Distributed Systems: Designing Scalable Python Backends

Almost all systems today connected through the World Wide Web are distributed systems. Distributed systems are a group of multiple computers or servers that work together and functionalize optimally. This allows multiple users to utilize such software or services without facing slow loading times and poor performance. For example, imagine you build a website and host it on a single-user server, this would perform well until user traffic increases, demanding more resources and speed. Distributed systems aid performance and flexibility by splitting the application into individual services on separate servers that interact with each other. This would seem like a simple software or application to the user, but on the backend, it's multiple interconnected nodes talking to each other.

Python programming language is one of the slowest, but one of the most useful languages today, ever since the advent of artificial intelligence, machine learning, and Large Language Models, python has been the go-to language for these, but no one wants a chatbot or an ml service that takes a long amount of processing time to work, distributed systems is a key to optimizing such on an application. In this article, we’ll consider key features of distributed systems, why you should use distributed systems, and how we can scale a distributed system with Python.

Key Features Of Distributed Systems

The following are key features of distributed systems that make them work optimally:

• Nodes: Individual computers or processes that work together as part of the system; each node performs certain tasks and connects with others to ensure the system functions properly.
• Communication Protocols: Nodes can communicate and share information thanks to protocols like HTTP, gRPC, or TCP/IP, which guarantee dependable communication between components even when they are on different networks.
• Shared Resources: Distributed systems frequently rely on resources like databases, file systems, or message queues; proper management enables consistent and efficient access across all nodes.
• Fault Tolerance: Even if a node fails, distributed systems continue running, eliminating a single point of failure. Redundancy and replication techniques ensure reliability and high availability.
• Scalability: The ability to handle increased load by adding more nodes (horizontal scaling) or enhancing the capacity of existing nodes (vertical scaling). Scalability ensures the system remains responsive under high demand.

Why Scalability Matters?

Scalability as mentioned earlier is a system’s ability to handle increased load by adding resources. This ensures the system is always at optimal performance during traffic spikes. There are 2 major types of scaling:

1. Horizontal scaling: This involves using more machines and servers for the application to work smoothly.
2. Vertical scaling: This involves increasing the system's RAM, storage, and capacity.

How to Design Scalable Python Backends

Knowledge of the right tools is required to design scalable Python backends to allow the system to grow and remain efficient. Below are some key tools and strategies for building scalable Python backends.

• APIs: Use lightweight frameworks like Flask or Fast API to build scalable backend APIs. They are relatively easy to use when creating rest APIs. Fast API is best for performance and its support for asynchronous programming.
• Asynchronous processing: To support the main application, it is wise to offload some background tasks(like emails or data processing) using Cerely with Redis as the message broker.
• Load Balancing: To balance the load or traffic on the application, distribute incoming requests evenly across backend servers like Nginx or HAProxy in the distributed system.

Example: A task queue with Celery and Redis.

# tasks.py
from celery import Celery

app = Celery('tasks', broker='redis://localhost:6379/0')

@app.task
def process_order(order_id):
    print(f"Processing order {order_id}")

# Adding a task to the queue
process_order.delay(123)

Data Management in Distributed Systems

Maintaining the CAP theorem or properties when managing data in distributed systems.

• Consistency: All nodes in the distributed system see the same data. If data is updated in a node, all nodes should reflect the updated value immediately.
• Availability: The system responds even during a node failure. The system should always be operational.
• Partition Tolerance: It should work despite network failures between nodes.

Useful databases are:

• SQL Databases like PostgreSQL for transactional consistency.
• NoSQL Databases like MongoDB for scalable, flexible schemas.

An example is a case setting up a distributed MongoDB cluster to store and retrieve user data across multiple nodes, ensuring high availability and fault tolerance.

Tools for Deployment and Scaling

Deployment and scaling are when tools like docker and Kubernetes come in.

• Docker: Docker is used to containerize Python backend applications for consistent environments.
• Kubernetes: This helps automate the deployment, scaling, and management of the containerized application.

Example: Below is a simple/basic example of deploying a Python backend application using docker and Kubernetes.

Dockerfile:

 FROM python:3.10
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "app.py"]

Kubernetes Deployment:

 apiVersion: apps/v1
kind: Deployment
metadata:
  name: flask-backend
spec:
  replicas: 3
  selector:
    matchLabels:
      app: flask-backend
  template:
    metadata:
      labels:
        app: flask-backend
    spec:
      containers:
      - name: flask-backend
        image: flask-app:latest
        ports:
        - containerPort: 5000

Monitoring and Maintenance

In distributed systems, it is important to monitor and maintain nodes around the system as they interact and function as one. This will help to identify and fix possible faults.

Examples of tools that do this are Prometheus and Grafana.

• Prometheus: Prometheus helps to collect metrics on API performance, database latency, etc.
• Grafana: Grafana visualizes metrics with customizable dashboards.

Case Study: Scalable Ecommerce Backend

Before I conclude this article, let's look at how to apply distributed systems during the development of an e-commerce system.

1. FastAPI can be used for the backend system and handle API’s for order processing.
2. Celery with Redis is used to handle asynchronous background processing of tasks like payments or inventory updates.
3. The application is deployed on docker and Kubernetes to ensure the scaling of the system.
4. The application is monitored using tools like Prometheus.

Conclusion

By using Python tools like Flask, FastAPI, Celery, Docker, and Kubernetes, developers can build robust and scalable systems. In this article, we’ve covered the general terms related to distributed systems and how they can help, as well as some basic examples with Python. You can do advanced research on these tools and how they can work together and help. Start experimenting with these tools and create backends that can handle the challenges of real-world traffic and growth. Happy hacking.