Major fixes and new features

venv/lib/python3.12/site-packages/kafka_python-2.0.2.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: kafka-python
+Version: 2.0.2
+Summary: Pure Python client for Apache Kafka
+Home-page: https://github.com/dpkp/kafka-python
+Author: Dana Powers
+Author-email: dana.powers@gmail.com
+License: Apache License 2.0
+Keywords: apache kafka
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.4
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Provides-Extra: crc32c
+Requires-Dist: crc32c ; extra == 'crc32c'
+
+Kafka Python client
+------------------------
+
+.. image:: https://img.shields.io/badge/kafka-2.4%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
+    :target: https://kafka-python.readthedocs.io/en/master/compatibility.html
+.. image:: https://img.shields.io/pypi/pyversions/kafka-python.svg
+    :target: https://pypi.python.org/pypi/kafka-python
+.. image:: https://coveralls.io/repos/dpkp/kafka-python/badge.svg?branch=master&service=github
+    :target: https://coveralls.io/github/dpkp/kafka-python?branch=master
+.. image:: https://travis-ci.org/dpkp/kafka-python.svg?branch=master
+    :target: https://travis-ci.org/dpkp/kafka-python
+.. image:: https://img.shields.io/badge/license-Apache%202-blue.svg
+    :target: https://github.com/dpkp/kafka-python/blob/master/LICENSE
+
+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-python
+
+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:
+
+>>> 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)
+
+>>> # 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.
+
+>>> 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)
+
+>>> # 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()
+
+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 supports gzip compression/decompression natively. To produce or consume lz4
+compressed messages, you should install python-lz4 (pip install lz4).
+To enable snappy compression/decompression install python-snappy (also requires snappy library).
+See <https://kafka-python.readthedocs.io/en/master/install.html#optional-snappy-install>
+for more information.
+
+Optimized CRC32 Validation
+**************************
+
+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://pypi.org/project/crc32c/
+
+Protocol
+********
+
+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 2.4+).
+
+