Avro + Grpc in python

Developing a gRPC client in Python that leverages Avro serialization involves several steps. gRPC is a high-performance RPC framework, while Apache Avro is a serialization framework used for efficient data exchange. In this example, we will show how to:

Define gRPC services using Protocol Buffers (.proto).
Serialize and deserialize data using Avro.
Implement the gRPC server and client in Python.

We’ll walk through the Avro-based gRPC client-server application in Python, using gRPC to transmit messages and Avro to serialize the messages.

Overview of the Application

Avro is used to serialize complex data structures (e.g., dictionaries) into bytes.
gRPC transmits the serialized Avro bytes over the network.
Python will be used to implement the gRPC client and server.

Steps to Develop the gRPC + Avro Application

Step 1: Install Required Dependencies

Ensure you have the following dependencies installed:

pip install grpcio grpcio-tools avro-python3

grpcio and grpcio-tools: gRPC libraries for Python.
avro-python3: Avro library to serialize and deserialize data.

Step 2: Define the gRPC Service in `example.proto`

Create a .proto file, which defines the gRPC service. In this example, we define a UserService that allows the client to send user data serialized with Avro.

example.proto:

syntax = "proto3";

service UserService {
  // RPC to send user data
  rpc SendUserData (UserRequest) returns (UserResponse) {}
}

// Request message containing Avro-encoded user data as bytes
message UserRequest {
  bytes avro_data = 1;
}

// Response message confirming successful receipt
message UserResponse {
  string message = 1;
}

Step 3: Compile the `.proto` File

Run the following command to generate the Python code from your .proto file:

python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. example.proto

This will generate:

example_pb2.py: Contains the generated classes for messages.
example_pb2_grpc.py: Contains the generated gRPC service definitions.

Step 4: Create the Avro Schema

Create an Avro schema to define the structure of the user data.

user.avsc:

{
  "type": "record",
  "name": "User",
  "fields": [
    { "name": "name", "type": "string" },
    { "name": "age", "type": "int" },
    { "name": "email", "type": "string" }
  ]
}

Step 5: Implement the gRPC Server

The server will receive Avro-encoded user data from the client, deserialize it, and send a response back.

server.py:

import grpc
from concurrent import futures
import example_pb2
import example_pb2_grpc
import avro.schema
import avro.io
import io

# Load the Avro schema
schema = avro.schema.parse(open("user.avsc", "r").read())

class UserService(example_pb2_grpc.UserServiceServicer):
    def SendUserData(self, request, context):
        # Deserialize the Avro data
        bytes_reader = io.BytesIO(request.avro_data)
        decoder = avro.io.BinaryDecoder(bytes_reader)
        reader = avro.io.DatumReader(schema)
        user_data = reader.read(decoder)

        print(f"Received user data: {user_data}")

        # Send response back to client
        return example_pb2.UserResponse(message="User data received successfully")

def serve():
    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
    example_pb2_grpc.add_UserServiceServicer_to_server(UserService(), server)
    server.add_insecure_port("[::]:50051")
    server.start()
    print("Server started on port 50051...")
    server.wait_for_termination()

if __name__ == "__main__":
    serve()

Step 6: Implement the gRPC Client

The client will serialize user data using Avro and send it to the server via gRPC.

client.py:

import grpc
import example_pb2
import example_pb2_grpc
import avro.schema
import avro.io
import io

# Load the Avro schema
schema = avro.schema.parse(open("user.avsc", "r").read())

def serialize_user_data(user_data):
    bytes_writer = io.BytesIO()
    encoder = avro.io.BinaryEncoder(bytes_writer)
    writer = avro.io.DatumWriter(schema)
    writer.write(user_data, encoder)
    return bytes_writer.getvalue()

def run():
    # Create a gRPC channel and stub
    channel = grpc.insecure_channel("localhost:50051")
    stub = example_pb2_grpc.UserServiceStub(channel)

    # Create user data
    user_data = {"name": "Alice", "age": 30, "email": "alice@example.com"}

    # Serialize the user data using Avro
    avro_data = serialize_user_data(user_data)

    # Send the serialized data to the server
    response = stub.SendUserData(example_pb2.UserRequest(avro_data=avro_data))
    print(f"Server response: {response.message}")

if __name__ == "__main__":
    run()

Step 7: Run the Server and Client

Start the gRPC server:

python server.py

You should see:

Server started on port 50051...

Run the gRPC client:

python client.py

The client should output:

Server response: User data received successfully

And the server should display:

Received user data: {'name': 'Alice', 'age': 30, 'email': 'alice@example.com'}

Explanation

Server: The server receives the serialized Avro-encoded bytes from the client, deserializes them into a Python dictionary, and prints the user data.
Client: The client serializes the user data using Avro and sends it over a gRPC channel to the server.

Summary

In this tutorial, we implemented a gRPC client-server application in Python using Avro for data serialization. The key steps included:

Defining a gRPC service using Protocol Buffers.
Creating an Avro schema to define the data structure.
Implementing the gRPC server to receive and deserialize Avro data.
Implementing the gRPC client to serialize Avro data and send it to the server.

This approach allows you to take advantage of gRPC’s performance and Avro’s efficient serialization, making it suitable for high-performance, data-driven applications.

IT