apache-flink
Apache Flink Python API
Decision gist · record as of 2026-08-14
Yes, if you need a mature, production-grade distributed stream or batch processing framework in Python and can manage the JVM dependency and substantial runtime dependency footprint. Active maintenance, permissive license, and support for Python 3.9–3.12 make it solid for large-scale data pipelines. Not recommended for lightweight, single-machine workloads or if you want to avoid Java runtime overhead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Java/JVM runtime; communicates with Flink via py4j.
- Minimum Python 3.9.
- Medium install friction due to 16 runtime dependencies including py4j, apache-beam, numpy, pandas, and pyarrow.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-06-21 (54 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 214,134 downloads/mo, #9,423 on PyPI
Alternatives
Verify before relying
pip install apache-flink
import py4j
# Define and execute streaming or batch job using Table API or DataStream API- Whether all 16 runtime dependencies are strictly required or conditionally installed based on use case.
- Performance characteristics and scalability limits for typical workloads.
- Whether the DataStream and Table APIs have feature parity or if one is significantly more mature.
What it is and what it does
Apache Flink is a distributed processing engine for stateful computations over both bounded (batch) and unbounded (streaming) data. The Python API allows you to build scalable data pipelines using either a high-level Table API (similar to SQL or working with tabular data) or a lower-level DataStream API for fine-grained control over state and time semantics. It runs in common cluster environments and executes computations at in-memory speed across any scale.
The package depends on a substantial stack: py4j for JVM interop, apache-beam, and data-handling libraries like numpy, pandas, and pyarrow. It supports Python 3.9–3.12 and has been in production use since first release in 2020. The Table API suits exploratory data analysis and relational queries, while the DataStream API targets complex stream processing use cases requiring explicit state management.
Use it for
- Real-time data processing pipelines that ingest, transform, and aggregate streaming events.
- Large-scale batch ETL jobs that read, clean, and load data from distributed storage.
- Machine learning pipelines that prepare and aggregate data for model training at scale.
- Exploratory data analysis on large datasets using SQL-like queries via the Table API.
- Stateful stream processing with windowing, joins, and complex event detection.
- Data quality monitoring and anomaly detection on continuous event streams.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a mature, production-grade distributed stream or batch processing framework in Python and can manage the JVM dependency and substantial runtime dependency footprint.
Active maintenance, permissive license, and support for Python 3.9–3.12 make it solid for large-scale data pipelines. Not recommended for lightweight, single-machine workloads or if you want to avoid Java runtime overhead.
Install
apache-flink on PyPI
Before you install
Medium install friction due to 16 runtime dependencies including py4j, apache-beam, numpy, pandas, and pyarrow. Actively maintained with recent release 54 days ago. Wheels available for Python 3.9–3.12 on macOS and Linux.
Requires Java/JVM runtime; communicates with Flink via py4j. Minimum Python 3.9.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install apache-flink
import py4j
# Define and execute streaming or batch job using Table API or DataStream APIVerify before relying
- Whether all 16 runtime dependencies are strictly required or conditionally installed based on use case.
- Performance characteristics and scalability limits for typical workloads.
- Whether the DataStream and Table APIs have feature parity or if one is significantly more mature.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 16 packagespy4jpython-dateutilapache-beamcloudpickleavropytzfastavrorequestsprotobufnumpypandaspyarrowpemjahttplib2ruamel.yamlapache-flink-libraries |
| Maintenance | Actively maintained 54 days since the last release |
| First released | |
| Downloads | 214,134 / month, #9,423 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9 |
Evidence: apache_flink-2.3.0-cp310-cp310-macosx_10_9_x86_64.whl; apache_flink-2.3.0-cp310-cp310-macosx_11_0_arm64.whl; apache_flink-2.3.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl; apache_flink-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl; apache_flink-2.3.0-cp311-cp311-macosx_11_0_arm64.whl; apache_flink-2.3.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl; apache_flink-2.3.0-cp312-cp312-macosx_10_9_x86_64.whl; apache_flink-2.3.0-cp312-cp312-macosx_11_0_arm64.whl; apache_flink-2.3.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl; apache_flink-2.3.0-cp39-cp39-macosx_10_9_x86_64.whl; apache_flink-2.3.0-cp39-cp39-macosx_11_0_arm64.whl; apache_flink-2.3.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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See also apache-flink-libraries · apache-beam · pyspark · pyspark-client · quixstreams · apache-airflow-providers-apache-flink · aws-cdk.aws-kinesisanalytics-flink-alpha · signalflow-client-python · faust-streaming · delta-spark