Kafka Python client

This module provides low-level protocol support for Apache Kafka as well as high-level consumer and producer classes. Request batching is supported by the protocol as well as broker-aware request routing. Gzip and Snappy compression is also supported for message sets.

http://kafka.apache.org/

License

Copyright 2013, David Arthur under Apache License, v2.0. See LICENSE

Status

The current version of this package is 0.9.1 and is compatible with

Kafka broker versions

• 0.8.0
• 0.8.1
• 0.8.1.1

Python versions

• 2.6.9
• 2.7.6
• pypy 2.2.1

Usage

High level

from kafka.client import KafkaClient
from kafka.consumer import SimpleConsumer
from kafka.producer import SimpleProducer, KeyedProducer

kafka = KafkaClient("localhost:9092")

# To send messages synchronously
producer = SimpleProducer(kafka)
producer.send_messages("my-topic", "some message")
producer.send_messages("my-topic", "this method", "is variadic")

# To send messages asynchronously
producer = SimpleProducer(kafka, async=True)
producer.send_messages("my-topic", "async message")

# To wait for acknowledgements
# ACK_AFTER_LOCAL_WRITE : server will wait till the data is written to
#                         a local log before sending response
# ACK_AFTER_CLUSTER_COMMIT : server will block until the message is committed
#                            by all in sync replicas before sending a response
producer = SimpleProducer(kafka, async=False,
                          req_acks=SimpleProducer.ACK_AFTER_LOCAL_WRITE,
                          ack_timeout=2000)

response = producer.send_messages("my-topic", "async message")

if response:
    print(response[0].error)
    print(response[0].offset)

# To send messages in batch. You can use any of the available
# producers for doing this. The following producer will collect
# messages in batch and send them to Kafka after 20 messages are
# collected or every 60 seconds
# Notes:
# * If the producer dies before the messages are sent, there will be losses
# * Call producer.stop() to send the messages and cleanup
producer = SimpleProducer(kafka, batch_send=True,
                          batch_send_every_n=20,
                          batch_send_every_t=60)

# To consume messages
consumer = SimpleConsumer(kafka, "my-group", "my-topic")
for message in consumer:
    print(message)

kafka.close()

Keyed messages

from kafka.client import KafkaClient
from kafka.producer import KeyedProducer
from kafka.partitioner import HashedPartitioner, RoundRobinPartitioner

kafka = KafkaClient("localhost:9092")

# HashedPartitioner is default
producer = KeyedProducer(kafka)
producer.send("my-topic", "key1", "some message")
producer.send("my-topic", "key2", "this methode")

producer = KeyedProducer(kafka, partitioner=RoundRobinPartitioner)

Multiprocess consumer

from kafka.client import KafkaClient
from kafka.consumer import MultiProcessConsumer

kafka = KafkaClient("localhost:9092")

# This will split the number of partitions among two processes
consumer = MultiProcessConsumer(kafka, "my-group", "my-topic", num_procs=2)

# This will spawn processes such that each handles 2 partitions max
consumer = MultiProcessConsumer(kafka, "my-group", "my-topic",
                                partitions_per_proc=2)

for message in consumer:
    print(message)

for message in consumer.get_messages(count=5, block=True, timeout=4):
    print(message)

Low level

from kafka.client import KafkaClient
kafka = KafkaClient("localhost:9092")
req = ProduceRequest(topic="my-topic", partition=1,
    messages=[KafkaProdocol.encode_message("some message")])
resps = kafka.send_produce_request(payloads=[req], fail_on_error=True)
kafka.close()

resps[0].topic      # "my-topic"
resps[0].partition  # 1
resps[0].error      # 0 (hopefully)
resps[0].offset     # offset of the first message sent in this request

Install

Install with your favorite package manager

Pip:

git clone https://github.com/mumrah/kafka-python
pip install ./kafka-python

Setuptools:

git clone https://github.com/mumrah/kafka-python
easy_install ./kafka-python

Using setup.py directly:

git clone https://github.com/mumrah/kafka-python
cd kafka-python
python setup.py install

Optional Snappy install

Download and build Snappy from http://code.google.com/p/snappy/downloads/list

Linux:

wget http://snappy.googlecode.com/files/snappy-1.0.5.tar.gz
tar xzvf snappy-1.0.5.tar.gz
cd snappy-1.0.5
./configure
make
sudo make install

OSX:

brew install snappy

Install the python-snappy module

pip install python-snappy

Tests

Run the unit tests

tox

Run the integration tests

The integration tests will actually start up real local Zookeeper instance and Kafka brokers, and send messages in using the client.

Note that you may want to add this to your global gitignore:

.gradle/
clients/build/
contrib/build/
contrib/hadoop-consumer/build/
contrib/hadoop-producer/build/
core/build/
core/data/
examples/build/
perf/build/

First, check out and the Kafka source:

git submodule update --init
./build_integration.sh

Then run the tests against supported Kafka versions:

KAFKA_VERSION=0.8.0 tox
KAFKA_VERSION=0.8.1 tox