Python Stream Processing Fork

Installation

pip install faust-streaming

Documentation

• introduction: https://faust-streaming.github.io/faust/introduction.html
• quickstart: https://faust-streaming.github.io/faust/playbooks/quickstart.html
• User Guide: https://faust-streaming.github.io/faust/userguide/index.html

Why the fork

We have decided to fork the original Faust project because there is a critical process of releasing new versions which causes uncertainty in the community. Everybody is welcome to contribute to this fork, and you can be added as a maintainer.

We want to:

• Ensure continues release
• Code quality
• Use of latest versions of kafka drivers (for now only aiokafka)
• Support kafka transactions
• Update the documentation

and more...

Usage

# Python Streams
# Forever scalable event processing & in-memory durable K/V store;
# as a library w/ asyncio & static typing.
import faust

Faust is a stream processing library, porting the ideas from Kafka Streams to Python.

It is used at Robinhood to build high performance distributed systems and real-time data pipelines that process billions of events every day.

Faust provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark, Storm, Samza, Flink,

It does not use a DSL, it's just Python! This means you can use all your favorite Python libraries when stream processing: NumPy, PyTorch, Pandas, NLTK, Django, Flask, SQLAlchemy, ++

Faust requires Python 3.6 or later for the new async/await_ syntax, and variable type annotations.

Here's an example processing a stream of incoming orders:

app = faust.App('myapp', broker='kafka://localhost')

# Models describe how messages are serialized:
# {"account_id": "3fae-...", amount": 3}
class Order(faust.Record):
    account_id: str
    amount: int

@app.agent(value_type=Order)
async def order(orders):
    async for order in orders:
        # process infinite stream of orders.
        print(f'Order for {order.account_id}: {order.amount}')

The Agent decorator defines a "stream processor" that essentially consumes from a Kafka topic and does something for every event it receives.

The agent is an async def function, so can also perform other operations asynchronously, such as web requests.

This system can persist state, acting like a database. Tables are named distributed key/value stores you can use as regular Python dictionaries.

Tables are stored locally on each machine using a super fast embedded database written in C++, called RocksDB.

Tables can also store aggregate counts that are optionally "windowed" so you can keep track of "number of clicks from the last day," or "number of clicks in the last hour." for example. Like Kafka Streams, we support tumbling, hopping and sliding windows of time, and old windows can be expired to stop data from filling up.

For reliability, we use a Kafka topic as "write-ahead-log". Whenever a key is changed we publish to the changelog. Standby nodes consume from this changelog to keep an exact replica of the data and enables instant recovery should any of the nodes fail.

To the user a table is just a dictionary, but data is persisted between restarts and replicated across nodes so on failover other nodes can take over automatically.

You can count page views by URL:

# data sent to 'clicks' topic sharded by URL key.
# e.g. key="http://example.com" value="1"
click_topic = app.topic('clicks', key_type=str, value_type=int)

# default value for missing URL will be 0 with `default=int`
counts = app.Table('click_counts', default=int)

@app.agent(click_topic)
async def count_click(clicks):
    async for url, count in clicks.items():
        counts[url] += count

The data sent to the Kafka topic is partitioned, which means the clicks will be sharded by URL in such a way that every count for the same URL will be delivered to the same Faust worker instance.

Faust supports any type of stream data: bytes, Unicode and serialized structures, but also comes with "Models" that use modern Python syntax to describe how keys and values in streams are serialized:

# Order is a json serialized dictionary,
# having these fields:

class Order(faust.Record):
    account_id: str
    product_id: str
    price: float
    quantity: float = 1.0

orders_topic = app.topic('orders', key_type=str, value_type=Order)

@app.agent(orders_topic)
async def process_order(orders):
    async for order in orders:
        # process each order using regular Python
        total_price = order.price * order.quantity
        await send_order_received_email(order.account_id, order)

Faust is statically typed, using the mypy type checker, so you can take advantage of static types when writing applications.

The Faust source code is small, well organized, and serves as a good resource for learning the implementation of Kafka Streams.

Learn more about Faust in the introduction introduction page to read more about Faust, system requirements, installation instructions, community resources, and more.

or go directly to the quickstart tutorial to see Faust in action by programming a streaming application.

then explore the User Guide for in-depth information organized by topic.

• Robinhood: http://robinhood.com
• async/await:https://medium.freecodecamp.org/a-guide-to-asynchronous-programming-in-python-with-asyncio-232e2afa44f6
• Celery: http://celeryproject.org
• Kafka Streams: https://kafka.apache.org/documentation/streams
• Apache Spark: http://spark.apache.org
• Storm: http://storm.apache.org
• Samza: http://samza.apache.org
• Flink: http://flink.apache.org
• RocksDB: http://rocksdb.org
• Aerospike: https://www.aerospike.com/
• Apache Kafka: https://kafka.apache.org

Local development

1. Clone the project
2. Create a virtualenv: python3.7 -m venv venv && source venv/bin/activate
3. Install the requirements: ./scripts/install
4. Run lint: ./scripts/lint
5. Run tests: ./scripts/tests

Faust key points

Simple

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

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

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

@app.timer(interval=1.0)
async def example_sender(app):
    await hello.send(
        value=Greeting(from_name='Faust', to_name='you'),
    )

if __name__ == '__main__':
    app.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.

Distributed

Start more instances of your application as needed.

Fast

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.

Flexible

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, NLTK, NumPy, SciPy, TensorFlow, etc.

Bundles

Faust also defines a group of setuptools extensions that can be used to install Faust and the dependencies for a given feature.

You can specify these in your requirements or on the pip command-line by using brackets. Separate multiple bundles using the comma:

pip install "faust-streaming[rocksdb]"

pip install "faust-streaming[rocksdb,uvloop,fast,redis,aerospike]"

The following bundles are available:

Faust with extras

Stores

RocksDB

For using RocksDB for storing Faust table state. Recommended in production.

pip install faust-streaming[rocksdb] (uses RocksDB 6)

pip install faust-streaming[rocksdict] (uses RocksD