| Name | quixstreams JSON |
| Version |
3.5.0
JSON |
| download |
| home_page | None |
| Summary | Python library for building stream processing applications with Apache Kafka |
| upload_time | 2024-12-19 14:06:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.9 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2022] [Quix Analytics Ltd.] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
streaming
processing
pipeline
event
real-time
time series
dataframe
kafka
quix
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
confluent-kafka
requests
rocksdict
typing_extensions
orjson
pydantic
pydantic-settings
jsonschema
jsonlines
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](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)
# Open source Python framework for reliable data engineering
Quix Streams is an end-to-end framework for real-time Python data engineering, operational analytics and machine learning on Apache Kafka data streams. Extract, transform and load data reliably in fewer lines of code using your favourite Python libraries.
Build data pipelines and event-driven microservice architectures leveraging Kafka's low-level scalability, resiliency and durability features in a lightweight library without server-side clusters to manage.
Quix Streams provides the following features to make your life easier:
- Pure Python, meaning no wrappers around Java and no cross-language debugging.
- Sources & Sinks API for building custom connectors that integrate data with Kafka.
- Streaming DataFrame API for building tabular data processing pipelines.
- Serializers API supporting JSON, Avro, Protobuf & Schema Registry.
- State API with built-in RocksDB state object for stateful processing.
- Application API for managing the Kafka-related setup, teardown and message lifecycle.
- Operators for common processing tasks like Windowing, Branching, Group By and Reduce.
- Exactly-once processing guarantees via Kafka transactions.
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
# PyPI
python -m pip install quixstreams
# or conda
conda install -c conda-forge quixio::quixstreams
```
#### Requirements
Python 3.9+, 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 automatically tracks the sdf!)
app.run()
```
### 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/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)
- [x] [Windowed aggregations over Sliding windows](https://quix.io/docs/quix-streams/windowing.html)
- [ ] Joins
For a more detailed overview of the planned features, please look at [the Roadmap Board](https://github.com/orgs/quixio/projects/1).
## 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).
Raw data
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"name": "quixstreams",
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"requires_python": "<4,>=3.9",
"maintainer_email": null,
"keywords": "streaming, processing, pipeline, event, real-time, time series, DataFrame, Kafka, Quix",
"author": null,
"author_email": "Quix Analytics Ltd <devs@quix.io>",
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"platform": null,
"description": "\n\n [](https://github.com/quixio/quix-streams/releases)\n\n[](https://quix.io/docs/quix-streams/introduction.html) \\\n[](https://quix.io/slack-invite)\n[](https://www.youtube.com/@QuixStreams)\n[](https://www.linkedin.com/company/70925173/)\n[](https://twitter.com/quix_io)\n\n# Open source Python framework for reliable data engineering\n\nQuix Streams is an end-to-end framework for real-time Python data engineering, operational analytics and machine learning on Apache Kafka data streams. Extract, transform and load data reliably in fewer lines of code using your favourite Python libraries.\n\nBuild data pipelines and event-driven microservice architectures leveraging Kafka's low-level scalability, resiliency and durability features in a lightweight library without server-side clusters to manage.\n\nQuix Streams provides the following features to make your life easier:\n- Pure Python, meaning no wrappers around Java and no cross-language debugging.\n- Sources & Sinks API for building custom connectors that integrate data with Kafka.\n- Streaming DataFrame API for building tabular data processing pipelines.\n- Serializers API supporting JSON, Avro, Protobuf & Schema Registry.\n- State API with built-in RocksDB state object for stateful processing.\n- Application API for managing the Kafka-related setup, teardown and message lifecycle.\n- Operators for common processing tasks like Windowing, Branching, Group By and Reduce.\n- Exactly-once processing guarantees via Kafka transactions.\n\nUse 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.\n\n## Getting Started \ud83c\udfc4\n\n### Install Quix Streams\n\n```shell\n# PyPI\npython -m pip install quixstreams\n\n# or conda\nconda install -c conda-forge quixio::quixstreams\n```\n\n#### Requirements\nPython 3.9+, Apache Kafka 0.10+\n\nSee [requirements.txt](https://github.com/quixio/quix-streams/blob/main/requirements.txt) for the full list of requirements\n\n### Documentation\n[Quix Streams Docs](https://quix.io/docs/quix-streams/introduction.html)\n\n### Example\n\nHere's an example of how to <b>process</b> data from a Kafka Topic with Quix Streams:\n\n```python\nfrom quixstreams import Application\n\n# A minimal application reading temperature data in Celsius from the Kafka topic,\n# converting it to Fahrenheit and producing alerts to another topic.\n\n# Define an application that will connect to Kafka\napp = Application(\n broker_address=\"localhost:9092\", # Kafka broker address\n)\n\n# Define the Kafka topics\ntemperature_topic = app.topic(\"temperature-celsius\", value_deserializer=\"json\")\nalerts_topic = app.topic(\"temperature-alerts\", value_serializer=\"json\")\n\n# Create a Streaming DataFrame connected to the input Kafka topic\nsdf = app.dataframe(topic=temperature_topic)\n\n# Convert temperature to Fahrenheit by transforming the input message (with an anonymous or user-defined function)\nsdf = sdf.apply(lambda value: {\"temperature_F\": (value[\"temperature\"] * 9/5) + 32})\n\n# Filter values above the threshold\nsdf = sdf[sdf[\"temperature_F\"] > 150]\n\n# Produce alerts to the output topic\nsdf = sdf.to_topic(alerts_topic)\n\n# Run the streaming application (app automatically tracks the sdf!)\napp.run()\n```\n\n### Tutorials\n\nTo see Quix Streams in action, check out the Quickstart and Tutorials in the docs: \n\n- [**Quickstart**](https://quix.io/docs/quix-streams/quickstart.html)\n- [**Tutorial - Word Count**](https://quix.io/docs/quix-streams/tutorials/word-count/tutorial.html)\n- [**Tutorial - Anomaly Detection**](https://quix.io/docs/quix-streams/tutorials/anomaly-detection/tutorial.html)\n- [**Tutorial - Purchase Filtering**](https://quix.io/docs/quix-streams/tutorials/purchase-filtering/tutorial.html)\n\n\n### Key Concepts\nThere are two primary objects:\n- `StreamingDataFrame` - a predefined declarative pipeline to process and transform incoming messages.\n- `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.\n\nUnder the hood, the `Application` will:\n- Consume and deserialize messages.\n- Process them with your `StreamingDataFrame`.\n- Produce it to the output topic.\n- Automatically checkpoint processed messages and state for resiliency.\n- Scale using Kafka's built-in consumer groups mechanism.\n\n\n### Deployment\nYou can run Quix Streams pipelines anywhere Python is installed.\n\nDeploy to your own infrastructure or to [Quix Cloud](https://quix.io/product) on AWS, Azure, GCP or on-premise for a fully managed platform. \nYou'll get self-service DevOps, CI/CD and monitoring, all built with best in class engineering practices learned from Formula 1 Racing.\n\nPlease see the [**Connecting to Quix Cloud**](https://quix.io/docs/quix-streams/quix-platform.html) page \nto learn how to use Quix Streams and Quix Cloud together.\n\n## Roadmap \ud83d\udccd\n\nThis library is being actively developed by a full-time team.\n\nHere are some of the planned improvements:\n\n- [x] [Windowed aggregations over Tumbling & Hopping windows](https://quix.io/docs/quix-streams/windowing.html)\n- [x] [Stateful operations and recovery based on Kafka changelog topics](https://quix.io/docs/quix-streams/advanced/stateful-processing.html)\n- [x] [Group-by operation](https://quix.io/docs/quix-streams/groupby.html)\n- [x] [\"Exactly Once\" delivery guarantees for Kafka message processing (AKA transactions)](https://quix.io/docs/quix-streams/configuration.html#processing-guarantees)\n- [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\n- [x] [Schema Registry support](https://quix.io/docs/quix-streams/advanced/schema-registry.html)\n- [x] [Windowed aggregations over Sliding windows](https://quix.io/docs/quix-streams/windowing.html)\n- [ ] Joins\n\nFor a more detailed overview of the planned features, please look at [the Roadmap Board](https://github.com/orgs/quixio/projects/1).\n\n## Get Involved \ud83e\udd1d\n\n- Please use [GitHub issues](https://github.com/quixio/quix-streams/issues) to report bugs and suggest new features.\n- 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.\n- Watch and subscribe to [@QuixStreams on YouTube](https://www.youtube.com/@QuixStreams) for code-along tutorials from scratch and interesting community highlights.\n- 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.\n- If you have any questions or feedback - write to us at support@quix.io!\n\n\n## License \ud83d\udcd7\n\nQuix Streams is licensed under the Apache 2.0 license. \nView a copy of the License file [here](https://github.com/quixio/quix-streams/blob/main/LICENSE).\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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