manner using asynchronous generators. The concept of “agents” comes from the actor model, and means the stream processor can execute concurrently on many CPU cores, and on hundreds of machines at the same time.

Use regular Python syntax to process streams and reuse your favorite libraries:

async def process(stream):
    async for value in stream:

Tables are sharded dictionaries that enable stream processors to be stateful with persistent and durable data.

Streams are partitioned to keep relevant data close, and can be easily repartitioned to achieve the topology you need.

In this example we repartition an order stream by account id, to count orders in a distributed table:

import faust

# this model describes how message values are serialized
# in the Kafka "orders" topic.
class Order(faust.Record, serializer='json'):
    account_id: str
    product_id: str
    amount: int
    price: float

app = faust.App('hello-app', broker='kafka://localhost')
orders_kafka_topic = app.topic('orders', value_type=Order)

# our table is sharded amongst worker instances, and replicated
# with standby copies to take over if one of the nodes fail.
order_count_by_account = app.Table('order_count', default=int)

async def process(orders: faust.Stream[Order]) -> None:
    async for order in orders.group_by(Order.account_id):
        order_count_by_account[order.account_id] += 1

If we start multiple instances of this Faust application on many machines, any order with the same account id will be received by the same stream processing agent, so the count updates correctly in the table.

Sharding/partitioning is an essential part of stateful stream processing applications, so take this into account when designing your system, but note that streams can also be processed in round-robin order so you can use Faust for event processing and as a task queue also.

Asynchronous with asyncio

Faust takes full advantage of asyncio and the new async/await keywords in Python 3.6+ to run multiple stream processors in the same process, along with web servers and other network services.

Thanks to Faust and asyncio you can now embed your stream processing topology into your existing asyncio/gevent/ eventlet/Twisted/Tornado applications.

Faust is…

Faust is extremely easy to use. To get started using other stream processing solutions you have complicated hello-world projects, and infrastructure requirements. Faust only requires Kafka, the rest is just Python, so If you know Python you can already use Faust to do stream processing, and it can integrate with just about anything.

Here’s one of the easier applications you can make:

import faust

class Greeting(faust.Record):
    from_name: str
    to_name: str

topic = app.topic('hello-topic', value_type=Greeting)

async def hello(greetings):
    async for greeting in greetings:
        print(f'Hello from {greeting.from_name} to {greeting.to_name}')

async def example_sender(app):
    await hello.send(
        value=Greeting(from_name='Faust', to_name='you'),

if __name__ == '__main__':

You’re probably a bit intimidated by the async and await keywords, but you don’t have to know how asyncio works to use Faust: just mimic the examples, and you’ll be fine.

The example application starts two tasks: one is processing a stream, the other is a background thread sending events to that stream. In a real-life application, your system will publish events to Kafka topics that your processors can consume from, and the background thread is only needed to feed data into our example.

Highly Available

Faust is highly available and can survive network problems and server crashes. In the case of node failure, it can automatically recover, and tables have standby nodes that will take over.


Start more instances of your application as needed.


A single-core Faust worker instance can already process tens of thousands of events every second, and we are reasonably confident that throughput will increase once we can support a more optimized Kafka client.


Faust is just Python, and a stream is an infinite asynchronous iterator. If you know how to use Python, you already know how to use Faust, and it works with your favorite Python libraries like Django, Flask, SQLAlchemy, NTLK, NumPy, SciPy, TensorFlow, etc.

Faust is used for…

  • Event Processing

  • Distributed Joins & Aggregations

  • Machine Learning

  • Asynchronous Tasks

  • Distributed Computing

  • Data Denormalization

  • Intrusion Detection

  • Realtime Web & Web Sockets.

  • and much more…

How do I use it?

Step 1: Add events to your system

  • Was an account created? Publish to Kafka.

  • Did a user change their password? Publish to Kafka.

  • Did someone make an order, create a comment, tag something, …? Publish it all to Kafka!

Step 2: Use Faust to process those events

Some ideas based on the events mentioned above:

  • Send email when an order is dispatches.

  • Find orders created with no corresponding dispatch event for more than three consecutive days.

  • Find accounts that changed their password from a suspicious IP address.

  • Starting to get the idea?

What do I need?

Faust requires Python 3.6 or later, and a running Kafka broker.

There’s no plan to support earlier Python versions. Please get in touch if this is something you want to work on.







pip install faust[rocksdb]


aredis 1.1

pip install faust[redis]



pip install faust[datadog]



pip install faust[statsd]



pip install faust[uvloop]



pip install faust[gevent]



pip install faust[eventlet]


These can be all installed using pip install faust[fast]:






pip install faust[aiodns]



pip install faust[cchardet]



pip install faust[ciso8601]



pip install faust[cython]



pip install faust[orjson]



pip install faust[setproctitle]

Debugging extras

These can be all installed using pip install faust[debug]:






pip install faust[aiomonitor]



pip install faust[setproctitle]


See bundles in the Installation instructions section of this document for a list of supported setuptools extensions.

To specify multiple extensions at the same time

separate extensions with the comma:

$ pip install faust[uvloop,fast,rocksdb,datadog,redis]

RocksDB On MacOS Sierra

To install python-rocksdb on MacOS Sierra you need to specify some additional compiler flags:

$ CFLAGS='-std=c++11 -stdlib=libc++ -mmacosx-version-min=10.10' \
    pip install -U --no-cache python-rocksdb

Design considerations

Modern Python

Faust uses current Python 3 features such as async/await and type annotations. It’s statically typed and verified by the mypy type checker. You can take advantage of type annotations when writing Faust applications, but this is not mandatory.


Faust is designed to be used as a library, and embeds into any existing Python program, while also including helpers that make it easy to deploy applications without boilerplate.


The Faust worker is built up by many different services that start and stop in a certain order. These services can be managed by supervisors, but if encountering an irrecoverable error such as not recovering from a lost Kafka connections, Faust is designed to crash.

For this reason Faust is designed to run inside a process supervisor tool such as supervisord, Circus, or one provided by your Operating System.


Faust abstracts away storages, serializers, and even message transports, to make it easy for developers to extend Faust with new capabilities, and integrate into your existing systems.


The source code is short and readable and serves as a good starting point for anyone who wants to learn how Kafka stream processing systems work.

Getting Help

Mailing list

For discussions about the usage, development, and future of Faust, please join the faust-users mailing list.


Bug tracker


