Skip to main content

Python adapter for Kafka

Project description

NAAS-PYTHON-KAFKA

This is the Kafka adapter for Python: it allows you to easily connect Python services to Apache Kafka via Python.

The implementation is a wrapper around Confluent-Kafka-Python:

  • AVRO schema's and messages: both key's and values should have a schema. as explained here.
  • Kafka consumer and producer for the test-bed topics.
  • Management
    • Heartbeat (topic: system-heartbeat), so you know which clients are online. Each time the test-bed-adapter is executed, it starts a heartbeat process to notify the its activity to other clients.

Installation

You need to install Python 3+.

To run the examples you will need to install the dependencies specified on the file requirements.txt.

For that, run

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt # Or instead of `pip`, use `pip3`

from the project folder.

Examples and usage

  • url_producer: creates a message with 4 URLs to RSS feeds on the topic ('system_rss_urls')
  • rss_producer: listens to url messages ('system_rss_urls') and produces RSS messages ('system_rss_urls')
  • rss_consumer: listens to RSS messages ('system_rss_urls') and prints them to console.

Uploading to PyPi

  1. Ensure you have the necessary tools installed: Make sure you have setuptools and wheel installed. You can install them using pip:
pip install setuptools wheel
Build the distribution files: In the root directory of your project, run the following command to build the distribution files (wheel and source distribution):
  1. Build the distribution files: In the root directory of your project, run the following command to build the distribution files (wheel and source distribution):
python setup.py sdist bdist_wheel
This command will generate the distribution files inside the dist directory.

This command will generate the distribution files inside the dist directory.

  1. Register an account on PyPI: If you haven't done so already, create an account on PyPI and verify your email address.

  2. Install and configure twine: Install twine, a tool used to upload packages to PyPI, using pip:

pip install twine
Upload the package to PyPI: Use twine to upload the distribution files to PyPI:
  1. Upload the package to PyPI: Use twine to upload the distribution files to PyPI:
twine upload dist/*
This command will prompt you to enter your PyPI username and password. Once provided, twine will upload the distribution files to PyPI.

This command will prompt you to enter your PyPI username and password. Once provided, twine will upload the distribution files to PyPI.

  1. Verify the package on PyPI: Visit your package page on PyPI to ensure that the package has been successfully uploaded and published.

Remember to update the version number in your setup.py file for each new release to avoid conflicts.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

osint-python-test-bed-adapter-2.3.2.tar.gz (9.0 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file osint-python-test-bed-adapter-2.3.2.tar.gz.

File metadata

File hashes

Hashes for osint-python-test-bed-adapter-2.3.2.tar.gz
Algorithm Hash digest
SHA256 d588c37ee1e94491f63de3826d069e663ce7add986f3b42974a7d46c21eea23b
MD5 d67e30d96083d7410a8393257ec566bd
BLAKE2b-256 0ed591515b74103bc23935d520eefa57d07d6aa1dc090ef229ee22bdf037882b

See more details on using hashes here.

File details

Details for the file osint_python_test_bed_adapter-2.3.2-py3-none-any.whl.

File metadata

File hashes

Hashes for osint_python_test_bed_adapter-2.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 8f21224ea7e1ba782acf0ca972a1b4a871cf402fd504e63fab6ef21fa660a1b8
MD5 c12727770b54e2ca37bc8b1c5836eef1
BLAKE2b-256 97b883744d20950bd1320d71d9c096420a5b8e05822e4efa5838da78c295c0c9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page