Pulsar Python client

You can use a Pulsar Python client to create producers, consumers, and readers. For Pulsar features that Python clients support, see Client Feature Matrix.

Installation

Use pip to install the latest version:

pip install 'pulsar-client==2.11.0'

You can install optional components alongside the client library:

# avro serialization
pip install 'pulsar-client[avro]==2.11.0'

# functions runtime
pip install 'pulsar-client[functions]==2.11.0'

# all optional components
pip install 'pulsar-client[all]==2.11.0'

Installation via PyPi is available for the following Python versions:

Platform	Supported Python versions
macOS (>= 11.0)	3.7, 3.8, 3.9 and 3.10
Linux (including Alpine Linux)	3.7, 3.8, 3.9 and 3.10

Connection URLs

To connect to Pulsar using client libraries, you need to specify a Pulsar protocol URL.

You can assign Pulsar protocol URLs to specific clusters and use the pulsar scheme. The following is an example of localhost with the default port 6650:

pulsar://localhost:6650

If you have multiple brokers, separate IP:port by commas:

pulsar://localhost:6550,localhost:6651,localhost:6652

If you use TLS authentication, add +ssl in the scheme:

pulsar+ssl://pulsar.us-west.example.com:6651

API reference

All the methods in producer, consumer, and reader of Pulsar Python clients are thread-safe. See the API docs for more details.

Release notes

For the changelog of Pulsar Python clients, see release notes.

Examples

You can find a variety of Python code examples for the pulsar-client library.

Producer example

The following example creates a Python producer for the my-topic topic and sends 10 messages on that topic:

import pulsar

client = pulsar.Client('pulsar://localhost:6650')

producer = client.create_producer('my-topic')

for i in range(10):
    producer.send(('Hello-%d' % i).encode('utf-8'))

client.close()

Enable Chunking

Message chunking enables Pulsar to process large payload messages by splitting the message into chunks at the producer side and aggregating chunked messages on the consumer side.

The message chunking feature is OFF by default. The following is an example of how to enable message chunking when creating a producer.

producer = client.create_producer(
            topic,
            chunking_enabled=True
        )

By default, producer chunks the large message based on max message size (maxMessageSize) configured at broker (eg: 5MB).

note

To enable chunking, you need to disable batching (chunking_enabled=false) concurrently.

Consumer example

The following example creates a consumer with the my-subscription subscription name on the my-topic topic, receives incoming messages, prints the content and ID of messages that arrive, and acknowledges each message to the Pulsar broker.

import pulsar

client = pulsar.Client('pulsar://localhost:6650')

consumer = client.subscribe('my-topic', 'my-subscription')

while True:
    msg = consumer.receive()
    try:
        print("Received message '{}' id='{}'".format(msg.data(), msg.message_id()))
        # Acknowledge successful processing of the message
        consumer.acknowledge(msg)
    except Exception:
        # Message failed to be processed
        consumer.negative_acknowledge(msg)

client.close()

This example shows how to configure negative acknowledgment.

from pulsar import Client, schema
client = Client('pulsar://localhost:6650')
consumer = client.subscribe('negative_acks','test',schema=schema.StringSchema())
producer = client.create_producer('negative_acks',schema=schema.StringSchema())
for i in range(10):
    print('send msg "hello-%d"' % i)
    producer.send_async('hello-%d' % i, callback=None)
producer.flush()
for i in range(10):
    msg = consumer.receive()
    consumer.negative_acknowledge(msg)
    print('receive and nack msg "%s"' % msg.data())
for i in range(10):
    msg = consumer.receive()
    consumer.acknowledge(msg)
    print('receive and ack msg "%s"' % msg.data())
try:
    # No more messages expected
    msg = consumer.receive(100)
except:
    print("no more msg")
    pass

Configure chunking

You can limit the maximum number of chunked messages a consumer maintains concurrently by configuring the max_pending_chunked_message and auto_ack_oldest_chunked_message_on_queue_full parameters. When the threshold is reached, the consumer drops pending messages by silently acknowledging them or asking the broker to redeliver them later.

The following is an example of how to configure message chunking.

consumer = client.subscribe(topic, "my-subscription",
                                max_pending_chunked_message=10,
                                auto_ack_oldest_chunked_message_on_queue_full=False
                                )

Reader interface example

You can use the Pulsar Python API to use the Pulsar reader interface. Here's an example:

# MessageId taken from a previously fetched message
msg_id = msg.message_id()

reader = client.create_reader('my-topic', msg_id)

while True:
    msg = reader.read_next()
    print("Received message '{}' id='{}'".format(msg.data(), msg.message_id()))
    # No acknowledgment

Multi-topic subscriptions

In addition to subscribing a consumer to a single Pulsar topic, you can also subscribe to multiple topics simultaneously. To use multi-topic subscriptions, you can supply a regular expression (regex) or a List of topics. If you select topics via regex, all topics must be within the same Pulsar namespace.

The following is an example:

import re
consumer = client.subscribe(re.compile('persistent://public/default/topic-*'), 'my-subscription')
while True:
    msg = consumer.receive()
    try:
        print("Received message '{}' id='{}'".format(msg.data(), msg.message_id()))
        # Acknowledge successful processing of the message
        consumer.acknowledge(msg)
    except Exception:
        # Message failed to be processed
        consumer.negative_acknowledge(msg)
client.close()

Create a Python client with multiple advertised listeners

To ensure clients in both internal and external networks can connect to a Pulsar cluster, Pulsar introduces advertisedListeners.

The following example creates a Python client using multiple advertised listeners:

import pulsar

client = pulsar.Client('pulsar://localhost:6650', listener_name='external')

Schema

Supported schema types

You can use different built-in schema types in Pulsar. All the definitions are in the pulsar.schema package.

Schema	Notes
BytesSchema	Get the raw payload as a bytes object. No serialization/deserialization are performed. This is the default schema mode
StringSchema	Encode/decode payload as a UTF-8 string. Uses str objects
JsonSchema	Require record definition. Serializes the record into standard JSON payload
AvroSchema	Require record definition. Serializes in AVRO format

Schema definition reference

The schema definition is done through a class that inherits from pulsar.schema.Record.

This class has a number of fields that can be of either pulsar.schema.Field type or another nested Record. All the fields are specified in the pulsar.schema package. The fields are matching the AVRO field types.

Field Type	Python Type	Notes
Boolean	bool
Integer	int
Long	int
Float	float
Double	float
Bytes	bytes
String	str
Array	list	Need to specify record type for items.
Map	dict	Key is always String. Need to specify value type.

Additionally, any Python Enum type can be used as a valid field type.

Fields parameters

When adding a field, you can use these parameters in the constructor.

Argument	Default	Notes
default	None	Set a default value for the field, such as a = Integer(default=5).
required	False	Mark the field as "required". It is set in the schema accordingly.

Schema definition examples

Simple definition

class Example(Record):
    a = String()
    b = Integer()
    c = Array(String())
    i = Map(String())

Using enums

from enum import Enum

class Color(Enum):
    red = 1
    green = 2
    blue = 3

class Example(Record):
    name = String()
    color = Color

Complex types

class MySubRecord(Record):
    x = Integer()
    y = Long()
    z = String()

class Example(Record):
    a = String()
    sub = MySubRecord()

Set namespace for Avro schema

Set the namespace for the Avro Record schema using the special field _avro_namespace.

class NamespaceDemo(Record):
   _avro_namespace = 'xxx.xxx.xxx'
   x = String()
   y = Integer()

The schema definition is like this.

{
  "name": "NamespaceDemo", "namespace": "xxx.xxx.xxx", "type": "record", "fields": [
    {"name": "x", "type": ["null", "string"]},
    {"name": "y", "type": ["null", "int"]}
  ]
}

Declare and validate schema

Before the producer is created, the Pulsar broker validates that the existing topic schema is the correct type and that the format is compatible with the schema definition of a class. If the format of the topic schema is incompatible with the schema definition, an exception occurs in the producer creation.

Once a producer is created with a certain schema definition, it only accepts objects that are instances of the declared schema class.

Similarly, for a consumer or reader, the consumer returns an object (which is an instance of the schema record class) rather than raw bytes.

Example

consumer = client.subscribe(
                  topic='my-topic',
                  subscription_name='my-subscription',
                  schema=AvroSchema(Example) )

while True:
    msg = consumer.receive()
    ex = msg.value()
    try:
        print("Received message a={} b={} c={}".format(ex.a, ex.b, ex.c))
        # Acknowledge successful processing of the message
        consumer.acknowledge(msg)
    except Exception:
        # Message failed to be processed
        consumer.negative_acknowledge(msg)

For more code examples, see Schema - Get started.

End-to-end encryption

Pulsar encryption allows applications to encrypt messages at producers and decrypt messages at consumers. See Get started for more details.