Skip to content

SomeNeologism/kafka-python

master
Switch branches/tags
Code
This branch is 1891 commits behind dpkp:master.
Contribute

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Kafka Python client

Build Status

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.

Compatible with Apache Kafka 0.8.1

http://kafka.apache.org/

License

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

Status

The current version of this package is 0.9.0 and is compatible with Kafka brokers running version 0.8.1.

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

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

Install the python-snappy module

pip install python-snappy

Tests

Run the unit tests

These are broken at the moment

tox ./test/test_unit.py

or

python -m test.test_unit

Run the integration tests

First, checkout the Kafka source

git submodule init
git submodule update
cd kafka-src
./sbt update
./sbt package
./sbt assembly-package-dependency

And then run the tests. This will actually start up real local Zookeeper instance and Kafka brokers, and send messages in using the client.

tox ./test/test_integration.py

or

python -m test.test_integration

About

Kafka protocol support in Python

Resources

License

Stars

Watchers

Forks

Packages

No packages published