Kafka Python client
------------------------
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Python client for the Apache Kafka distributed stream processing system.
kafka-python-ng is designed to function much like the official java client, with a
sprinkling of pythonic interfaces (e.g., consumer iterators).
kafka-python-ng is best used with newer brokers (0.9+), but is backwards-compatible with
older versions (to 0.8.0). Some features will only be enabled on newer brokers.
For example, fully coordinated consumer groups -- i.e., dynamic partition
assignment to multiple consumers in the same group -- requires use of 0.9+ kafka
brokers. Supporting this feature for earlier broker releases would require
writing and maintaining custom leadership election and membership / health
check code (perhaps using zookeeper or consul). For older brokers, you can
achieve something similar by manually assigning different partitions to each
consumer instance with config management tools like chef, ansible, etc. This
approach will work fine, though it does not support rebalancing on failures.
See https://kafka-python.readthedocs.io/en/master/compatibility.html
for more details.
Please note that the master branch may contain unreleased features. For release
documentation, please see readthedocs and/or python's inline help.
.. code-block:: bash
$ pip install kafka-python-ng
KafkaConsumer
*************
KafkaConsumer is a high-level message consumer, intended to operate as similarly
as possible to the official java client. Full support for coordinated
consumer groups requires use of kafka brokers that support the Group APIs: kafka v0.9+.
See https://kafka-python.readthedocs.io/en/master/apidoc/KafkaConsumer.html
for API and configuration details.
The consumer iterator returns ConsumerRecords, which are simple namedtuples
that expose basic message attributes: topic, partition, offset, key, and value:
.. code-block:: python
# join a consumer group for dynamic partition assignment and offset commits
from kafka import KafkaConsumer
consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')
# or as a static member with a fixed group member name
# consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group',
# group_instance_id='consumer-1', leave_group_on_close=False)
for msg in consumer:
print (msg)
.. code-block:: python
# join a consumer group for dynamic partition assignment and offset commits
from kafka import KafkaConsumer
consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')
for msg in consumer:
print (msg)
.. code-block:: python
# manually assign the partition list for the consumer
from kafka import TopicPartition
consumer = KafkaConsumer(bootstrap_servers='localhost:1234')
consumer.assign([TopicPartition('foobar', 2)])
msg = next(consumer)
.. code-block:: python
# Deserialize msgpack-encoded values
consumer = KafkaConsumer(value_deserializer=msgpack.loads)
consumer.subscribe(['msgpackfoo'])
for msg in consumer:
assert isinstance(msg.value, dict)
.. code-block:: python
# Access record headers. The returned value is a list of tuples
# with str, bytes for key and value
for msg in consumer:
print (msg.headers)
.. code-block:: python
# Get consumer metrics
metrics = consumer.metrics()
KafkaProducer
*************
KafkaProducer is a high-level, asynchronous message producer. The class is
intended to operate as similarly as possible to the official java client.
See https://kafka-python.readthedocs.io/en/master/apidoc/KafkaProducer.html
for more details.
.. code-block:: python
from kafka import KafkaProducer
producer = KafkaProducer(bootstrap_servers='localhost:1234')
for _ in range(100):
producer.send('foobar', b'some_message_bytes')
.. code-block:: python
# Block until a single message is sent (or timeout)
future = producer.send('foobar', b'another_message')
result = future.get(timeout=60)
.. code-block:: python
# Block until all pending messages are at least put on the network
# NOTE: This does not guarantee delivery or success! It is really
# only useful if you configure internal batching using linger_ms
producer.flush()
.. code-block:: python
# Use a key for hashed-partitioning
producer.send('foobar', key=b'foo', value=b'bar')
.. code-block:: python
# Serialize json messages
import json
producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8'))
producer.send('fizzbuzz', {'foo': 'bar'})
.. code-block:: python
# Serialize string keys
producer = KafkaProducer(key_serializer=str.encode)
producer.send('flipflap', key='ping', value=b'1234')
.. code-block:: python
# Compress messages
producer = KafkaProducer(compression_type='gzip')
for i in range(1000):
producer.send('foobar', b'msg %d' % i)
.. code-block:: python
# Include record headers. The format is list of tuples with string key
# and bytes value.
producer.send('foobar', value=b'c29tZSB2YWx1ZQ==', headers=[('content-encoding', b'base64')])
.. code-block:: python
# Get producer performance metrics
metrics = producer.metrics()
Thread safety
*************
The KafkaProducer can be used across threads without issue, unlike the
KafkaConsumer which cannot.
While it is possible to use the KafkaConsumer in a thread-local manner,
multiprocessing is recommended.
Compression
***********
kafka-python-ng supports the following compression formats:
- gzip
- LZ4
- Snappy
- Zstandard (zstd)
gzip is supported natively, the others require installing additional libraries.
See https://kafka-python.readthedocs.io/en/master/install.html for more information.
Optimized CRC32 Validation
**************************
Kafka uses CRC32 checksums to validate messages. kafka-python-ng includes a pure
python implementation for compatibility. To improve performance for high-throughput
applications, kafka-python will use `crc32c` for optimized native code if installed.
See https://kafka-python.readthedocs.io/en/master/install.html for installation instructions.
See https://pypi.org/project/crc32c/ for details on the underlying crc32c lib.
Protocol
********
A secondary goal of kafka-python-ng is to provide an easy-to-use protocol layer
for interacting with kafka brokers via the python repl. This is useful for
testing, probing, and general experimentation. The protocol support is
leveraged to enable a KafkaClient.check_version() method that
probes a kafka broker and attempts to identify which version it is running
(0.8.0 to 2.6+).
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"description": "Kafka Python client\n------------------------\n\n.. image:: https://img.shields.io/badge/kafka-2.6%2C%202.5%2C%202.4%2C%202.3%2C%202.2%2C%202.1%2C%202.0%2C%201.1%2C%201.0%2C%200.11%2C%200.10%2C%200.9%2C%200.8-brightgreen.svg\n :target: https://kafka-python-ng.readthedocs.io/en/master/compatibility.html\n.. image:: https://img.shields.io/pypi/pyversions/kafka-python-ng.svg\n :target: https://pypi.python.org/pypi/kafka-python-ng\n.. image:: https://coveralls.io/repos/wbarnha/kafka-python-ng/badge.svg?branch=master&service=github\n :target: https://coveralls.io/github/wbarnha/kafka-python-ng?branch=master\n.. image:: https://img.shields.io/badge/license-Apache%202-blue.svg\n :target: https://github.com/wbarnha/kafka-python-ng/blob/master/LICENSE\n.. image:: https://img.shields.io/pypi/dw/kafka-python-ng.svg\n :target: https://pypistats.org/packages/kafka-python-ng\n.. image:: https://img.shields.io/pypi/v/kafka-python.svg\n :target: https://pypi.org/project/kafka-python-ng\n.. image:: https://img.shields.io/pypi/implementation/kafka-python-ng\n :target: https://github.com/wbarnha/kafka-python-ng/blob/master/setup.py\n\n\n\nPython client for the Apache Kafka distributed stream processing system.\nkafka-python-ng is designed to function much like the official java client, with a\nsprinkling of pythonic interfaces (e.g., consumer iterators).\n\nkafka-python-ng is best used with newer brokers (0.9+), but is backwards-compatible with\nolder versions (to 0.8.0). Some features will only be enabled on newer brokers.\nFor example, fully coordinated consumer groups -- i.e., dynamic partition\nassignment to multiple consumers in the same group -- requires use of 0.9+ kafka\nbrokers. Supporting this feature for earlier broker releases would require\nwriting and maintaining custom leadership election and membership / health\ncheck code (perhaps using zookeeper or consul). For older brokers, you can\nachieve something similar by manually assigning different partitions to each\nconsumer instance with config management tools like chef, ansible, etc. This\napproach will work fine, though it does not support rebalancing on failures.\n\nSee https://kafka-python.readthedocs.io/en/master/compatibility.html\n\nfor more details.\n\nPlease note that the master branch may contain unreleased features. For release\ndocumentation, please see readthedocs and/or python's inline help.\n\n\n.. code-block:: bash \n\n $ pip install kafka-python-ng\n\n\n\nKafkaConsumer\n*************\n\nKafkaConsumer is a high-level message consumer, intended to operate as similarly\nas possible to the official java client. Full support for coordinated\nconsumer groups requires use of kafka brokers that support the Group APIs: kafka v0.9+.\n\n\nSee https://kafka-python.readthedocs.io/en/master/apidoc/KafkaConsumer.html\n\nfor API and configuration details.\n\nThe consumer iterator returns ConsumerRecords, which are simple namedtuples\nthat expose basic message attributes: topic, partition, offset, key, and value:\n\n.. code-block:: python\n\n # join a consumer group for dynamic partition assignment and offset commits\n from kafka import KafkaConsumer\n consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')\n # or as a static member with a fixed group member name\n # consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group',\n # group_instance_id='consumer-1', leave_group_on_close=False)\n for msg in consumer:\n print (msg)\n\n.. code-block:: python\n\n # join a consumer group for dynamic partition assignment and offset commits\n from kafka import KafkaConsumer\n consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')\n for msg in consumer:\n print (msg)\n\n.. code-block:: python\n\n # manually assign the partition list for the consumer\n from kafka import TopicPartition\n consumer = KafkaConsumer(bootstrap_servers='localhost:1234')\n consumer.assign([TopicPartition('foobar', 2)])\n msg = next(consumer)\n\n.. code-block:: python\n\n # Deserialize msgpack-encoded values\n consumer = KafkaConsumer(value_deserializer=msgpack.loads)\n consumer.subscribe(['msgpackfoo'])\n for msg in consumer:\n assert isinstance(msg.value, dict)\n\n.. code-block:: python\n\n # Access record headers. The returned value is a list of tuples\n # with str, bytes for key and value\n for msg in consumer:\n print (msg.headers)\n\n.. code-block:: python\n\n # Get consumer metrics\n metrics = consumer.metrics()\n\n\nKafkaProducer\n*************\n\nKafkaProducer is a high-level, asynchronous message producer. The class is\nintended to operate as similarly as possible to the official java client.\n\nSee https://kafka-python.readthedocs.io/en/master/apidoc/KafkaProducer.html\n\nfor more details.\n\n.. code-block:: python\n\n from kafka import KafkaProducer\n producer = KafkaProducer(bootstrap_servers='localhost:1234')\n for _ in range(100):\n producer.send('foobar', b'some_message_bytes')\n\n.. code-block:: python\n\n # Block until a single message is sent (or timeout)\n future = producer.send('foobar', b'another_message')\n result = future.get(timeout=60)\n\n.. code-block:: python\n\n # Block until all pending messages are at least put on the network\n # NOTE: This does not guarantee delivery or success! It is really\n # only useful if you configure internal batching using linger_ms\n producer.flush()\n\n.. code-block:: python\n\n # Use a key for hashed-partitioning\n producer.send('foobar', key=b'foo', value=b'bar')\n\n.. code-block:: python\n\n # Serialize json messages\n import json\n producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8'))\n producer.send('fizzbuzz', {'foo': 'bar'})\n\n.. code-block:: python\n\n # Serialize string keys\n producer = KafkaProducer(key_serializer=str.encode)\n producer.send('flipflap', key='ping', value=b'1234')\n\n.. code-block:: python\n\n # Compress messages\n producer = KafkaProducer(compression_type='gzip')\n for i in range(1000):\n producer.send('foobar', b'msg %d' % i)\n\n.. code-block:: python\n\n # Include record headers. 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