Future-Proofing Your Software: A Beginner's Guide to Compatibility

#systemdesign #beginners #architecture #software

Have you ever redecorated a room? You might swap out the furniture or repaint the walls. Similarly, software is constantly evolving. Updates are essential to keep your programs running smoothly and securely. They handle bug fixes, patch security vulnerabilities, and introduce new features. However, just like a brand-new rug might not match your existing curtains, software updates can sometimes clash with older versions of the code or data formats. Hence, understanding backward and forward compatibility becomes essential.

What is Compatibility?

In large-scale applications, code changes often cannot happen at once. With server-side applications, you may want to perform a staged rollout (i.e., gradually deploying the new version to a few users before making it available to everyone), reducing the risk of downtime. With client-side applications, you're at the mercy of the user, who may not install the update for some time. As a result, old and new versions of the code may exist in the system at the same time. For the system to continue running smoothly, you need to maintain compatibility in both directions:

Backward compatibility
Forward compatibility

Forward Compatibility

When a program attempts to be as compatible as possible with future versions of itself, this is known as forward compatibility. This largely involves the old code ignoring additions by the new code; skipping formats it does not understand, instead of throwing an exception. In the context of dataflow, this means that older code can read data written by newer code.

Backward Compatibility

A program is considered backward compatible if its dependents continue to work the same way, without modification, on its older versions. In the context of dataflow, this means that newer code can read data written by older code.

For example, if you have a database and you add a new field to the schema and make it required, when the new code tries to read data written by the old code, the operation will fail because the old code has no knowledge of the required field and thus there is no value for it. This is a common mistake. A simple practice for maintaining backward compatibility in this scenario is to make all fields added after the initial deployment optional or give them a default value. For the same reason, you should not remove a required field.

Backward compatibility is usually easier to achieve because- as the author of the new code, you know how the old code works. You may keep the old code to handle its specific use-case/data format.

Why Should You Care About Compatibility?

Reduced Downtime: In case of issues with a new version, maintaining compatibility allows for a quick rollback to a stable previous version, minimizing downtime.
Improved User Experience: Backward compatibility enables older and newer versions to coexist. It reduces the pressure to force immediate updates on all users, avoiding disruptions.
Seamless Updates: New features and bug fixes can be introduced without worrying about breaking functionality for users on older versions (up to a certain point).
Evolvable Codebase: Compatibility practices such as using modules and interfaces, and loose coupling of components encourage cleaner and maintainable code that can adapt to future changes.

Challenges to Consider

Balancing New Features and Old Code: Maintaining compatibility can make it harder to introduce radical changes or completely revamp the codebase in future updates.
Increased Testing: Ensuring compatibility across multiple versions adds complexity to the testing process.
Technical Debt: Over time, prioritizing backward compatibility can lead to technical debt, where older code becomes cumbersome to maintain alongside newer versions.

Strategies for Maintaining Compatibility

Versioning

Setting up a versioning system to track the changes made with each update will help everyone understand how the software works over time.

The Semantic Versioning Specification (SemVer) is a popular versioning scheme that uses a Major.Minor.Patch (X.Y.Z) format, where you increment Z if only backward compatible bug fixes are introduced, increment Y if any new backward compatible functionality is added, and increment X for any breaking changes (i.e. backward incompatible functionality). Learn more about SemVer at www.semver.org.

Clear Deprecation Policy

Communicate a plan for phasing out older features or data formats well in advance, giving users time to adapt.

Modular Design

Break down code into smaller, independent modules so that changes in one section will have minimal impact on others. Modules are chunks of code that have specific functions. They can be reused in different parts of the program. The code snippets below are examples of a basic calculator program written in a monolithic approach vs modular approach.

function calculate(operation, num1, num2) {
  if (operation === 'add') {
    return num1 + num2;
  } else if (operation === 'subtract') {
    return num1 - num2;
  } else if (operation === 'multiply') {
    return num1 * num2;
  } else if (operation === 'divide') {
    return num1 / num2;
  } 
  return 'Invalid operation';
}

i. Monolithic design: As you can see, all the logic is in one function. This works but can become difficult to maintain if the application grows.

// operations.js
function add(x, y) { return x + y; }
function subtract(x, y) { return x - y; }
function multiply(x, y) { return x * y; }
function divide(x, y) { return x / y; }

module.exports = {add, subtract, multiply, divide}

// calculator.js
import { add, subtract, multiply, divide } from './operations.js';

function calculate(operation, num1, num2) {
  let result;
  switch (operation) {
    case 'add':
      result = add(num1, num2);
      break;
    case 'subtract':
      result = subtract(num1, num2);
      break;
    case 'multiply':
      result = multiply(num1, num2);
      break;
    case 'divide':
      result = divide(num1, num2);
      break;
    default:
      return 'Invalid operation';
  }
  return result;
}

ii. Modular Design: Here, the operations have been separated into a different module, making the code more organized and reusable.

Loose Coupling

To accommodate different versions and functionalities, code components should be loosely coupled. While modular design is about code structure, coupling is about the interaction between the components. The less they know about each other, the more loosely coupled they are. See an example of loosely vs tightly coupled TypeScript code in the snippet below.

class CartService {
    constructor(private cart: ICart) {}
    addItem(item: string) {
        this.cart.addItem(item);
    }
    removeItem(item: string) {
        this.cart.removeItem(item);
    }
}

interface ICart {
    addItem(item: string): void;
    removeItem(item: string): void;
}

class CartImpl implements ICart {
    addItem(item: string) {
        console.log(item, ' added')
    }
    removeItem(item: string) {
        console.log(item, ' removed')
    }
}

const cartService = new CartService(new CartImpl());
cartService.addItem('item1'); // result: 'item1 added'
cartService.removeItem('item1'); // result: 'item1 removed'

i. Loosely coupled: The CartService does not care how the item is added, only that it can be added. It relies on the interface ICart rather than specific implementations. This allows for flexibility as you can switch implementations (e.g., applying a discount) without modifying the CartService code.

class CartService {
  private cart: any[] = [];

  addItem(item: string) {
    this.cart.push(item);
    console.log(this.cart)
  }

  removeItem(item: string) {
    const index = this.cart.indexOf(item);
    if (index !== -1) {
      this.cart.splice(index, 1);
    }
    console.log(this.cart)
  }
}

const cartService = new CartService();
cartService.addItem('item1'); // result: ['item1']
cartService.removeItem('item1'); // result: []

ii. Tightly coupled: This approach can lead to maintainability issues and reduced flexibility. However, tight coupling is not always a bad thing, so one must always consider the trade-offs of both architectures. Learn more about coupling here- It’s All About (Loose) Coupling — PragmaticCoding.

Automated Testing

Automated testing frameworks ensure compatibility across different versions during development.

Conclusion

While compatibility should not be overlooked, striking the right balance is key. Maintaining support for older versions can consume valuable resources. For example, continuing to support Node.js v4 might be impractical, given the availability of several newer, more efficient versions. Always weigh the benefits of supporting older versions against the cost and potential risks.

By carefully considering these factors and implementing effective compatibility strategies, you can create software that adapts to change while minimizing user disruptions. For a deeper dive into software evolution, compatibility and other system design concepts, check out the resources below.