confluent-kafka-python is Confluent's Python client for Apache Kafka and the Confluent Platform.
Features:
-
High performance - confluent-kafka-python is a lightweight wrapper around librdkafka, a finely tuned C client.
-
Reliability - There are a lot of details to get right when writing an Apache Kafka client. We get them right in one place (librdkafka) and leverage this work across all of our clients (also confluent-kafka-go and confluent-kafka-dotnet).
-
Supported - Commercial support is offered by Confluent.
-
Future proof - Confluent, founded by the creators of Kafka, is building a streaming platform with Apache Kafka at its core. It's high priority for us that client features keep pace with core Apache Kafka and components of the Confluent Platform.
The Python bindings provides a high-level Producer and Consumer with support for the balanced consumer groups of Apache Kafka >= 0.9.
See the API documentation for more info.
License: Apache License v2.0
Producer:
from confluent_kafka import Producer
p = Producer({'bootstrap.servers': 'mybroker1,mybroker2'})
def delivery_report(err, msg):
""" Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). """
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))
for data in some_data_source:
# Trigger any available delivery report callbacks from previous produce() calls
p.poll(0)
# Asynchronously produce a message, the delivery report callback
# will be triggered from poll() above, or flush() below, when the message has
# been successfully delivered or failed permanently.
p.produce('mytopic', data.encode('utf-8'), callback=delivery_report)
# Wait for any outstanding messages to be delivered and delivery report
# callbacks to be triggered.
p.flush()High-level Consumer:
from confluent_kafka import Consumer, KafkaError
c = Consumer({
'bootstrap.servers': 'mybroker',
'group.id': 'mygroup',
'default.topic.config': {
'auto.offset.reset': 'smallest'
}
})
c.subscribe(['mytopic'])
while True:
msg = c.poll(1.0)
if msg is None:
continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
continue
else:
print(msg.error())
break
print('Received message: {}'.format(msg.value().decode('utf-8')))
c.close()AvroProducer
from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer
value_schema_str = """
{
"namespace": "my.test",
"name": "value",
"type": "record",
"fields" : [
{
"name" : "name",
"type" : "string"
}
]
}
"""
key_schema_str = """
{
"namespace": "my.test",
"name": "key",
"type": "record",
"fields" : [
{
"name" : "name",
"type" : "string"
}
]
}
"""
value_schema = avro.loads(value_schema_str)
key_schema = avro.loads(key_schema_str)
value = {"name": "Value"}
key = {"name": "Key"}
avroProducer = AvroProducer({
'bootstrap.servers': 'mybroker,mybroker2',
'schema.registry.url': 'http://schem_registry_host:port'
}, default_key_schema=key_schema, default_value_schema=value_schema)
avroProducer.produce(topic='my_topic', value=value, key=key)
avroProducer.flush()AvroConsumer
from confluent_kafka import KafkaError
from confluent_kafka.avro import AvroConsumer
from confluent_kafka.avro.serializer import SerializerError
c = AvroConsumer({
'bootstrap.servers': 'mybroker,mybroker2',
'group.id': 'groupid',
'schema.registry.url': 'http://127.0.0.1:8081'})
c.subscribe(['my_topic'])
while True:
try:
msg = c.poll(10)
except SerializerError as e:
print("Message deserialization failed for {}: {}".format(msg, e))
break
if msg is None:
continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
continue
else:
print(msg.error())
break
print(msg.value())
c.close()See the examples directory for more examples, including how to configure the python client for use with Confluent Cloud.
Install self-contained binary wheels for OSX and Linux from PyPi:
$ pip install confluent-kafka
Install AvroProducer and AvroConsumer:
$ pip install confluent-kafka[avro]
Install from source from PyPi (requires librdkafka + dependencies to be installed separately):
$ pip install --no-binary :all: confluent-kafka
For source install, see Prerequisites below.
The Python client (as well as the underlying C library librdkafka) supports all broker versions >= 0.8. But due to the nature of the Kafka protocol in broker versions 0.8 and 0.9 it is not safe for a client to assume what protocol version is actually supported by the broker, thus you will need to hint the Python client what protocol version it may use. This is done through two configuration settings:
broker.version.fallback=YOUR_BROKER_VERSION(default 0.9.0.1)api.version.request=true|false(default true)
When using a Kafka 0.10 broker or later you don't need to do anything
(api.version.request=true is the default).
If you use Kafka broker 0.9 or 0.8 you must set
api.version.request=false and set
broker.version.fallback to your broker version,
e.g broker.version.fallback=0.9.0.1.
More info here: https://github.com/edenhill/librdkafka/wiki/Broker-version-compatibility
- Python >= 2.7 or Python 3.x
- librdkafka >= 0.9.5 (latest release is embedded in wheels)
librdkafka is embedded in the macosx manylinux wheels, for other platforms or when a specific version of librdkafka is desired, following these guidelines:
-
For Debian/Ubuntu based systems, add this APT repo and then do
sudo apt-get install librdkafka-dev python-dev: http://docs.confluent.io/current/installation.html#installation-apt -
For RedHat and RPM-based distros, add this YUM repo and then do
sudo yum install librdkafka-devel python-devel: http://docs.confluent.io/current/installation.html#rpm-packages-via-yum -
On OSX, use homebrew and do
brew install librdkafka
NOTE: The pre-built Linux wheels do NOT contain SASL Kerberos support. If you need SASL Kerberos support you must install librdkafka and its dependencies using the above repositories and then build confluent-kafka from source.
$ python setup.py build
If librdkafka is installed in a non-standard location provide the include and library directories with:
$ C_INCLUDE_PATH=/path/to/include LIBRARY_PATH=/path/to/lib python setup.py ...
Run unit-tests:
In order to run full test suite, simply execute:
$ tox -r
NOTE: Requires tox (please install with pip install tox), several supported versions of Python on your path, and librdkafka installed into tmp-build.
Run integration tests:
To run the integration tests, uncomment the following line from tox.ini and add the paths to your Kafka and Confluent Schema Registry instances. You can also run the integration tests outside of tox by running this command from the source root.
examples/integration_test.py <kafka-broker> [<test-topic>] [<schema-registry>]
WARNING: These tests require an active Kafka cluster and will create new topics.
Install sphinx and sphinx_rtd_theme packages:
$ pip install sphinx sphinx_rtd_theme
Build HTML docs:
$ make docs
or:
$ python setup.py build_sphinx
Documentation will be generated in docs/_build/.