gRPC and Protobuf with Python

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Context and Overview

I am trying to give you a quick learning for GRPC, Protobuf including a Python based application to test.

gRPC (Remote Procedure Call):

- Definition: gRPC is a high-performance, open-source RPC framework developed by Google. It allows you to define remote service methods using Protocol Buffers and then generate client and server code in multiple languages.

- Purpose: gRPC enables efficient communication between distributed systems, allowing services written in different languages to communicate seamlessly.

- Usage: It is commonly used in microservices architectures, where services need to communicate with each other over a network.

Protocol Buffers (protobuf):

- Definition: Protocol Buffers is a language-neutral, platform-neutral, extensible mechanism for serializing structured data. It was developed by Google and used for efficient data serialization.

- Purpose: Protocol Buffers are used to define the structure of data that is transmitted between different systems or components. They offer a compact binary format for data exchange and are language-agnostic.

- Usage: Protocol Buffers are commonly used in scenarios where efficient data serialization is required, such as communication between microservices, storage of data, and configuration files.

Benefits

gRPC:

- Efficiency: gRPC uses HTTP/2 as the underlying protocol, which supports multiplexed streams, header compression, and other features that improve efficiency over traditional HTTP/1.x.

- Language Agnostic: gRPC supports multiple programming languages, making building polyglot systems where services are written in different languages easy.

- Automatic Code Generation: gRPC provides tools to automatically generate client and server code based on the service definition, reducing boilerplate code and making development faster.

- Streaming Support: gRPC supports both unary and streaming RPCs, allowing bidirectional communication between client and server.

Protocol Buffers:

- Efficiency: Protocol Buffers use a binary encoding format, which is more compact and efficient than JSON or XML. This results in smaller message sizes and faster serialization/deserialization.

- Schema Evolution: Protocol Buffers support backward and forward compatibility, allowing you to evolve your data schema over time without breaking existing clients or servers.

- Language Agnostic: Similar to gRPC, Protocol Buffers are language agnostic, enabling interoperability between systems written in different languages.

- Version Control: Protocol Buffers allow you to version your data schema, making it easier to manage changes over time and ensuring compatibility between different software versions.

Links for Further Reading

- gRPC Documentation: https://grpc.io/docs/

- Protocol Buffers Documentation: https://developers.google.com/protocol-buffers

- gRPC GitHub Repository: https://github.com/grpc/grpc

- Protocol Buffers GitHub Repository: https://github.com/protocolbuffers/protobuf

- gRPC vs REST: https://grpc.io/blog/grpc-vs-rest/

- gRPC Python Quick Start: https://grpc.io/docs/languages/python/quickstart/

- Protocol Buffers Language Guide: https://developers.google.com/protocol-buffers/docs/proto3

- GitHub demo application: https://github.com/dhirajpatra/grpc_protobuf_python

SQLAlchemy and Alembic

 

SQLAlchemy and Alembic: Explained with Example

SQLAlchemy:

• A powerful Python library for interacting with relational databases.
• Provides an object-relational mapper (ORM) that lets you define your data model as Python classes and map them to tables in a database.
• Simplifies writing SQL queries and manipulating data through its object-oriented interface.

Alembic:

• A migration tool built on top of SQLAlchemy.
• Allows you to track changes to your database schema over time and manage upgrades and downgrades.
• Generates migration scripts as your data model evolves, providing version control for your database schema.

Example:

Let's consider a model that defines a User table with two attributes: id (primary key) and username.

Python code (SQLAlchemy):

Python

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class
 
User(Base):
    __tablename__ = "users"


    
id = Column(Integer, primary_key=True)
    username = Column(String(50), nullable=False)

engine = create_engine("sqlite:///database.db")
Base.metadata.create_all(engine)

# Creating new User objects and writing to the database
user1 = User(username="alice")
user2 = User(username="bob")

session = Session(engine)
session.add_all([user1, user2])
session.commit()

# Reading users from the database
users = session.query(User).all()
print(users)

Creating a Migration with Alembic:

1. Initialize Alembic:

alembic init

2. Generate a migration script for the initial schema:

alembic revision --autogenerate

This creates a migration script containing the necessary SQL statements to create the users table.

3. Upgrade the database schema:

alembic upgrade

This executes the migration script and creates the users table in the database.

Adding a new attribute to the model:

We can add a new attribute email to the User model:

Python

class User(Base):
    # ... existing code
    email = Column(String(100))

Base.metadata.alter(engine)

This will alter the existing users table in the database and add the email column.

4. Generate a new migration script:

alembic revision --autogenerate

5. Upgrade the database schema:

alembic upgrade

Benefits of using SQLAlchemy and Alembic:

• Code readability: Focuses on the data model structure rather than writing raw SQL queries.
• Maintainability: Easier to evolve the database schema and track changes.
• Version control: Migration scripts act as version control for the database schema.
• Portability: Code can be ported to different databases with minimal changes.

Remember: This is a simplified example. For real-world scenarios, you can define much more complex models with relationships, constraints, and other features.

You can find more example in internet also can check my github repo here https://github.com/dhirajpatra