Python client for the Apache Kafka distributed stream processing system. kafka-python is designed to function much like the official java client, with a sprinkling of pythonic interfaces (e.g., consumer iterators).
Please note that the master branch may contain unreleased features. For release documentation, please see readthedocs and/or python's inline help.
New in 2.3 release: python -m kafka.* interfaces for quick scripts and testing.
$ pip install kafka-pythonKafkaConsumer 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:
from kafka import KafkaConsumer consumer = KafkaConsumer('my_favorite_topic') for msg in consumer: print (msg)# 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)# 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)# Deserialize msgpack-encoded values consumer = KafkaConsumer(value_deserializer=msgpack.loads) consumer.subscribe(['msgpackfoo']) for msg in consumer: assert isinstance(msg.value, dict)# 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)# Read only committed messages from transactional topic consumer = KafkaConsumer(isolation_level='read_committed') consumer.subscribe(['txn_topic']) for msg in consumer: print(msg)# Get consumer metrics metrics = consumer.metrics()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.
from kafka import KafkaProducer producer = KafkaProducer(bootstrap_servers='localhost:1234') for _ in range(100): producer.send('foobar', b'some_message_bytes')# Block until a single message is sent (or timeout) future = producer.send('foobar', b'another_message') result = future.get(timeout=60)# 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()# Use a key for hashed-partitioning producer.send('foobar', key=b'foo', value=b'bar')# Serialize json messages import json producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8')) producer.send('fizzbuzz', {'foo': 'bar'})# Serialize string keys producer = KafkaProducer(key_serializer=str.encode) producer.send('flipflap', key='ping', value=b'1234')# Compress messages producer = KafkaProducer(compression_type='gzip') for i in range(1000): producer.send('foobar', b'msg %d' % i)# Use transactions producer = KafkaProducer(transactional_id='fizzbuzz') producer.init_transactions() producer.begin_transaction() future = producer.send('txn_topic', value=b'yes') future.get() # wait for successful produce producer.commit_transaction() # commit the transaction producer.begin_transaction() future = producer.send('txn_topic', value=b'no') future.get() # wait for successful produce producer.abort_transaction() # abort the transaction# 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')])# Get producer performance metrics metrics = producer.metrics()kafka-python also provides simple command-line interfaces for consumer, producer, and admin clients. Access via python -m kafka.consumer, python -m kafka.producer, and python -m kafka.admin. See https://kafka-python.readthedocs.io/en/master/usage.html for more details.
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.
kafka-python 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.
Kafka uses CRC32 checksums to validate messages. kafka-python 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.
A secondary goal of kafka-python 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+).
Use python's logging module to view internal operational events. See https://docs.python.org/3/howto/logging.html for overview / howto.
import logging logging.basicConfig(level=logging.DEBUG)
