pip install faust-streaming
introduction: https://faust-streaming.github.io/faust/introduction.htmlquickstart: https://faust-streaming.github.io/faust/playbooks/quickstart.htmlUser Guide: https://faust-streaming.github.io/faust/userguide/index.htmlWe 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:
and more...
# 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.comasync/await:https://medium.freecodecamp.org/a-guide-to-asynchronous-programming-in-python-with-asyncio-232e2afa44f6Celery: http://celeryproject.orgKafka Streams: https://kafka.apache.org/documentation/streamsApache Spark: http://spark.apache.orgStorm: http://storm.apache.orgSamza: http://samza.apache.orgFlink: http://flink.apache.orgRocksDB: http://rocksdb.orgAerospike: https://www.aerospike.com/Apache Kafka: https://kafka.apache.orgpython3.7 -m venv venv && source venv/bin/activate./scripts/install./scripts/lint./scripts/testsFaust 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.
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, NLTK, NumPy, SciPy, TensorFlow, etc.
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:
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 RocksDB 8, not backwards compatible with 6)
pip install faust-streaming[aerospike] for using Aerospike for storing Faust table state. Recommended if supported
Aerospike can be enabled as the state store by specifying
store="aerospike://"
By default, all tables backed by Aerospike use use_partitioner=True and generate changelog topic events similar
to a state store backed by RocksDB.
The following configuration options should be passed in as keys to the options parameter in Table
namespace : aerospike namespace
ttl: TTL for all KV's in the table
username: username to connect to the Aerospike cluster
password: password to connect to the Aerospike cluster
hosts : the hosts parameter as specified in the aerospike client
policies: the different policies for read/write/scans policies
client: a dict of host and policies defined above
faust-streaming[redis] for using Redis as a simple caching backend (Memcached-style).
faust-streaming[yaml] for using YAML and the PyYAML library in streams.
faust-streaming[fast] for installing all the available C speedup extensions to Faust core.
faust-streaming[datadog] for using the Datadog Faust monitor.
faust-streaming[statsd] for using the Statsd Faust monitor.
faust-streaming[prometheus] for using the Prometheus Faust monitor.
faust-streaming[uvloop] for using Faust with uvloop.
faust-streaming[eventlet] for using Faust with eventlet
faust-streaming[debug] for using aiomonitor to connect and debug a running Faust worker.
faust-streaming[setproctitle]when the setproctitle module is installed the Faust worker will use it to set a nicer process name in ps/top listings.vAlso installed with the fast and debug bundles.
Download the latest version of Faust from https://pypi.org/project/faust-streaming/
You can install it by doing:
$ tar xvfz faust-streaming-0.0.0.tar.gz
$ cd faust-streaming-0.0.0
$ python setup.py build
# python setup.py install
The last command must be executed as a privileged user if you are not currently using a virtualenv.
You can install the latest snapshot of Faust using the following pip command:
pip install https://github.com/faust-streaming/faust/zipball/master#egg=faust
Yes! Use eventlet as a bridge to integrate with asyncio.
This approach works with any blocking Python library that can work with eventlet
Using eventlet requires you to install the faust-aioeventlet module,
and you can install this as a bundle along with Faust:
pip install -U faust-streaming[eventlet]
Then to actually use eventlet as the event loop you have to either
use the -L <faust --loop> argument to the faust program:
faust -L eventlet -A myproj worker -l info
or add import mode.loop.eventlet at the top of your entry point script:
#!/usr/bin/env python3
import mode.loop.eventlet # noqa
It's very important this is at the very top of the module, and that it executes before you import libraries.
Yes! Use the tornado.platform.asyncio bridge
Yes! Use the asyncio reactor implementation: https://twistedmatrix.com/documents/current/api/twisted.internet.asyncioreactor.html
No. Faust requires Python 3.8 or later, since it heavily uses features that were
introduced in Python 3.6 (async, await, variable type annotations).
You may need to increase the limit for the maximum number of open files. On macOS and Linux you can use:
ulimit -n max_open_files to increase the open files limit to max_open_files.
On docker, you can use the --ulimit flag:
docker run --ulimit nofile=50000:100000 <image-tag>
where 50000 is the soft limit, and 100000 is the hard limit See the difference.
Faust supports kafka with version >= 0.10.
For discussions about the usage, development, and future of Faust, please join the fauststream Slack.
If you have any suggestions, bug reports, or annoyances please report them to our issue tracker at https://github.com/faust-streaming/faust/issues/
This software is licensed under the New BSD License. See the LICENSE file in the top distribution directory for the full license text.
Development of Faust happens at GitHub
You're highly encouraged to participate in the development of Faust.
Everyone interacting in the project's code bases, issue trackers, chat rooms, and mailing lists is expected to follow the Faust Code of Conduct.
As contributors and maintainers of these projects, and in the interest of fostering an open and welcoming community, we pledge to respect all people who contribute through reporting issues, posting feature requests, updating documentation, submitting pull requests or patches, and other activities.
We are committed to making participation in these projects a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion, or nationality.
Examples of unacceptable behavior by participants include:
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct. By adopting this Code of Conduct, project maintainers commit themselves to fairly and consistently applying these principles to every aspect of managing this project. Project maintainers who do not follow or enforce the Code of Conduct may be permanently removed from the project team.
This code of conduct applies both within project spaces and in public spaces when an individual is representing the project or its community.
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by opening an issue or contacting one or more of the project maintainers.
FAQs
Python Stream Processing. A Faust fork
We found that faust-streaming demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 6 open source maintainers collaborating on the project.
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