KQ (Kafka Queue) is a lightweight Python library which lets you enqueue and execute jobs asynchronously using Apache Kafka. It uses kafka-python under the hood.
- Support for Python 3.5 will be dropped from KQ version 3.0.0.
- See releases for latest updates.
- Apache Kafka 0.9+
- Python 3.6+
Install using pip:
pip install kqStart your Kafka instance. Example using Docker:
docker run -p 9092:9092 -e ADV_HOST=127.0.0.1 lensesio/fast-data-devDefine your KQ worker.py module:
import logging
from kafka import KafkaConsumer
from kq import Worker
# Set up logging.
formatter = logging.Formatter("[%(levelname)s] %(message)s")
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
logger = logging.getLogger("kq.worker")
logger.setLevel(logging.DEBUG)
logger.addHandler(stream_handler)
# Set up a Kafka consumer.
consumer = KafkaConsumer(
bootstrap_servers="127.0.0.1:9092",
group_id="group",
auto_offset_reset="latest"
)
# Set up a worker.
worker = Worker(topic="topic", consumer=consumer)
worker.start()Start your worker:
python my_worker.py
[INFO] Starting Worker(hosts=127.0.0.1:9092 topic=topic, group=group) ...Enqueue a function call:
import requests
from kafka import KafkaProducer
from kq import Queue
# Set up a Kafka producer.
producer = KafkaProducer(bootstrap_servers="127.0.0.1:9092")
# Set up a queue.
queue = Queue(topic="topic", producer=producer)
# Enqueue a function call.
job = queue.enqueue(requests.get, "https://google.com")
# You can also specify the job timeout, Kafka message key and partition.
job = queue.using(timeout=5, key=b"foo", partition=0).enqueue(requests.get, "https://google.com")The worker executes the job in the background:
python my_worker.py
[INFO] Starting Worker(hosts=127.0.0.1:9092, topic=topic, group=group) ...
[INFO] Processing Message(topic=topic, partition=0, offset=0) ...
[INFO] Executing job c7bf2359: requests.api.get("https://www.google.com")
[INFO] Job c7bf2359 returned: <Response [200]>See the documentation for more information.