commlib-py 0.11.4

Internal DSL for communication and messaging in CyberPhysical Systems

commlib-py

Commlib is an internal DSL for communication and messaging in Cyber-Physical Systems. Can be used for rapid development of the communication layer on-device, at the Edge and on the Cloud.

The goal of this project is to implement a simple Protocol-agnostic API (AMQP, Kafka, Redis, MQTT, etc) for common communication patterns in the context of Cyber-Physical Systems, using message broker technologies. Such patterns include PubSub, RPC and Preemptive Services (aka Actions), among others.

Installation

Install from PyPi:

pip install commlib-py

Alternatively, download this repo and install using setuptools:

git clone [e-mail]:robotics-4-all/commlib-py.git
cd commlib-py && python setup.py install

Commlib can also be used in a virtual envirtonment

python -m venv myvenv
pip install commlib-py

JSON Serialization

It is recommended to use a fast json library, such as orjson or ujson.

The framework will load and use the most performance optimal library based on installations. The default is ujson.

Guide

Node

A Node is a software component that follows the Component-Port-Connector model. It has input and output ports for communicating with the world. Each port defines an endpoint and can be of the following types.

Input Port:

• Subscriber
• RPC Service
• Action Service

Output Port:

• Publisher
• RPC Client
• Action Client

InOut Port:

• RPCBridge: Bridge RPC Communication between two brokers. Directional.
• TopicBridge: Bridge PubSub Communication between two brokers. Directional.
• PTopicBridge: Bridge PubSub Communication between two brokers, based on a topic pattern. Directional.

Furthermore, it implements several features:

• Publish Heartbeat messages in the background for as long as the node is active
• Provide control interfaces, to start and stop the execution of the Node
• Provides methods to create endpoints and bind to Node ports.

from commlib.node import Node, TransportType
from commlib.msg import RPCMessage
## Import the Redis transports
## Imports are lazy handled internally
from commlib.transports.redis import ConnectionParameters


class AddTwoIntMessage(RPCMessage):
    class Request(RPCMessage.Request):
        a: int = 0
        b: int = 0

    class Response(RPCMessage.Response):
        c: int = 0


def on_request(msg):
    print(f'On-Request: {msg}')
    resp = AddTwoIntMessage.Response(c = msg.a + msg.b)
    return resp


if __name__ == '__main__':
    conn_params = ConnectionParameters()
    node = Node(node_name='add_two_ints_node',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)
    rpc = node.create_rpc(msg_type=AddTwoIntMessage,
                          # rpc_name='add_two_ints_node.add_two_ints',
                          on_request=add_two_int_handler)
    node.run_forever(sleep_rate=1)

A Node always binds to a specific broker for implementing the input and output ports. Of course you can instantiate and run several Nodes in a single-process application.

Node class:

class Node:
    
    def __init__(self,
                 node_name: Optional[str] = '',
                 connection_params: Optional[Any] = None,
                 transport_connection_params: Optional[Any] = None,
                 debug: Optional[bool] = False,
                 heartbeats: Optional[bool] = True,
                 heartbeat_uri: Optional[str] = None,
                 compression: CompressionType = CompressionType.NO_COMPRESSION,
                 ctrl_services: Optional[bool] = False):

Node methods to create and run Endpoints::

Node:
   create_action(self, *args, **kwargs)
   create_action_client(self, *args, **kwargs)
   create_event_emitter(self, *args, **kwargs)
   create_heartbeat_thread(self)
   create_mpublisher(self, *args, **kwargs)
   create_psubscriber(self, *args, **kwargs)
   create_publisher(self, *args, **kwargs)
   create_rpc(self, *args, **kwargs)
   create_rpc_client(self, *args, **kwargs)
   create_start_service(self, uri: str = None)
   create_stop_service(self, uri: str = None)
   create_subscriber(self, *args, **kwargs)
   run_forever(self, sleep_rate: float = 0.001)
   run(self)
   stop(self)

Endpoint (Low-level API)

It is possible to construct endpoints without binding them to a specific Node. This is a feature to support a wider range of applications, where the concept Node might not be usable.

One can create endpoint instances by using the following classes of each supported transport

• RPCClient
• RPCServer
• Publisher
• Subscriber
• MPublisher (Multi-topic Publisher)
• PSubscriber (Pattern-based Subscriber)
• ActionService (Preemptable Services with feedback)
• ActionClient

from commlib.transports.redis import RPCService
from commlib.transports.amqp import Subscriber
from commlib.transports.mqtt import Publisher, RPCClient
...

Or use the endpoint_factory to construct endpoints.

import time
from commlib.endpoints import endpoint_factory, EndpointType, TransportType


def callback(data):
    print(data)


if __name__ == '__main__':
    topic = 'factory_test_topic'
    mqtt_sub = endpoint_factory(
        EndpointType.Subscriber,
        TransportType.MQTT
    )(topic=topic, on_message=callback)
    mqtt_sub.run()
    mqtt_pub = endpoint_factory(
        EndpointType.Publisher,
        TransportType.MQTT
    )(topic=topic, debug=True)

    data = {'a': 1, 'b': 2}
    while True:
        mqtt_pub.publish(data)
        time.sleep(1)

Communication Patters

Req/Resp (RPC) Communication

Server Side Example

from commlib.msg import RPCMessage
from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters


class AddTwoIntMessage(RPCMessage):
    class Request(RPCMessage.Request):
        a: int = 0
        b: int = 0

    class Response(RPCMessage.Response):
        c: int = 0


def add_two_int_handler(msg):
    print(f'Request Message: {msg.__dict__}')
    resp = AddTwoIntMessage.Response(c = msg.a + msg.b)
    return resp


if __name__ == '__main__':
    conn_params = ConnectionParameters()
    node = Node(
        node_name='add_two_ints_node',
        connection_params=conn_params,
        # heartbeat_uri='nodes.add_two_ints.heartbeat',
        debug=True
    )
    rpc = node.create_rpc(
        msg_type=AddTwoIntMessage,
        rpc_name='add_two_ints_node.add_two_ints',
        on_request=add_two_int_handler
    )
    node.run_forever(sleep_rate=1)

Client Side Example

import time

from commlib.msg import RPCMessage
from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters


class AddTwoIntMessage(RPCMessage):
    class Request(RPCMessage.Request):
        a: int = 0
        b: int = 0

    class Response(RPCMessage.Response):
        c: int = 0


if __name__ == '__main__':
    conn_params = ConnectionParameters()
    node = Node(node_name='myclient',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)
    rpc = node.create_rpc_client(
        msg_type=AddTwoIntMessage,
        rpc_name='add_two_ints_node.add_two_ints'
    )
    node.run()

    # Create an instance of the request object
    msg = AddTwoIntMessage.Request()
    while True:
        # returns AddTwoIntMessage.Response instance
        resp = rpc.call(msg)
        print(resp)
        msg.a += 1
        msg.b += 1
        time.sleep(1)

PubSub Communicaton

Traditional Topic-based Publish-Subscribe pattern for asynchronous communication as depicted below.

An example of using PubSub communication is located at examples/simple_pubsub.

Write a Simple Topic Publisher

from commlib.msg import MessageHeader, PubSubMessage
from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters

class SonarMessage(PubSubMessage):
    header: MessageHeader = MessageHeader()
    range: float = -1
    hfov: float = 30.6
    vfov: float = 14.2


class SonarMessage(PubSubMessage):
    distance: float = 0.001
    horizontal_fov: float = 30.0
    vertical_fov: float = 14.0


if __name__ == "__main__":
    conn_params = ConnectionParameters(host='localhost', port=1883)
    node = Node(node_name='sensors.sonar.front',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)

    pub = node.create_publisher(msg_type=SonarMessage,
                                topic='sensors.sonar.front')
    node.run()

    msg = SonarMessage()
    while True:
        pub.publish(msg)
        msg.range += 1
        time.sleep(1)

Write a Simple Topic Subscriber

#!/usr/bin/env python

import time

from commlib.msg import MessageHeader, PubSubMessage
from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters


class SonarMessage(PubSubMessage):
    header: MessageHeader = MessageHeader()
    range: float = -1
    hfov: float = 30.6
    vfov: float = 14.2


def on_message(msg):
    print(f'Received front sonar data: {msg}')


if __name__ == '__main__':
    conn_params = ConnectionParameters()

    node = Node(node_name='obstacle_avoidance_node',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)

    node.create_subscriber(msg_type=SonarMessage,
                           topic='sensors.sonar.front',
                           on_message=on_message)

    node.run_forever(sleep_rate=1)

Pattern-based Topic Subscription

For pattern-based topic subscription one can also use the PSubscriber class directly.

For multi-topic publisher one can also use the MPublisher class directly.

#!/usr/bin/env python

##
# Pattern-based Subscriber
##

from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters

def on_message(msg, topic):
    print(f'Message at topic <{topic}>: {msg}')


if __name__ == '__main__':
    conn_params = ConnectionParameters()
    node = Node(node_name='example5_listener',
                connection_params=conn_params,
                debug=True)

    node.create_psubscriber(topic='topic.*', on_message=on_message)
    node.run_forever()

#!/usr/bin/env python

##
# Multi-Topic Puiblisher
##

from commlib.node import Node
from commlib.transports.mqtt import ConnectionParameters

def on_message(msg, topic):
    print(f'Message at topic <{topic}>: {msg}')


if __name__ == '__main__':
    conn_params = ConnectionParameters()
    node = Node(node_name='example5_publisher',
                connection_params=conn_params,
                debug=True)

    pub = node.create_mpublisher()
    node.run()

    topicA = 'topic.a'
    topicB = 'topic.b'

    while True:
        pub.publish({'a': 1}, topicA)
        pub.publish({'b': 1}, topicB)
        time.sleep(1)

Pythonic implementation of Subscribers and RPCs using decorators

from commlib.msg import MessageHeader, PubSubMessage, RPCMessage
from commlib.node import Node, TransportType
from commlib.transports.redis import ConnectionParameters


class SonarMessage(PubSubMessage):
    header: MessageHeader = MessageHeader()
    range: float = -1
    hfov: float = 30.6
    vfov: float = 14.2


class AddTwoIntMessage(RPCMessage):
    class Request(RPCMessage.Request):
        a: int = 0
        b: int = 0

    class Response(RPCMessage.Response):
        c: int = 0


conn_params = ConnectionParameters()

node = Node(node_name='obstacle_avoidance_node',
            connection_params=conn_params,
            debug=True)


@node.subscribe('sensors.sonar.front', SonarMessage)
def on_message(msg):
    print(f'Received front sonar data: {msg}')


@node.rpc('add_two_ints_node.add_two_ints', AddTwoIntMessage)
def add_two_int_handler(msg):
    print(f'Request Message: {msg.__dict__}')
    resp = AddTwoIntMessage.Response(c = msg.a + msg.b)
    return resp


node.run_forever(sleep_rate=0.01)

Preemptable Services with Feedback (Actions)

Actions are pre-emptable services with support for asynchronous feedback publishing. This communication pattern is used to implement services which can be stopped and can provide feedback data, such as the move command service of a robot.

Write an Action Service

import time

from commlib.action import GoalStatus
from commlib.msg import ActionMessage
from commlib.transports.redis import ConnectionParameters
)


class ExampleAction(ActionMessage):
    class Goal(ActionMessage.Goal):
        target_cm: int = 0

    class Result(ActionMessage.Result):
        dest_cm: int = 0

    class Feedback(ActionMessage.Feedback):
        current_cm: int = 0


def on_goal(goal_h):
    c = 0
    res = ExampleAction.Result()
    while c < goal_h.data.target_cm:
        if goal_h.cancel_event.is_set():
            break
        goal_h.send_feedback(ExampleAction.Feedback(current_cm=c))
        c += 1
        time.sleep(1)
    res.dest_cm = c
    return res


if __name__ == '__main__':
    action_name = 'testaction'
    conn_params = ConnectionParameters()
    node = Node(node_name='action_service_example_node',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)
    node.create_action(msg_type=ExampleAction,
                       action_name=action_name,
                       on_goal=on_goal)
    node.run_forever()

Write an Action Client

import time

from commlib.action import GoalStatus
from commlib.msg import ActionMessage
from commlib.transports.redis import ActionClient, ConnectionParameters


class ExampleAction(ActionMessage):
    class Goal(ActionMessage.Goal):
        target_cm: int = 0

    class Result(ActionMessage.Result):
        dest_cm: int = 0

    class Feedback(ActionMessage.Feedback):
        current_cm: int = 0


def on_feedback(feedback):
    print(f'ActionClient <on-feedback> callback: {feedback}')


def on_result(result):
    print(f'ActionClient <on-result> callback: {result}')


def on_goal_reached(result):
    print(f'ActionClient <on-goal-reached> callback: {result}')


if __name__ == '__main__':
    action_name = 'testaction'
    conn_params = ConnectionParameters()
    node = Node(node_name='action_client_example_node',
                connection_params=conn_params,
                # heartbeat_uri='nodes.add_two_ints.heartbeat',
                debug=True)
    action_client = node.create_action_client(
        msg_type=ExampleAction,
        action_name=action_name,
        on_goal_reached=on_goal_reached,
        on_feedback=on_feedback,
        on_result=on_result
    )
    node.run()
    goal_msg = ExampleAction.Goal(target_cm=5)
    action_client.send_goal(goal_msg)
    resp = action_client.get_result(wait=True)
    print(f'Action Result: {resp}')
    node.stop()

Broker-to-broker (B2B) bridges

In the context of IoT and CPS, it is a common requirement to bridge messages between message brokers, based on application-specific rules. An example is to bridge analytics (preprocessed) data from the Edge to the Cloud. And what happens if the brokers use different communication protocols?

Below are examples of an MQTT Redis-to-MQTT Bridge and a Redis-to-MQTT Topic Bridge.

#!/usr/bin/env python

import time

import commlib.transports.amqp as acomm
import commlib.transports.redis as rcomm
import commlib.transports.mqtt as mcomm

from commlib.bridges import (
    RPCBridge, RPCBridgeType, TopicBridge, TopicBridgeType
)


def redis_to_mqtt_rpc_bridge():
    """
    [RPC Client] ----> [Broker A] ------> [Broker B] ---> [RPC Service]
    """
    bA_params = rcomm.ConnectionParameters()
    bB_params = mcomm.ConnectionParameters()
    bA_uri = 'ops.start_navigation'
    bB_uri = 'thing.robotA.ops.start_navigation'
    br = RPCBridge(RPCBridgeType.REDIS_TO_MQTT,
                   from_uri=bA_uri, to_uri=bB_uri,
                   from_broker_params=bA_params,
                   to_broker_params=bB_params,
                   debug=False)
    br.run()


def redis_to_mqtt_topic_bridge():
    """
    [Producer Endpoint] ---> [Broker A] ---> [Broker B] ---> [Consumer Endpoint]
    """
    bA_params = rcomm.ConnectionParameters()
    bB_params = mcomm.ConnectionParameters()
    bA_uri = 'sonar.front'
    bB_uri = 'thing.robotA.sensors.sonar.font'
    br = TopicBridge(TopicBridgeType.REDIS_TO_MQTT,
                     from_uri=bA_uri, to_uri=bB_uri,
                     from_broker_params=bA_params,
                     to_broker_params=bB_params,
                     debug=False)
    br.run()


if __name__ == '__main__':
    redis_to_mqtt_rpc_bridge()
    redis_to_mqtt_topic_bridge()

A Pattern-based Topic Bridge (PTopicBridge) example is also shown below. In this example, we use static definition of messages (SonarMessage), also referred as typed communication.

#!/usr/bin/env python

import time

from commlib.msg import PubSubMessage
from commlib.bridges import PTopicBridge
import commlib.transports.amqp as acomm
import commlib.transports.redis as rcomm


class SonarMessage(PubSubMessage):
    distance: float = 0.001
    horizontal_fov: float = 30.0
    vertical_fov: float = 14.0


if __name__ == '__main__':
    """
    [Broker A] ------------> [Broker B] ---> [Consumer Endpoint]
    """
    bA_uri = 'sensors.*'
    bB_namespace = 'myrobot'

    bA_params = rcomm.ConnectionParameters()
    bB_params = mcomm.ConnectionParameters()

    br = PTopicBridge(TopicBridgeType.REDIS_TO_MQTT,
                      bA_uri,
                      bB_namespace,
                      bA_params,
                      bB_params,
                      msg_type=SonarMessage,
                      debug=False)
    br.run()

Action bridges

TCP Bridge

TCP bridge forwards tcp packages between two endpoints:

[Client] -------> [TCPBridge, port=xxxx] ---------> [TCP endpoint, port=xxxx]

A one-to-one connection is performed between the bridge and the endpoint.

REST Proxy

Implements a REST proxy, that enables invocation of REST services via broker communication. The proxy uses an RPCService to run the broker endpoint and an http client for calling REST services. An RPC call is transformed into proper, REST-compliant, http request, based on the input parameters.

class RESTProxyMessage(RPCMessage):
    class Request(RPCMessage.Request):
        base_url: str
        path: str = '/'
        verb: str = 'GET'
        query_params: Dict = {}
        path_params: Dict = {}
        body_params: Dict = {}
        headers: Dict = {}

    class Response(RPCMessage.Response):
        data: Union[str, Dict, int]
        headers: Dict[str, Any]
        status_code: int = 200

Responses from the REST services are returned to clients in the form of a RPCMessage.Response message.

Transports

AMQP / RabbitMQ

RPC (request/reply) and PubSub Endpoints are supported by the protocol itself (AMQP), using dedicated exchanges.

For RPC enpoints a Direct Exchange is used to route requests and responses, optionally using the Direct Reply-to. If the Direct Reply-to feature is used, then RPC endpoints must publish to the default exchange "".

To use Direct Reply-to, an RPC client should:

• Consume from the pseudo-queue amq.rabbitmq.reply-to in no-ack mode.
• Set the reply-to property in their request message to amq.rabbitmq.reply-to.

Meta-information such as the serialization method used, is passed through the message properties metadata, as specified my AMQP.

Redis

Req/Resp communication (RPC) is not supported out-of-the-box. To support RPC communication over Redis, a custom layer implements the pattern for both endpoints using Redis Lists to represent queues. RPC server listens for requests from a list (LPOP / BLPOP), while an RPC client sends request messages to that list (RPUSH). In order for the client to be able to receive responses, he must listen to a temporary queue. To achieve this, the request message must include a reply_to property that is used by the RPCServer implementation to send the response message. Furthermore, serialization and encoding properties are defined. Finally, the header includes a timestamp, that indicates the time that the message was sent to to wire.

Below is the data model of the request message.

{
  'data': {},
  'header': {
    'timestamp': <int>,
    'reply_to': <str>,
    'content_type': 'application/json',
    'content_encoding': 'utf8',
    'agent': 'commlib'
  }
}

Note: The RPC Client implementation is responsible to remove any created temporary queues!

MQTT

Req/Resp communication (RPC) is not supported out-of-the-box. To support RPC communication over MQTT, a custom layer implements the pattern for both endpoints using MQTT topics. RPC server listens for requests at a specific topic, while an RPC client listens to a temporary topic for response messages. For the server to know where to send the response, the request message must include a reply_to property that is used by the RPCServer implementation to send the response message. Furthermore, serialization and encoding properties are defined. Finally, the header includes a timestamp, that indicates the time that the message was sent to to wire.

Below is the data model of the Request message.

{
  'data': {},
  'header': {
    'timestamp': -1,
    'reply_to': "UNIQUE_NAME",
    'content_type': 'application/json',
    'content_encoding': 'utf8',
    'agent': 'commlib',
  }
}

Examples

Examples can be found at the examples/ directory of this repository.

Tests

Run tests by executing tox command under this repo directory:

make tests

Development

Starting from 2024 (>0.11.3) only PRs from the devel branch are merged into the master branch.

Other branches are merged into devel first.

Types of branches:

• Bug fix: fix/<bug_short_name>
• New feature: feat/<feature_short_name>
• Documentation: doc/<doc_short_name>

New versions will be released from the master branch after a PR request from devel.