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).
kafka-python 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.
$ 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()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+).
