How to make the system work for you

Once upon a time, I had to shovel through hundreds of articles, flip through mountains of books, and drink more than one cup of coffee to understand microservice architecture. I was looking for answers to questions that appeared at every step: how to make microservices work together, how to keep the system afloat, and, most importantly, how not to go crazy from all these nuances. After this marathon, I want to share my findings and advice to save you a couple of sleepless nights.

This article is a kind of a small guide or roadmap for those who want to dive into microservices, but without unnecessary stress. I will tell you what really works in practice, what techniques are worth adopting, and which ones are better to stay away from. All this will be based on real examples and my experience, without unnecessary theory and abstruse terms.

If you are just starting to understand microservices or are already head over heels in them, but still feel that some things are not completely clear, this article is just for you. It will help you understand how to build a system that will work stably, scalably and, most importantly, will not make you grab your head every time something goes wrong. I hope my advice and observations will be useful, and you will avoid the same rakes that I myself have stepped on more than once. After all, as they say, it is always more pleasant to learn from other people's mistakes.

1. Selecting and customizing a framework

When it comes to choosing a microservices framework for Go, popular options like Gin and Echo come to mind first. Both are popular for their simplicity, functionality, and great performance. While both are great, if I were asked, I would choose Echo.

Echo is attractive for its minimalism and simplicity. Unlike more heavyweight solutions, it provides all the necessary tools for creating a RESTful API, without unnecessary bells and whistles. Echo stands out for its performance – it copes well even under high load. Built-in support for middleware, routing, validation – all this makes Echo a good choice for a quick start.

And here's where it gets interesting: despite my love for Echo, I'm not a fan of frameworks at all. httprouter is much more appealing to me – it perfectly captures Go's strengths: simplicity and power.

With httprouter you have full control over request processing. No extra abstractions, no dependencies. You decide how the application will look, and this gives huge opportunities for optimization and customization.

When I work with httprouter, I know that every byte of my code does exactly what I intended. I don't depend on the internal implementations of the framework, which may change with updates or be insufficiently flexible for my needs. Every middleware, every request handling function is my work, and it is exactly as I intended it to be.

Yes, using httprouter requires more attention to detail and effort at the start, but the result is worth it. You create a system that perfectly suits your requirements and can be easily scaled and optimized.

So, if you need to choose a framework, Echo is a great choice. But in real projects, I still choose my path – httprouter. This approach allows me to create systems that exactly match my expectations, without unnecessary complexity and unnecessary dependencies.

2. Inter-service communication using gRPC and Protocol Buffers

Choosing a protocol for interaction between microservices is not an easy task. The most common and simple option is REST API, which uses HTTP and JSON to transfer data. Everyone is familiar with it, it is simple and quite powerful. But when you need something faster and more efficient, gRPC comes into play.

gRPC is not just an alternative to REST, it’s a whole other level. Instead of transmitting data in text format, like JSON, gRPC uses a binary format. This means that data is transmitted faster and takes up less space. In a microservice architecture, where services are constantly communicating with each other, this can significantly improve performance.

gRPC uses Protocol Buffers, or Protobuf, to describe data structures. This file is then turned into code that you use in your project. Imagine that you described the request and response in one file, and then got ready-made code that you simply insert into the project. This simplifies life and reduces the risk of errors that could occur if everything was written manually.

Why would I choose gRPC? It’s simple. It gives you speed and efficiency that are hard to get with REST. Plus, Protobuf ensures that the data is transferred strictly in the required format. This means that your microservices will clearly understand each other, without any unpleasant surprises.

gRPC also supports bidirectional streaming connections, allowing your services to send and receive data in real time — ideal for complex systems that need to process large amounts of data on the fly.

Yes, gRPC is a bit more complex to set up than REST, and it can be hard to move away from the familiar HTTP/JSON. But if you care about speed and reliability, gRPC is a great choice. You get faster and easier interactions between microservices, which ultimately makes the whole system more efficient.

3. Ensuring data consistency and distributed transactions

In the world of microservices, one of the main challenges is how to ensure that data remains consistent across services. In monolithic applications, everything is simple: you have one database, and you can be sure that all operations are consistent. But with microservices, things are more complicated: each service can have its own database, and this is where the real puzzles begin.

Let's say you have two microservices: one processes orders, the other monitors inventory. When a customer places an order, you need to update both the order data and the inventory at the same time. Ideally, both changes should happen together. But what if one of the actions fails? For example, the order is saved, but the inventory data is not updated? This will create a data mismatch, which means problems.

The solution to this problem is distributed transactions. But they are not as simple as transactions in a single database. There are two main approaches: two-phase commit (2PC) and sagas.

Two-phase commit (2PC) is a classic way to maintain consistency. It works like this: first, all services are instructed to prepare for an operation (the prepare phase), and then they are instructed to complete it (the commit phase). If all services are ready, the operation is completed; if not, everything is rolled back. This is reliable, but not always efficient, especially when services wait for a long time for each other.

Sagas are a more flexible approach. Instead of trying to perform all operations at once, sagas break them down into multiple steps. If something goes wrong at any point, previous steps can be undone with compensating actions. For example, if an order was created but the warehouse failed to update, the system can simply cancel the order. Sagas are more flexible and are great for microservices, where services have their own lives.

The important thing is the choice of approach. If you need an absolute guarantee of consistency and are willing to sacrifice performance, 2PC may be a good option. But if speed and flexibility are more important, sagas are the way to go.

In practice, most microservice systems choose sagas. This is because in complex distributed systems, it is difficult to ensure perfect synchronization, and it is better to have a rollback mechanism than to try to force all services to work in sync.

So when you’re building microservices, think about how you’ll maintain data consistency. It’s important not just to choose the right approach, but to remember that having a plan in place for when things go wrong is what separates reliable systems from the rest.

4. Advanced logging and monitoring

In a microservice architecture, you can't get far without good logging and monitoring. Why? Because in a microservice architecture, dozens, and sometimes hundreds, of services communicate with each other. If something goes wrong, you need to quickly find where exactly the problem is. And that's where logging and monitoring come in.

Let's start with logging. When you have hundreds of service instances distributed across different servers or containers, simply logging to the console or a file won't work anymore. The most effective way is to centralize the logs, collecting them in one place so they can be easily analyzed.

The most common stacks for this are ELK (Elasticsearch, Logstash, Kibana) or EFK (Elasticsearch, Fluentd, Kibana). How does it work? Logs from all services are collected by agents (such as Fluentd or Logstash), sent to Elasticsearch for indexing, and then you can view and analyze them using Kibana.

Why is this important? Imagine that an error only appears when several services interact. Without centralized logging, you would have to go to each server, find the logs you need, and manually compare them. But with ELK or EFK, you just go to Kibana, enter a query, and see all the logs related to this error, regardless of which servers or services they are on.

Now let's talk about monitoring. It's important not only to know that your services are running, but also to see how they are running. How many requests are being processed? What is the latency? How many errors are occurring? All of these metrics help you understand how healthy your microservices are and where bottlenecks might be.

For monitoring, a combination of Prometheus and Grafana is most often used. Prometheus collects metrics from your services — response time, number of errors, memory and CPU usage. And Grafana provides a convenient interface for their visualization. You can build dashboards that will show you the state of the entire system in real time.

Monitoring isn't just about performance. It also helps you understand how users interact with your services, which requests are creating the most load, and where potential problems might lie.

And don’t forget about alerts. You can’t sit in front of your monitor all the time and monitor all the graphs. Set up notifications that will trigger if something goes wrong. For example, if the number of errors exceeds the acceptable level or if the response time becomes critical. Such notifications can be set up directly in Grafana or through integration with other tools, such as PagerDuty or Slack.

So, advanced logging and monitoring are not a luxury, but a necessity in the world of microservices. Without them, you will be working blind, and this is a direct path to long nights spent finding and fixing problems.

5. Performance Optimization

Working with multiple microservices requires special attention to performance. In the world of microservices, everything is interconnected: if one service is slow, it can become a bottleneck and slow down the entire system. Therefore, it is important not only to write fast code, but also to consider all aspects of the system.

The first thing to pay attention to is code optimization. Yes, it may sound trivial, but clean and efficient code is the basis of performance. In Go, this is especially important because the language is initially designed for high loads. Remember about memory management, avoid unnecessary allocations, and use goroutines for parallel tasks.

But fast code alone is not enough. It is also important to optimize the data architecture. Think about how data is stored and processed: how often do you access the database? Are the indexes set up correctly? How are the queries structured? All of this greatly affects the speed of microservices.

Caching is worth paying special attention to. It is one of the most powerful tools for increasing productivity. If you often request the same data, it makes sense to cache it so as not to access the database each time. In Go, you can use Redis or Memcached for this. For example, if you have a heavy database query, save its result in the cache and update it only when the data actually changes.

Don’t forget about load balancing — this is a key point. In a microservice architecture, there are usually several instances of the same service running in parallel. But if requests are not distributed evenly, one instance may be overloaded, while others may be idle. This is where load balancers like NGINX or HAProxy come in. They distribute requests between instances so that each one is loaded evenly.

Another important thing is profiling. This is a process that helps you understand where exactly your system is slowing down. Go has a built-in tool for this called pprof, which allows you to see which parts of your code take up the most time and memory. This is especially useful when you have already optimized your code, but the system still does not work as fast as you would like.

Asynchronous task processing is another way to speed up the system. Not all tasks need to be executed immediately after they are received. For example, sending email notifications or updating statistics can be done in the background so as not to slow down the main processes. For this, message queues such as RabbitMQ or Kafka are used. They allow you to postpone the execution of tasks and process them as needed.

And finally, horizontal scaling. In the world of microservices, it is better to add another instance of a service than to try to squeeze the maximum out of one. This allows you to distribute the load and make the system more fault-tolerant. Kubernetes is your best friend in this matter, because it automatically scales instances depending on the current load.

Performance optimization is not a one-time action, but an ongoing process. You must monitor how the system works and look for opportunities for improvement. In the world of microservices, there are no trifles. Proper optimization will make your system fast, reliable, and resilient to any load.

6. Testing and CI/CD

When developing on a microservice architecture, testing becomes an integral part of the process. And you need to test everything — from individual code to the entire system as a whole. And here a well-tuned CI/CD (Continuous Integration / Continuous Deployment) process comes to the rescue.

Let's start with testing. In the world of microservices, testing isn't just about making sure a single piece of code works. It's about making sure the entire system works as expected when all the pieces are put together.

At the most basic level, unit tests check that each function or method works as expected. This is important, but it is not enough. You need to make sure that individual pieces of code work correctly, but more importantly, that they work correctly together with other parts of the system.

This is where integration tests come in. They check how different services interact with each other. For example, one service sends a request to another — it’s important to make sure that the request has arrived and the response has returned correctly. In a microservice architecture, such tests are especially important because each service has its own life, and you need to make sure that they are all synchronized.

Another important type of tests are contract tests. They are needed to make sure that services exchange data in the correct format. For example, if one service expects JSON with certain fields, you need to make sure that the other service sends exactly that data. Contract tests help prevent errors when changing the API.

And, of course, don't forget about load testing. It shows how the system behaves under high load. This is important for microservices, because the load may be distributed unevenly, and one of the services may become a bottleneck.

Now about CI/CD. In the world of microservices, CI/CD is not just a convenience, it’s a necessity. When you have many services, each of which can be updated independently, you need to be sure that each change goes through a rigorous review process before it gets to production.

Continuous Integration (CI) is a process that automates the build and testing of code with every change. Every time one of the developers commits the code, the system automatically builds the project, runs all the tests and checks that everything works as it should. This allows you to catch errors right away and not let bugs into production.

Continuous Deployment (CD) is the next step. If all tests pass, the code is automatically deployed to production. This allows changes to be delivered to users faster and reduces the risk of errors associated with manual deployment.

Automation is a key part of CI/CD. Use tools like Jenkins, GitLab CI, or GitHub Actions to set up the entire process so that developers can focus on writing code, not on how to deploy it.

And don't forget about the ability to roll back. Even if all tests pass, there is always a chance that something will go wrong in the production environment. Therefore, it is important to be able to quickly roll back changes and return to a stable version.

Ultimately, testing and CI/CD aren’t just a development step, they’re the foundation of development. They help ensure that every piece of code works as it should, that all services interact with each other correctly, and that any changes can be implemented quickly and safely into the system. In a world of microservices, where everything is so interconnected, this is especially important.

Conclusion

Well, now you know how to build your own microservices constructor. I hope this guide helped you avoid some of the pitfalls I once encountered. Microservices are not just a buzzword, but a truly powerful tool if you approach them wisely.

Of course, microservices architecture is not a magic wand that will solve all your development problems. It requires attention to detail, time for setup, and, yes, sometimes late nights with a cup of coffee in hand. But when done right, you get a flexible, scalable system that will please not only you, but your users as well.

So if there ever comes a time when your system is running like clockwork and you're enjoying a well-deserved rest, and someone asks how you did it, just say, “Well, it wasn't an easy road, but microservices now know who's boss!”

If you have any tips or questions about Go microservices, please share them in the comments!

And of course, don't forget that microservices are like a baby: they need to be fed (tests), dressed (logging), and protected (security). And if they start acting up, you can always go back to good old monoliths… or at least pretend it's a joke.