A Simple Guide: Services and Microservices

In today’s software world, creating large, complex applications can quickly become overwhelming. That’s why many developers break their applications into services or microservices. Each service focuses on one specific part of the application, making it easier to build, manage, and scale. But what exactly are services and microservices? Why should you break your app into them? And how can you do it in a simple, effective way?

This guide will straightforwardly explain these concepts so that even beginners can easily understand them.

What is a Service?

Imagine your app as a big puzzle. Each service is like a puzzle piece that does one thing really well. For example, one service might manage user accounts, while another handles payments. These pieces work together to make the whole puzzle, or app, function smoothly.

A service is a self-contained part of your application with the following characteristics:

1. Own Responsibilities: Each service has its own set of tasks or responsibilities. For example, a User Service might be responsible for registering, updating, and deleting user accounts.
2. Independent Code: Services have their own codebase. This means you can make changes to one service without affecting the rest of the app.
3. Own Data: Each service manages its own data. For example, the User Service keeps track of user details, while the Order Service manages order data.
4. API Communication: Services communicate through APIs (Application Programming Interfaces). APIs let services talk to each other without sharing all the details. For example, the Payment Service might ask the User Service to confirm a user’s identity before processing a payment.
5. Scalability: Because services are independent, you can scale them separately. If your Order Service gets a lot of traffic, you can give it more resources without affecting other services.

Why Split an Application Into Services?

When you first build an app, it might seem easier to keep everything together. However, as your app grows, managing all its features in one big block becomes difficult. Breaking the app into services has several benefits:

1. Simplicity and Organization

When an app is broken into smaller services, each one has a clear purpose. This makes it easier to work on and understand each service individually. For example, when a developer needs to update the user registration process, they can focus on the User Service without worrying about unrelated parts of the app.

2. Faster Development

Multiple developers or teams can work on different services at the same time. While one team works on improving the Payment Service, another can enhance the Product Service. This speeds up development and allows teams to work independently.

3. Easier Maintenance

When an app is divided into services, it’s easier to fix bugs or add new features. If there’s a problem with the Order Service, you only need to work on that part without disturbing the rest of the app.

4. Scalability

As your app grows, certain services might become more popular than others. For example, if your Payment Service starts getting a lot of requests, you can scale it by adding more resources. This way, you only spend extra money where it’s needed, without upgrading the entire system.

5. Fault Isolation

If one service breaks, it won’t necessarily bring down the entire app. For instance, if the Email Service fails, other parts like viewing products or placing orders can still work. This makes your app more reliable and resilient.

How to Identify Services in Your Application

So how do you know which parts of your app should become services? Here’s a simple way to break it down:

1. By Features or Functions

Start by looking at the main features of your app. Each major feature could become its own service. For example:

• User Service: Manages user registration, login, and profile updates.
• Order Service: Handles customer orders, order history, and tracking.
• Payment Service: Manages payments, refunds, and billing.

Each service should focus on a specific task or responsibility within your app.

2. Data Ownership

Every service should manage its own data. For example:

• The User Service should only handle user data, like usernames, passwords, and profiles.
• The Order Service should only deal with order data, like products, quantities, and shipping addresses.
• The Payment Service should focus on payment data, like transaction details and billing info.

Keeping data ownership separate ensures that each service is responsible for its part of the system.

3. Avoid Overlapping Responsibilities

Make sure each service has a clear and unique responsibility. You don’t want multiple services doing the same thing. For example, if the User Service is handling user logins, don’t create another service to manage logins as well. This will only add confusion and complexity.

What Are Microservices?

Microservices take the concept of services one step further. While services handle major features, microservices focus on even smaller, more specific tasks. For example, instead of one big User Service that handles everything about users, you could have:

• A Login Microservice to handle user logins.
• A Profile Microservice to manage user profiles.
• An Email Microservice to handle email notifications.

Each microservice does one small thing, making them easy to manage and update independently.

Microservices Benefits:

1. Small and Focused: Microservices focus on one thing, making them easier to manage and develop.
2. Independent Scaling: Like services, microservices can be scaled independently. If your Login Microservice gets heavy traffic, you can add more resources to that part.
3. Flexible Development: You can use different technologies for different microservices. For example, one microservice might be written in Python, while another uses Node.js, depending on what works best for each task.

However, too many microservices can become difficult to manage, so it’s important to strike the right balance.

Guidelines for Splitting Your Application

Let’s go over some simple rules to follow when splitting your app into services or microservices:

1. Break by Features

Each important feature in your app can become a service. For instance, if you’re building an e-commerce site, you might have:

• Product Service for managing product listings.
• Cart Service for adding and removing items from the shopping cart.
• Order Service for processing customer orders.

2. Ensure Data Ownership

Every service or microservice should manage its own data. This way, the Product Service handles product information, and the Order Service manages order data without overlap.

3. Shared Features? Use a Centralized Service

If many parts of your app use the same data, you can create a shared service. For example, instead of having multiple services send emails, you could create a centralized Email Service. Other services can then ask the Email Service to send messages on their behalf.

4. Start Simple

It’s tempting to split everything into tiny microservices right away, but this can lead to unnecessary complexity. Start by breaking your app into a few key services. As your app grows, you can create smaller microservices if needed.

Common Pitfalls to Avoid

While splitting your app into services and microservices has many advantages, it’s important not to go overboard. Here are some common mistakes to avoid:

1. Too Many Microservices: If you create too many small microservices, your app can become harder to manage. Each microservice will need its resources, monitoring, and scaling, which adds overhead.
2. Complex Dependencies: Microservices and services should be loosely connected. If one service relies too heavily on another, it can create complex dependencies. This makes your app harder to understand and maintain.
3. Lack of Clear Ownership: Make sure each service has a clear owner. A service might become neglected or outdated if no one is responsible for it.

What Are Stateless Services?

Stateless services are services that don’t maintain any internal state or store data within themselves. They rely on external data sources or parameters provided by incoming requests. Every time a stateless service receives a request, it treats it as a completely new event, independent of past interactions.

For example, if a Stateless Payment Service is called to process a transaction, all necessary information about the transaction (like user data or payment method) must be included in the request. The service processes the payment and doesn’t retain any memory of the transaction afterwards.

Key Benefits of Stateless Services:

1. Easier Scaling: Since stateless services don’t rely on stored data, they can be scaled horizontally with minimal effort. You can simply add more servers to handle more requests.
2. Improved Caching: Without the complexity of managing state, stateless services can use frontend caching techniques to reduce the load on backend systems.
3. Fault Tolerance: If one stateless service fails, another can immediately take its place, as there’s no state to restore.

Example Use Case:

• Authentication Service: Imagine a user login process. Each time a user sends login credentials, the service checks them against a database and generates a session token. It doesn’t store login attempts or any user-specific state after the process is done.

What Are Stateful Services?

Unlike stateless services, stateful services maintain data or a “state” that persists between requests. They rely on stored information to carry out tasks, making them more complex but necessary for certain operations that require ongoing interactions.

For instance, a User Session Service might track a user’s session as they navigate a website. Each interaction the user makes builds upon their previous actions.

Why Use Stateful Services?

1. Persistence: Some actions, like managing user sessions, require data to persist across multiple requests.
2. Real-Time Interactions: In applications like multiplayer games or financial systems, keeping track of ongoing processes (like scores or stock trades) is critical.
3. Complexity Management: When you need to remember user preferences, shopping cart contents, or anything that requires continuity, a stateful service is required.

Example Use Case:

• Shopping Cart Service: When a user adds items to their cart, the service keeps track of those items until the user checks out or abandons the cart. The state of the cart (which items are present) is maintained over time.

Data Management in Stateless and Stateful Services

Stateless Services: Data Handling

In stateless services, data is passed with each request, meaning no data is stored in the service itself. This method allows for easy horizontal scaling, as the service doesn’t need to worry about syncing data across instances.

Benefits of Stateless Data Handling:

• Simplified Infrastructure: Since no data is stored, you don’t need complex databases or memory caches within the service.
• Scalability: New instances of the service can be spun up quickly without needing to sync data.

Example:

If your API receives user details in every request, you don’t need to store those details inside the service. Each request carries the needed data to complete its task independently.

Stateful Services: Localizing Data

For stateful services, managing data becomes more complex. To make things manageable and scalable, you should localize data as much as possible. This means keeping data close to the service that needs it, without centralizing it across different services.

Benefits of Localizing Data:

1. Reduced Dataset Size: By distributing data across different services, you reduce the size of each individual dataset, improving performance.
2. Optimized Access: Keeping data close to where it’s needed allows for faster access and less load on the database.
3. Flexibility: You can optimize the data store for each service, choosing between relational databases, key-value stores, or other types based on the needs of each service.

Example:

Let’s say you have a User Profile Service and a Payment Service. Instead of storing all user data in both services, the User Profile Service stores user details (name, email), while the Payment Service stores only payment-related information (billing address, credit card). This way, each service manages only the data it needs.

Data Partitioning for Scalability

As your application grows, a single database might no longer be able to handle all requests efficiently. This is where data partitioning comes into play. By partitioning data, you can spread the load across multiple databases, making your application faster and more reliable.

Functional Partitioning:

Partitioning data by function means breaking your data into smaller sets based on specific features or services. For example, user data might be stored in one database, while transaction data is stored in another.

Key-Based Partitioning:

This is a more advanced type of partitioning where data is divided based on a unique key. For instance, you might store all users whose names start with A–D in one database, E–K in another, and so on.

Advantages of Data Partitioning:

• Increased Performance: Smaller datasets reduce the time needed to retrieve information.
• Scalability: By distributing data across multiple databases, you can scale your application without overloading a single data store.

Challenges with Data Partitioning:

• Complexity: Partitioning data adds complexity because the application must know where to retrieve specific data.
• Cross-Partition Queries: Querying data across partitions can be difficult, especially for large datasets.
• Repartitioning: If partitions become imbalanced, repartitioning is necessary, which can be challenging and time-consuming.

Best Practices for Managing Data in Services

1. Localize Data: Keep data close to the service that needs it to avoid unnecessary complexity and reduce latency.
2. Use Stateless Services Where Possible: When scaling is your main goal, stateless services provide flexibility and performance.
3. Be Careful with Partition Keys: Choose partition keys that allow for even distribution of data to avoid “hot” partitions that get overloaded.
4. Plan for Growth: Consider the scalability of your architecture from the start. Anticipating growth will prevent architectural bottlenecks later.

What Are Cascading Failures?

Imagine a service, “Our Service,” which relies on other services — let’s call them Service A, Service B, and Service C. Additionally, Consumer 1 and Consumer 2 depend on “Our Service” (Figure 1). Now, what happens if one of the services our system depends on, such as Service A, fails?

Without proper precautions, the failure of Service A can cause Our Service to fail as well. This failure can then propagate further, causing Consumer 1 and Consumer 2 to fail. This is a classic example of cascading failures (Figure 2), where one small error snowballs into a larger system failure.

Responding to Service Failures

When a service dependency fails, your response must be:

• Predictable
• Understandable
• Reasonable for the situation

1. Predictable Response

Predictability is crucial in preventing cascading failures. Even if a downstream service fails, your service should still provide a predictable response. For instance, if an error occurs, it’s acceptable to return an error message — this is a predictable response. However, returning unpredictable data (like garbage values) can worsen the situation and lead to confusion in upstream services.

By offering a consistent and expected output, even in failure scenarios, you minimize the likelihood of propagating unpredictable behavior across the system.

2. Understandable Response

An understandable response refers to adhering to agreed-upon formats and contracts between services. If a service fails, it should still return a response that fits within the API contract. Even when services are misbehaving, maintaining this contract ensures that upstream services can continue functioning correctly without disruption.

3. Reasonable Response

A reasonable response should reflect what’s happening with the service. For example, if a dependent service cannot return valid data, it should return something like “Try again later” instead of incorrect or misleading information. An unreasonable response could cause severe issues, such as data corruption or unintended actions within the system.

Identifying and Managing Failures

Understanding how to detect a failure is critical in responding effectively. Here are several common failure modes:

1. Garbled Response: The response is unreadable or in an unexpected format.
2. Fatal Error: The service responds with an understandable message but indicates an internal failure.
3. Unexpected Results: The service response is clear but contains incorrect or unexpected data.
4. Out of Bounds: The data returned is within an acceptable format but doesn’t fall within expected limits.
5. No Response: The service fails to respond altogether.
6. Slow Response: The response arrives, but it takes too long, potentially breaching service-level agreements (SLAs).

Among these, responses that never arrive or are significantly delayed pose unique challenges. In these cases, simple timeouts might not be effective, as response times can vary significantly.

Implementing the Circuit Breaker Pattern

One effective strategy for managing dependency failures is the circuit breaker pattern. This pattern monitors the health of your service dependencies and temporarily halts requests if too many failures are detected within a given period.

Here’s how it works:

• Normal Operation: Requests flow between your service and its dependencies as usual.
• Failures Detected: If a threshold of errors (or slow responses) is reached, the circuit breaker trips, causing the service to stop sending further requests to the failed dependency.
• Periodic Checks: The system periodically checks the dependency to see if it has recovered.
• Circuit Reset: Once the dependency is stable, the circuit breaker resets, allowing requests to resume.

This approach allows you to prevent further cascading failures by isolating the failing service.

Handling Slow Responses

Handling slow responses is often more complicated. Timeouts alone may not be sufficient, as responses that are “sometimes” slow can generate unpredictable results. A more sophisticated approach involves tracking the response times of calls to your dependencies and using this data to determine when a slowdown is significant enough to trigger the circuit breaker.

For example:

• 500 requests in 1 minute taking longer than 150 ms could trigger a warning.
• 50 requests taking longer than 500 ms in 1 minute might trip the circuit breaker.
• 5 requests in 1 minute that exceed 1,000 ms would be an immediate red flag.

By using this layered technique, you can better handle dependencies that may fluctuate between normal and slow response times, ensuring your service continues to provide a predictable and reasonable response to users.

When handling errors in services, there are several key strategies to consider. These approaches help ensure that your system remains functional and minimizes negative impacts on users and other components.

1. Graceful Degradation

When a service dependency fails, try to continue the core functionality, even if limited. This is especially useful when the failure affects non-critical parts of the system, such as images or additional details that do not stop the main operation. For example, an e-commerce site can still show product information even if the image service fails.

2. Reduced Functionality

If a service failure affects some functionality but not the core service, you can reduce the offered features while still maintaining the overall experience. For instance, you could show product listings without images or offer limited details until a service is back online.

3. Graceful Backoff

In situations where the service cannot complete a request meaningfully, use alternative approaches to provide some value. For example, instead of showing an error, you could display links to popular products when product details are unavailable.

4. Fail Early

If there’s no way to handle an error, it’s important to fail as soon as you know the request cannot be completed. This prevents unnecessary work and resource consumption. For instance, if a request involves invalid data (like a “divide by zero” operation), fail the request immediately rather than attempting an impossible operation.

5. Resource Conservation

When you know a request will fail, avoid making extra calls or performing further operations. This saves resources and allows you to respond more quickly, preventing additional complexity.

6. Customer-Caused Problems

Handle errors caused by invalid user input as soon as possible. If a request exceeds the limits of what your service can handle (e.g., a large number of records), reject the request early with an appropriate error message rather than attempting to process it, only to fail later.

7. Providing Service Limits

Clearly state and enforce service limits to prevent misuse. For example, if your service can’t handle more than 5,000 records at a time, communicate this in the API contract and enforce the limit within the service logic.

By incorporating these patterns, services can remain resilient and responsive, even when dependencies fail or invalid requests are made.

In Conclusion :

adopting a microservices architecture offers numerous advantages for modern software development, including improved scalability, flexibility, and resilience. By breaking down applications into smaller, independently deployable services, teams can respond more rapidly to changing business needs and enhance overall productivity. However, it’s essential to approach this architecture with careful planning, considering factors such as service communication, data management, and deployment strategies. As we continue to navigate the complexities of software development, embracing microservices can empower organizations to build robust, maintainable, and innovative solutions that meet the demands of today’s dynamic market.