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.html
We have decided to fork the originalFaust
project because there is a critical process of releasing new versions which causes uncertainty in the community. Everybody is welcome to contribute to thisfork
,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 onlyaiokafka)
- Support kafka transactions
- Update the documentation
and more...
# Python Streams
# Forever scalable event processing & in-memory durable K/V store;
# as a library w/ asyncio & static typing.
importfaust
Faustis a stream processing library, porting the ideas from
Kafka Streams
to Python.
It is used atRobinhood
to build high performance distributed systems
and real-time data pipelines that process billions of events every day.
Faust provides bothstream processingandevent processing,
sharing similarity with tools such asKafka 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 newasync/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}
classOrder(faust.Record):
account_id:str
amount:int
@app.agent(value_type=Order)
asyncdeforder(orders):
asyncfororderinorders:
# 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 anasync 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++, calledRocksDB
.
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. LikeKafka 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" 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)
asyncdefcount_click(clicks):
asyncforurl,countinclicks.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:
classOrder(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)
asyncdefprocess_order(orders):
asyncfororderinorders:
# process each order using regular Python
total_price=order.price*order.quantity
awaitsend_order_received_email(order.account_id,order)
Faust is statically typed, using themypy
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 ofKafka Streams
.
Learn more about Faust in theintroduction
introduction page
to read more about Faust, system requirements, installation instructions,
community resources, and more.
or go directly to thequickstart
tutorial
to see Faust in action by programming a streaming application.
then explore theUser Guide
for in-depth information organized by topic.
Robinhood
:http://robinhoodasync/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:// aerospike /Apache Kafka
:https://kafka.apache.org
- Clone the project
- Create a virtualenv:
Python 3.7 -m venv venv && source venv/bin/activate
- Install the requirements:
./scripts/install
- Run lint:
./scripts/lint
- Run tests:
./scripts/tests
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::
importfaust
classGreeting(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)
asyncdefhello(greetings):
asyncforgreetingingreetings:
print(f'Hello from{greeting.from_name}to{greeting.to_name}')
@app.timer(interval=1.0)
asyncdefexample_sender(app):
awaithello.send(
value=Greeting(from_name='Faust',to_name='you'),
)
if__name__=='__main__':
app.main()
You're probably a bit intimidated by theasync
andawait
keywords,
but you don't have to know howasyncio
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 ofsetuptools
extensions that can be used
to install Faust and the dependencies for a given feature.
You can specify these in your requirements or on thepip
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 usingRocksDB
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 usingAerospike
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 useuse_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 inTable
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 theaerospike client
policies
:the different policies for read/write/scanspolicies
client
:a dict ofhost
andpolicies
defined above
faust-streaming[redis]
for usingRedis
as a simple caching backend (Memcached-style).
faust-streaming[yaml]
for using YAML and thePyYAML
library in streams.
faust-streaming[fast]
for installing all the available C speedup extensions to Faust core.
faust-streaming[datadog]
for using theDatadog
Faust monitor.
faust-streaming[statsd]
for using theStatsd
Faust monitor.
faust-streaming[prometheus]
for using thePrometheus
Faust monitor.
faust-streaming[uvloop]
for using Faust withuvloop
.
faust-streaming[eventlet]
for using Faust witheventlet
faust-streaming[debug]
for usingaiomonitor
to connect and debug a running Faust worker.
faust-streaming[setproctitle]
when thesetproctitle
module is installed the Faust worker will use it to set a nicer process name inps
/top
listings.vAlso installed with thefast
anddebug
bundles.
Download the latest version of Faust fromhttps://pypi.org/project/faust-streaming/
You can install it by doing:
$ tar xvfz faust-streaming-0.0.0.tar.gz
$cdfaust-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 followingpip
command:
pip install https://github /faust-streaming/faust/zipball/master#egg=faust
Yes! Useeventlet
as a bridge to integrate withasyncio
.
This approach works with any blocking Python library that can work witheventlet
Usingeventlet
requires you to install thefaust-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 thefaust
program:
faust -L eventlet -A myproj worker -l info
or addimport mode.loop.eventlet
at the top of your entry point script:
#!/usr/bin/env Python 3
importmode.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 thetornado.platform.asyncio
bridge
Yes! Use theasyncio
reactor implementation:https://twistedmatrix /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).
I get a maximum number of open files exceeded error by RocksDB when running a Faust app locally. How can I fix this
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 limitSee the difference.
Faust supports kafka with version >= 0.10.
For discussions about the usage, development, and future of Faust, please join thefauststream
Slack.
- https://fauststream.slack
- Sign-up:https://join.slack /t/fauststreaming/shared_invite/zt-1q1jhq4kh-Q1t~rJgpyuMQ6N38cByE9g
If you have any suggestions, bug reports, or annoyances please report them to our issue tracker athttps://github /faust-streaming/faust/issues/
This software is licensed under theNew BSD License
.See theLICENSE
file in the top distribution directory for the full license text.
Development ofFaust
happens atGitHub
You're highly encouraged to participate in the development ofFaust
.
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:
- The use of sexualized language or imagery
- Personal attacks
- Trolling or insulting/derogatory comments
- Public or private harassment
- Publishing other's private information, such as physical or electronic addresses, without explicit permission
- Other unethical or unprofessional conduct.
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.