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:
# Build the distribution files: In the root directory of your project, run the following command to build the distribution files (wheel and source distribution):
pip install setuptools wheel
  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):
# This command will generate the distribution files inside the dist directory.
python setup.py sdist bdist_wheel

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:

# Upload the package to PyPI: Use twine to upload the distribution files to PyPI:
pip install twine
  1. Upload the package to PyPI: Use twine to 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.
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.

  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-2.4.1.tar.gz (8.7 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file osint-python-test-bed-adapter-2.4.1.tar.gz.

File metadata

File hashes

Hashes for osint-python-test-bed-adapter-2.4.1.tar.gz
Algorithm Hash digest
SHA256 9f3d3c2f204d5028208e44d26d889811560fee662fd024db8362b65d2489cbf0
MD5 6f6e644d5e784bce624683c741b2c79b
BLAKE2b-256 c1675ef31ccdaeb166b812029dffb55932af6173ff5ea468e1e94c33e1fc06f9

See more details on using hashes here.

File details

Details for the file osint_python_test_bed_adapter-2.4.1-py3-none-any.whl.

File metadata

File hashes

Hashes for osint_python_test_bed_adapter-2.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 53ccb2c98bffe8e0a90f3690e922bfcd51dd6a1bd59138bd1176aae5e7c8db43
MD5 643bc08891f2e168747d2a54670e91ec
BLAKE2b-256 730ff9ca7c55b9c7a03e7d141b761dc6cb0b95dc8311ed4a992d01f811e3612e

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