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https://github.com/quixio/quix-streams
100% Python stream processing with Streaming DataFrames
https://github.com/quixio/quix-streams
data-engineering data-intensive-applications data-science event-driven-architecture kafka machine-learning python real-time-data-processing stream-processing stream-processor streaming-data streaming-data-pipelines streaming-data-processing time-series-data
Last synced: 3 months ago
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100% Python stream processing with Streaming DataFrames
- Host: GitHub
- URL: https://github.com/quixio/quix-streams
- Owner: quixio
- License: apache-2.0
- Created: 2022-11-17T20:36:03.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T09:23:29.000Z (6 months ago)
- Last Synced: 2024-05-22T10:36:24.167Z (6 months ago)
- Topics: data-engineering, data-intensive-applications, data-science, event-driven-architecture, kafka, machine-learning, python, real-time-data-processing, stream-processing, stream-processor, streaming-data, streaming-data-pipelines, streaming-data-processing, time-series-data
- Language: Python
- Homepage: https://docs.quix.io
- Size: 4.94 MB
- Stars: 824
- Watchers: 17
- Forks: 41
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-streaming - Quix Streams - a streaming library originally designed for the McLaren Formula 1 racing team that can process high volumes of time-series data with up to nanosecond precision using Apache Kafka as a message broker. (Table of Contents / Streaming Library)
README
[](https://github.com/quixio/quix-streams/releases)
[](https://quix.io/docs/quix-streams/introduction.html) \
[](https://quix.io/slack-invite)
[](https://www.youtube.com/@QuixStreams)
[](https://www.linkedin.com/company/70925173/)
[](https://twitter.com/quix_io)
# 100% Python Stream Processing for Apache Kafka
Quix Streams is a cloud-native library for processing data in Kafka using pure Python. It’s designed to give you the power of a distributed system in a lightweight library by combining Kafka's low-level scalability and resiliency features with an easy-to-use Python interface (to ease newcomers to stream processing).
It has the following benefits:
- Streaming DataFrame API (similar to pandas DataFrame) for tabular data transformations.
- Custom stateful operations via a state object.
- Custom reducing and aggregating over tumbling and hopping time windows.
- Exactly-once processing semantics via Kafka transactions.
- Pure Python with no need for a server-side engine.
Use Quix Streams to build simple Kafka producer/consumer applications or leverage stream processing to build complex event-driven systems, real-time data pipelines and AI/ML products.
## Getting Started 🏄
### Install Quix Streams
```shell
python -m pip install quixstreams
```
#### Requirements
Python 3.8+, Apache Kafka 0.10+
See [requirements.txt](https://github.com/quixio/quix-streams/blob/main/requirements.txt) for the full list of requirements
### Documentation
[Quix Streams Docs](https://quix.io/docs/quix-streams/introduction.html)
### Example
Here's an example of how to process data from a Kafka Topic with Quix Streams:
```python
from quixstreams import Application
# A minimal application reading temperature data in Celsius from the Kafka topic,
# converting it to Fahrenheit and producing alerts to another topic.
# Define an application that will connect to Kafka
app = Application(
broker_address="localhost:9092", # Kafka broker address
)
# Define the Kafka topics
temperature_topic = app.topic("temperature-celsius", value_deserializer="json")
alerts_topic = app.topic("temperature-alerts", value_serializer="json")
# Create a Streaming DataFrame connected to the input Kafka topic
sdf = app.dataframe(topic=temperature_topic)
# Convert temperature to Fahrenheit by transforming the input message (with an anonymous or user-defined function)
sdf = sdf.apply(lambda value: {"temperature_F": (value["temperature"] * 9/5) + 32})
# Filter values above the threshold
sdf = sdf[sdf["temperature_F"] > 150]
# Produce alerts to the output topic
sdf = sdf.to_topic(alerts_topic)
# Run the streaming application
app.run(sdf)
```
### Tutorials
To see Quix Streams in action, check out the Quickstart and Tutorials in the docs:
- [**Quickstart**](https://quix.io/docs/quix-streams/quickstart.html)
- [**Tutorial - Word Count**](https://quix.io/docs/quix-streams/tutorials/word-count/tutorial.html)
- [**Tutorial - Anomaly Detection**](https://quix.io/docs/quix-streams/tutorials/anomaly-detection/tutorial.html)
- [**Tutorial - Purchase Filtering**](https://quix.io/docs/quix-streams/tutorials/purchase-filtering/tutorial.html)
### Key Concepts
There are two primary objects:
- `StreamingDataFrame` - a predefined declarative pipeline to process and transform incoming messages.
- `Application` - to manage the Kafka-related setup, teardown and message lifecycle (consuming, committing). It processes each message with the dataframe you provide for it to run.
Under the hood, the `Application` will:
- Consume and deserialize messages.
- Process them with your `StreamingDataFrame`.
- Produce it to the output topic.
- Automatically checkpoint processed messages and state for resiliency.
- Scale using Kafka's built-in consumer groups mechanism.
### Deployment
You can run Quix Streams pipelines anywhere Python is installed.
Deploy to your own infrastructure or to [Quix Cloud](https://quix.io/product) on AWS, Azure, GCP or on-premise for a fully managed platform.
You'll get self-service DevOps, CI/CD and monitoring, all built with best in class engineering practices learned from Formula 1 Racing.
Please see the [**Connecting to Quix Cloud**](https://quix.io/docs/quix-streams/quix-platform.html) page
to learn how to use Quix Streams and Quix Cloud together.
## Roadmap 📍
This library is being actively developed by a full-time team.
Here are some of the planned improvements:
- [x] [Windowed aggregations over Tumbling & Hopping windows](https://quix.io/docs/quix-streams/v2-0-latest/windowing.html)
- [x] [Stateful operations and recovery based on Kafka changelog topics](https://quix.io/docs/quix-streams/advanced/stateful-processing.html)
- [x] [Group-by operation](https://quix.io/docs/quix-streams/groupby.html)
- [x] ["Exactly Once" delivery guarantees for Kafka message processing (AKA transactions)](https://quix.io/docs/quix-streams/configuration.html#processing-guarantees)
- [ ] Joins
- [ ] Windowed aggregations over Sliding windows
- [ ] Support for Avro and Protobuf formats
- [ ] Schema Registry support
## Get Involved 🤝
- Please use [GitHub issues](https://github.com/quixio/quix-streams/issues) to report bugs and suggest new features.
- Join the [Quix Community on Slack](https://quix.io/slack-invite), a vibrant group of Kafka Python developers, data engineers and newcomers to Apache Kafka, who are learning and leveraging Quix Streams for real-time data processing.
- Watch and subscribe to [@QuixStreams on YouTube](https://www.youtube.com/@QuixStreams) for code-along tutorials from scratch and interesting community highlights.
- Follow us on [X](https://x.com/Quix_io) and [LinkedIn](https://www.linkedin.com/company/70925173) where we share our latest tutorials, forthcoming community events and the occasional meme.
- If you have any questions or feedback - write to us at [email protected]!
## License 📗
Quix Streams is licensed under the Apache 2.0 license.
View a copy of the License file [here](https://github.com/quixio/quix-streams/blob/main/LICENSE).