Metadata-Version: 2.1
Name: quixstreams
Version: 2.11.0
Summary: Python library for building stream processing applications with Apache Kafka
Author-email: Quix Analytics Ltd <devs@quix.io>
License: Apache License
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Project-URL: Homepage, https://github.com/quixio/quix-streams
Keywords: streaming,processing,pipeline,event,real-time,time series,DataFrame,Kafka,Quix
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
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# 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 <b>process</b> 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)
- [x] Support for [Avro](https://quix.io/docs/quix-streams/advanced/serialization.html#avro) and [Protobuf](https://quix.io/docs/quix-streams/advanced/serialization.html#protobuf) formats
- [x] [Schema Registry support](https://quix.io/docs/quix-streams/advanced/schema-registry.html)
- [ ] Joins
- [ ] Windowed aggregations over Sliding windows


## 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 support@quix.io!


## 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).
