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Microservices and the UNIX Philosophy: Similarities and Differences

Microservices and the UNIX Philosophy: Similarities and Differences
Hostman Team
Technical writer
Microservices
17.02.2025
Reading time: 23 min

Microservices are a relatively new concept in software architecture. This approach focuses on breaking down complex monolithic applications into small, isolated modules. Each of these modules, or microservices, performs only one specific task.

Interestingly, UNIX developers began applying similar principles when creating their operating system long ago. One of these principles states: "A program should do one thing and do it well." This principle suggests that a program should be limited to the required set of functions and do nothing more, while performing its task flawlessly. This closely resembles the concept of a microservice, which is also designed to perform only one specific task.

But are all the principles of microservices and the UNIX philosophy truly similar? Let's explore this further, starting with the more classic approach — the UNIX philosophy.

The UNIX Philosophy

The history of UNIX began in 1969 when Ken Thompson and Dennis Ritchie started developing the operating system at Bell Labs. Ken Thompson, known as one of the creators of UNIX, made a significant contribution not only to the system itself but also to its philosophy.

In 1973, UNIX was rewritten from the B programming language to C, bringing hardware independence and various important features that we are familiar with today, such as different variable types (int, char, float, etc.) and the need for their declaration (statically typed). Throughout the 1970s and 1980s, UNIX evolved, spreading through academic circles and commercial organizations, gradually forming its key principles.

UNIX became a revolutionary project that changed the approach to operating system development. Its creators aimed for simplicity and elegance in design, which is reflected in the system's philosophy. The UNIX philosophy, with its emphasis on modularity and efficiency, became the foundation for many modern software development approaches.

Key UNIX principles, formed during its evolution, have significantly influenced the future of development. Principles like "Do one thing and do it well" have become fundamental for many modern software design methodologies.

The UNIX philosophy evolved alongside the UNIX system itself, gradually crystallizing into a set of clear principles. Over the years, many formulations of these ideas emerged, but their essence remained unchanged. Today, we’ll look at these key principles in their modern understanding:

  1. Write programs that do one thing and do it well.

  2. Write programs to work together.

  3. Write programs that handle text streams, because that is a universal interface.

Although we're discussing the principles and philosophy of UNIX, for practical examples, we’ll use Linux, specifically Debian. This choice is due to Debian's free availability, ease of access (including on the Hostman platform), and its status as a classic example of a Linux system.

While Linux is not a direct descendant of UNIX, it inherits all its principles. Most of the commands and concepts discussed are applicable to both UNIX and Linux. It’s also worth noting that the popular Ubuntu distribution is derived from Debian, highlighting the latter's importance in the Linux world.

Principle 1: Write Programs That Do One Thing and Do It Well

If you've worked with Linux systems, you're probably familiar with the cat program (short for concatenate). Although it appears as a command in the bash (command line), it is actually a standalone program written in C, compiled, and usually located at /usr/bin/cat. Its source code is publicly available online as part of the GNU coreutils project.

Example of using cat:

$ cat /etc/passwd
  • $ is the command line prompt displayed by the terminal and isn't typed by the user.

  • cat is the program itself. We don't specify the full path because the $PATH variable stores directories where BASH searches for commands by default (in this case, /usr/bin/).

  • /etc/passwd is a text file in Linux systems that contains user information.

The result of this command will be a list of system users, similar to:

root:x:0:0:root:/root:/bin/bash
alice:x:1000:1000:Alice Smith,,,:/home/alice:/bin/bash
bob:x:1001:1001:Bob Johnson,,,:/home/bob:/bin/zsh
mysql:x:112:120:MySQL Server,,,:/nonexistent:/bin/false
www-data:x:33:33:www-data:/var/www:/usr/sbin/nologin

Each line contains the username, UID, GID, full name, home directory, and other parameters, separated by colons.

You can experiment with cat and other commands by:

  • Setting up a VPS on Hostman (with affordable hourly billing).
  • Using a virtual machine or Docker to run Linux locally.
  • Using WSL (Windows Subsystem for Linux) on Windows.
  • Directly in your system if you're already using Linux.

cat has many additional options, for example:

  • Displaying line numbers: -n or --number.
  • Showing hidden characters, such as tabs and line breaks: -A or --show-all.
  • Compressing multiple blank lines into one: -s or --squeeze-blank.

You can explore other options by running cat --help or reading the full documentation with man cat.

One important feature of cat is its ability to concatenate the contents of multiple files. This is a result of its main function: cat reads the specified files sequentially and sends their contents to standard output (stdout). This is where its name comes from — concatenate means "to link" or "to chain together." In essence, displaying content on the screen is a combination of the file's contents and the stdout stream in Linux.

Example of using cat to concatenate files:

cat /etc/hostname /etc/hosts

The output might look something like this:

myserver
127.0.0.1 localhost
::1 localhost ip6-localhost ip6-loopback

In this example, cat first displays the contents of /etc/hostname, followed immediately by the contents of /etc/hosts.

In this way, cat adheres to the first principle of UNIX: "Do one thing and do it well." It specializes in reading files and sending their contents to stdout without unnecessary complexity, maintaining the modular and efficient approach that defines the UNIX philosophy.

Principle 2: Write Programs to Work Together

This principle showcases one of UNIX's key features — the ability to combine simple tools to solve complex tasks. Thanks to Doug McIlroy, who introduced the concept of pipes in UNIX, commands can be chained together. The output of one command can be the input for another. Let's look at an example:

cat /etc/passwd | grep user

grep is another program (command) that filters a text stream and returns only the lines containing the specified text — in this case, user. Here, cat outputs all the lines of the /etc/passwd file. grep then checks each line; if the text user is present, the program outputs that line.

The output would be the line corresponding to the user account:

user:x:1000:1000:,,,:/home/user:/bin/bash

We can extend this process by adding more commands:

cat /etc/passwd | grep user | awk '{print $6}'

This command outputs the home directory of the user.

awk is a special programming language for processing text data. In this case, the awk program acts as an interpreter for this language. In this example, awk splits the lines into columns using the default delimiter — a colon (:) — and prints only the sixth column, which is the home directory:

/home/user

In UNIX, commands can be chained into long pipelines to perform complex operations. This modular approach allows each tool to focus on a specific task, working seamlessly together to achieve sophisticated results.

Principle 3: Write Programs That Handle Text Streams, Because This Is a Universal Interface

This principle emphasizes the importance of using text format for data exchange between programs. Text data is easily readable by both humans and machines, making it a universal interface.

In UNIX, most configuration files, logs, and command outputs are in text format. This allows users to easily view, edit, and process data using standard text tools.

Example of text stream processing:

du -h /var/log | sort -rh | head -n 5 | awk '{print $2 " - " $1}'

This complex command uses several programs that exchange data through text streams:

  1. du -h /var/log — Displays the sizes of files and directories in /var/log in a human-readable format.
  2. sort -rh — Sorts the output by size in reverse order.
  3. head -n 5 — Selects only the top five lines.
  4. awk '{print $2 " - " $1}' — Reformats the output, displaying only the first two columns in reverse order and adding a dash between them.

Example output:

/var/log/syslog - 200M
/var/log/auth.log - 150M
/var/log/kern.log - 100M
/var/log/dpkg.log - 50M
/var/log/faillog - 10M

Using text streams provides flexibility and universality when working with data. It allows users to easily combine different tools and build complex information processing systems while maintaining simplicity and efficiency in interactions between components.

This approach to text-based communication and modular design is at the heart of the UNIX philosophy, influencing many modern development practices, including the design of microservices.

Microservices

The term "microservices" was first mentioned at a software architecture conference in Venice in 2011. Since then, microservices have become an integral part of modern software architecture. By 2015, industry giants like Netflix and Amazon had successfully implemented this approach. Since then, the popularity of microservices in IT has been steadily growing.

A monolith is short for "monolithic architecture." In this type of architecture, all the project’s code is typically stored in a single Git repository, worked on by all developers. This means that even a small change in one function requires redeploying the entire application.

  • Monolithic architecture is characterized by tight coupling between components, which makes independent scaling and updating challenging.

  • As the project grows, maintenance becomes more difficult, and build and test times increase.

  • Introducing new technologies is also harder because changing one part might affect the whole system.

Despite these drawbacks, monolithic architecture can be effective for small projects or in the early stages of development due to its simplicity and cohesiveness. Notably, some exceptions like Stack Overflow and Etsy successfully use monolithic architectures even at large scales.

Microservices replace monolithic architecture when a project grows so large that it becomes difficult to manage. In a monolith, every system deployment (uploading the project to the production server) requires coordination among all developers, and testing and building take a lot of time.

Microservices break the project down into modules, each performing a specific task. The principles of microservice architecture include:

  • Service independence: Each module operates independently.
  • Decentralized data management: Each module manages its own data.
  • Infrastructure automation: Deployment and scaling are automated.

For example, a user service is connected only to the user database and handles functions related solely to it, such as adding or updating users. Payment or analytics functions are managed by other microservices, which may have their own separate databases.

Over time, services become more complex as checks, validations, and new features are added. Each module can be assigned to a separate team, which resembles encapsulation in OOP (Object-Oriented Programming).

The independence of microservices allows teams to work autonomously, speeding up the development and deployment of new features. External developers only need to understand the interfaces without delving into internal details. This also accelerates testing and building.

Microservices allow the use of different programming languages, such as:

  • Go for multithreaded operations
  • JavaScript for rapid prototyping
  • Rust where high performance is required

As mentioned earlier, each microservice can have its own database, for example:

  • Metrics are stored in InfluxDB
  • Users are managed in PostgreSQL
  • Logs are saved in MongoDB

Essentially, a microservice can function solely as an abstraction layer over a database.

An important advantage of microservices is the ease of horizontal scaling. This allows the system's capacity to be increased by adding new servers, which is typically cheaper and more efficient than vertical scaling (enhancing the power of individual servers). This approach provides flexibility and cost-efficiency as the load grows. We'll discuss this in more detail in one of the following sections.

Despite its advantages, the microservice architecture makes projects more complex. New challenges arise, such as ensuring reliable communication between components, securing data during transmission, and complicating the deployment process.

These challenges fall on the shoulders of DevOps specialists. They develop and implement strategies for effectively managing a distributed system, including monitoring, logging, and deployment automation.

They also implement CI (Continuous Integration) and CD (Continuous Delivery) practices. Although solving these problems requires additional resources, it provides the flexibility and scalability needed for the system's long-term growth.

Modern Tools for Working with Microservices

The evolution of microservice architecture is closely linked to the development of tools for creating, deploying, and managing distributed systems. Containerization and container orchestration have become key technologies in this field.

Containerization and Docker

Containerization is an OS-level virtualization method that allows isolated processes to run in a shared environment. Launched in 2013, Docker became synonymous with containerization, revolutionizing the way applications are developed and deployed.

Docker allows you to package an application with all its dependencies into a standardized unit of software — a container. Containers typically host individual microservices, making them ideal for microservice architecture. They are lightweight, start quickly, and ensure consistent runtime environments from development to production.

The standardization of containers led to the creation of the Open Container Initiative (OCI) in 2015, ensuring compatibility between different containerization tools.

Container Orchestration and Kubernetes

With the growing popularity of containers, there arose a need for tools to manage large numbers of containers in distributed environments. This led to the concept of container orchestration.

Initially developed by Google and released in 2014, Kubernetes has become the de facto standard for container orchestration. It is a platform for automating the deployment, scaling, and management of containerized applications.

Key features of Kubernetes:

  • Automatic scaling of containers based on load.
  • Load balancing between containers.
  • Self-healing capabilities for failed containers or nodes.
  • Configuration and secret management.
  • Zero-downtime deployments for updates.

Kubernetes allows the creation of clusters — groups of computers working as a unified system. This makes it ideal for microservice architecture, enabling efficient management of the lifecycle of numerous, distributed microservices.

Modern Tools for Microservices Development

Modern microservice development relies on a variety of tools and services that simplify the creation, deployment, and management of distributed systems. Russian cloud providers like Hostman offer comprehensive solutions for working with microservices:

  • Cloud Servers and VDS/VPS: Hostman provides virtual machines with pay-as-you-go billing, making them perfect for flexible microservices scaling.

  • Kubernetes: Managed Kubernetes clusters in Hostman enable efficient container orchestration, automating deployment, scaling, and management of microservices.

  • Cloud Databases: The Database-as-a-Service solution simplifies data management in microservice architectures, ensuring high availability and scalability.

  • S3 Object Storage: This service provides reliable storage for large volumes of data, which is often required in microservice applications.

  • Load Balancers: Load distribution between servers is critical for maintaining microservices' performance and fault tolerance.

  • App Platform: This service simplifies the deployment of applications from repositories, streamlining the deployment process for microservices.

These tools and services allow developers to create reliable, scalable, and secure microservice applications. They provide the necessary infrastructure and management tools, enabling teams to focus on developing business logic rather than solving infrastructure challenges.

Microservices vs Monolithic Architecture

Monolithic Architecture

Monolithic architecture has several advantages, especially in the early stages of development:

  • Simplicity in Development: All the code is located in a single repository, simplifying the development and debugging processes.

  • Unified Code Base: All developers work on the same code base, which fosters a better understanding of the project as a whole.

  • Simplified Deployment: A monolith is deployed as a single application, streamlining the deployment process.

  • Ease of Testing: Integration testing is easier since all components are within one application.

  • Performance: In some cases, a monolith can be more performant due to the lack of network overhead between components.

Microservice Architecture

However, as a project grows, microservice architecture begins to showcase its advantages:

  • Scalability: Each microservice can be scaled independently, optimizing resource usage.

  • Flexibility in Technology Choice: Different microservices can use the most suitable technology stack for their needs.

  • Independent Deployment: Services can be updated and deployed independently of each other, speeding up the development and release of new features.

  • Fault Isolation: Issues in one microservice do not affect the entire system.

  • Ease of Understanding and Maintenance: Each microservice is smaller and simpler than a monolith, making it easier to understand and maintain.

Comparison Table

Aspect

Monolith

Microservices

Development

Easier in early stages

More complex but more flexible as the project grows

Deployment

Simple but requires full updates

More complex but allows for partial updates

Scalability

Vertical, entire application

Horizontal, individual services

Reliability

One failure can affect the whole system

Failures are isolated within individual services

Tech Stack

Unified for the whole application

Can vary across different services

Performance

Potentially higher for small applications

Can be optimized for large systems

Team Collaboration

Entire team works on one codebase

Teams can work on separate services

Choosing between monolithic and microservice architecture depends on the project's size, requirements for flexibility, and scalability.

  • Monolithic Architecture is often preferable for smaller projects or MVPs (Minimal Viable Products) due to its simplicity and ease of deployment.

  • Microservices are better suited for large, complex systems with high demands for scalability and flexibility.

The decision should be based on the specific needs and long-term goals of the project.

Example of Microservices Architecture Development

Let's look at an example of creating a system for autonomous vehicle management. In this system, a cluster of interconnected servers automatically distributes containers across servers, optimizing resource usage and ensuring fault tolerance.

For instance:

  • Computer Vision Containers will run on a computer with a powerful GPU, which is necessary for fast visual data processing.

  • Vehicle Monitoring Services require a reliable CPU and can, therefore, be placed on less powerful but stable hardware.

  • Other Microservices will be evenly distributed across the cluster.

This architecture creates a fault-tolerant system. If one node (a separate unit in the distributed network) fails, the microservices can automatically move to another computer within the vehicle. Essentially, this replicates cloud architecture on local devices (on-premise), ensuring system continuity even with partial hardware failure.

On each node of this cluster, containers are launched—isolated microservices performing their specific tasks. This ensures flexibility in resource distribution and system management, optimizing the operation of each component of the autonomous vehicle.

Key Microservices in the System

  1. Motion Control Service: Controls steering, braking, and acceleration.
  2. Computer Vision Service: Processes camera data, recognizing objects and road situations.
  3. Navigation Service: Plans the route and makes driving decisions.
  4. Communication Service: Ensures communication with other vehicles and infrastructure.
  5. State Monitoring Service: Tracks the technical condition of the vehicle.

Each microservice operates autonomously and interacts with others through well-defined interfaces, providing several benefits:

  • Independent Updates

For example, the Computer Vision Microservice is a critical module responsible for recognizing road signs, lane markings, other road users, and obstacles. Its accuracy directly impacts the safety of the driver, passengers, and other road users.

With a microservices architecture, development and improvement can focus solely on this module without affecting other system components. A specialized team of computer vision and machine learning experts can work exclusively on enhancing this module.

  • Simplified Deployment

Imagine the team has developed a new machine learning model that significantly improves sign recognition under poor visibility conditions, such as fog or heavy rain. After thorough testing, only this specific module needs to be updated.

Moreover, the update can be deployed "Over the Air" (OTA) since only one microservice is updated, and the amount of data transferred is relatively small.

If the entire system had to be rebooted just to update the computer vision module, the vehicle would require a high-speed connection and a long time to download and install updates for the entire system.

  • Scalability and Fault Tolerance

Microservices architecture also enables easy scalability of individual system components. For example, if the new recognition model demands more computing power, the GPU capacity for the computer vision module can be increased without affecting other modules.

Additionally, this architecture enhances the system's fault tolerance. If the computer vision module fails during an update or operation, it won't lead to a total system crash. Other microservices can continue functioning, possibly using a previous version of the computer vision module or with limited functionality.

  • Flexibility in Technology Choice

Each microservice can be implemented using the most suitable technologies for its tasks. For the computer vision module, specialized machine learning libraries such as TensorFlow or PyTorch, optimized for GPU operations, can be used.

Meanwhile, other modules—like the communication module—can be built using different programming languages and technologies better suited for their tasks.

The microservices architecture provides the flexibility, scalability, and efficiency needed to develop and maintain complex systems like autonomous vehicle management. It allows continuous improvement of individual components without risking the integrity of the entire system.

Comparison of Microservices and the UNIX Philosophy

Despite the decades that separate the concepts of UNIX and microservices, parallels can be drawn between them. Comparing microservices with UNIX reveals both common principles and unique features of each approach. Both strive for modularity and specialization of components. Microservices, like UNIX utilities, often perform a single specific task, whether it's managing users or access or serving as an abstraction for a database. However, microservices are typically more complex and can grow with additional features.

The interaction of components is implemented differently: UNIX uses native stdin and stdout redirection through pipes, while microservices require specific protocols (REST, RPC) with clearly documented interfaces. This complicates communication between services compared to the simplicity of the UNIX approach.

However, both approaches often rely on a text-based format for data exchange. In microservices, this is typically JSON or YAML, aligning with the principle of text streams in UNIX.

These similarities and differences demonstrate the evolution of modularity and component interaction ideas in software development. Despite the time gap between their emergence, UNIX and microservices share many key concepts, highlighting the universality of certain development principles.

Similarities

Modularity and single responsibility:

  • UNIX: Utilities perform one task and do it well.

  • Microservices: Each service is responsible for a specific function (user management, access, caching).

Component Interaction:

  • UNIX: Utilities work together through pipelines.

  • Microservices: Services interact via APIs.

Text-based data format:

  • UNIX: Uses text streams for data exchange.

  • Microservices: Often use text formats (JSON, YAML) for data exchange.

Differences

Component Complexity:

  • UNIX: Utilities are usually simple and perform a minimal set of functions.

  • Microservices: Can be more complex and accumulate additional features.

Interaction Mechanism:

  • UNIX: Native stdin and stdout redirection through pipes.

  • Microservices: Require data transfer protocols (REST, RPC) with clearly defined interfaces.

Execution Context:

  • UNIX: Typically runs on a single computer with minimal delays.

  • Microservices: Can be distributed across different servers and data centers.

Goals and Application:

  • UNIX: Focused on the stability and reliability of the operating system.

  • Microservices: Focus on business logic and application flexibility.

Development and Deployment Complexity:

  • UNIX: Relatively simple development and installation of utilities.

  • Microservices: Require complex infrastructure for development, testing, and deployment.

Conclusion

We have analyzed the UNIX philosophy and microservices architecture, identifying both similarities and differences between these approaches to software development. Despite being separated by decades, both approaches demonstrate remarkable unity in key principles.

The main similarities we found include:

  • Modularity: Both UNIX and microservices aim to divide functionality into small, manageable components.

  • Specialization: Both approaches support the idea that each component should perform one task and do it well.

  • Interaction: Both UNIX and microservices emphasize effective communication between components.

However, we also identified significant differences:

  • Context of application: UNIX is designed to work on a single computer, while microservices are intended for distributed systems.

  • Complexity: Microservices are generally more complex to develop and deploy compared to UNIX utilities.

  • Flexibility: Microservices provide greater flexibility in choosing technologies and scaling individual components.

These similarities and differences are not coincidental. They reflect the evolution of software development principles in response to changing needs and technological advancements. The UNIX philosophy, created in the era of mainframes, laid the groundwork for the modular approach that today finds new expression in microservices, meeting the demands of the cloud computing and distributed systems era.

The principles underlying the UNIX philosophy and microservices architecture are also reflected in other software development methodologies. Object-Oriented Programming (OOP), with its concept of encapsulation and the SOLID principles, emphasizes the importance of modularity and specialization. The Single Responsibility Principle (SRP) from SOLID resonates with UNIX’s idea of “doing one thing well.” Design patterns such as facade, adapter, and singleton promote the creation of modular and efficiently interacting components. Functional programming, with its focus on pure functions and immutability, also shares the idea of creating small, well-defined components, which aligns with the principles of both UNIX and microservices architecture.

For modern developers and architects, understanding these principles is critically important. It allows us to learn from the time-tested ideas of UNIX, adapting them to the contemporary demands of scalability and flexibility that microservices provide.

Looking to the future, we can expect further development of both approaches. We will likely see new tools and practices that will simplify the development and deployment of microservices, making them accessible to a wider range of projects. At the same time, the principles of UNIX are likely to remain relevant, continuing to influence the design of operating systems and development tools.

Microservices
17.02.2025
Reading time: 23 min

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Microservices

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This is because gRPC was very well designed—it is reliable and fast and allows information systems to be built from heterogeneous nodes and components, much like the great CORBA once did. However, let’s assume I do not need cross-ecosystem interaction; I program only in Python/Golang/Java/(insert your language), and I want tools for distributed computing. Should I use gRPC, which, by the way, requires some time to master, or is there something that can help me "immediately and at low cost"? We are in luck. Today, RPC packages and service libraries are available in almost every programming ecosystem, such as: Python — xmlrpc package Go — net/rpc package Java — java.rmi (Remote Method Invocation) Haskell — WAI, xmlrpc, built-in OTP tools for distributed computing and clustering JavaScript — JSON-RPC Each of the aforementioned packages within its language ecosystem allows you to connect components together. To illustrate this with code, let's take a simple example from the documentation of the xmlrpc module in Python's standard library. RPC server code: from xmlrpc.server import SimpleXMLRPCServer def is_even(n): return n % 2 == 0 server = SimpleXMLRPCServer(("localhost", 8000)) print("Listening on port 8000...") server.register_function(is_even, "is_even") server.serve_forever() RPC client code: import xmlrpc.client with xmlrpc.client.ServerProxy("http://localhost:8000/") as proxy: print("3 is even: %s" % str(proxy.is_even(3))) print("100 is even: %s" % str(proxy.is_even(100))) As we can see, on the client side, everything looks very clear and simple, as if the is_even function is part of the client's own code. Everything is also quite simple and understandable on the server side: we define a function and then register it in the context of the server process responsible for RPC. It is important to note that the function we "expose" for external access is a regular function written in Python. It can easily be used locally in the server-side code, passing parameters to it and receiving the value it returns. The concept of RPC is very simple, elegant, and flexible: to call a function "on the other side," you only need to change the transport from local calls within a process to some network communication protocol and ensure bidirectional translation of parameters and results. REST So what is wrong with RPC, and why did we end up with REST as well? The first and perhaps the most serious reason is that RPC must have a layer that describes the nature of the data, interfaces, functions, and return calls. In CORBA, this is IDL; in gRPC, it is ProtoBuf. Even the slightest change requires synchronization of all definitions and interfaces. The second point, perhaps, stems from the very concept of a "function"—it is a black box that takes arguments as input and returns some value. A function does not describe or characterize itself in any way; the only way to understand what it does is by calling it and getting some result. Accordingly, as mentioned above, we need a description to determine the nature and order of computations. REST, as already mentioned at the beginning of this article, stands for REpresentational State Transfer, a protocol for transmitting representational state. It is important to clarify the meaning of the term "representational"—it means "self-descriptive," representing itself. Consequently, a certain state that is transferred between exchange participants does not require additional agreements, descriptions, or definitions—everything necessary, so to speak, is clear without words and is contained in the message itself. The term REST was introduced by Roy Fielding, one of the authors of HTTP, in 2000, in his dissertation "Architectural Styles and the Design of Network-based Software Architectures." He provided the theoretical basis for the way clients and servers interact on a global network, abstracting it and calling it "representational state transfer." Roy Fielding developed a concept for building distributed applications in which each request (REST request) from a client to a server already contains all the necessary information about the desired server response (the desired representational state), and the server is not required to store information about the client's state ("client session"). So, how does this work? In REST API, each service, each unit of information is designated by its URL. Thus, data can be retrieved simply by accessing this URL on the server. The URL in REST is structured as follows: /object/ — directs us to a list of objects /object/id — directs us to a single object with the specified ID or returns a 404 response if such an object is not found Thus, the very nature of defining a URL represents the nature of the server's response: in the first case—a list of objects, in the second—a single object. But that is not all. REST, as mentioned above, uses HTTP as its transport. And in HTTP, one of the key parameters that define the nature of the data returned by the server is the method. By using HTTP methods, we can define another set of self-descriptive states: GET /object/ — returns a list of objects GET /object/id — returns an object with the specified ID or 404 POST /object/ — creates a new object or returns an error (most often an error with code 400 or another) PUT /object/id — edits an object with the specified ID or returns errors DELETE /object/id — deletes an object with the specified ID or returns errors Some servers ignore the semantics of the PUT and DELETE methods; in this case, the POST /object/id method is used with a request body (object data) for editing or the same POST request with an empty body for deleting an object. Thus, instead of the variety of choices that REST provides us, we get a minimal set of operations on data. So, where is the advantage here? As mentioned above, REST is an architectural solution, not a technology. This means that REST does not impose any special requirements on participants in such a network, as is the case with gRPC, CORBA, or SOAP. It is only necessary to maintain the semantics of a self-defining state and a unified data transmission protocol. As a result, REST networks can combine the incompatible—a powerful cluster with load balancers, databases, and a simple "smart" light bulb with a microcontroller that is controlled via REST. Thus, REST is an extremely flexible architecture with virtually zero costs to ensure interoperability. However, to guarantee such an impressive result, REST introduces a number of restrictions (which is why this solution is also called architectural constraints). Let’s briefly list each of them: Client-server architecture. The architecture of REST networks must be based on the client-server model. Separating the client's interface needs from the server's needs improves the portability of client interface code, while simplifying the server part enhances scalability. Statelessness. The server should not store any special information about the client between calls. Traditional WEB sessions are not acceptable here. The server must receive all necessary information about the client's state from the request. Caching. The results of the server's response can be cached. This helps improve system performance. The server must ensure that the client receives up-to-date information if caching is applied. Uniform interface. This concerns the unified way of writing object URLs, which has already been discussed, and the semantics of HTTP methods. It also implies that the transport data format is one that is identically interpreted by both the server and the client. Typically, this is JSON, but there can be combined options when JSON and CBOR are used (the data type is described in the Content-Type header). Scalability and layers. The client should make no assumptions about how the server is structured. This allows for flexible system scalability, the use of caches, load balancers, and much more. By following the above constraints, we can build highly efficient systems, which is confirmed by our modern experience with distributed systems and web services. One of the most popular patterns implemented using REST is CRUD. This acronym is formed from the first letters of the operations Create, Read, Update, and Delete—the four basic operations sufficient for working with any data entity. More complex operations, known as use cases, can utilize CRUD REST API to access data entities. Use cases can also follow the prescriptions and constraints of REST; in this case, we call our information system RESTful. In such a system, REST conventions are used everywhere, and any expansion of the system also follows these conventions. This is a very pragmatic yet highly flexible approach: a unified architecture reduces system complexity, and as system complexity decreases, the percentage of errors also goes down. The concept of REST API is so popular that it exists in almost every programming language ecosystem. REST is built into Django and Laravel. In Go, you can use the Gin Gonic package or build your own RESTful system using only standard library packages. For Erlang, the erf library can be used, while in Elixir, REST API is already integrated into the Phoenix framework. REST, as an architecture, does not impose any restrictions on programming environments, frameworks, or anything else—it simply declares to services: "Just speak REST, and everything will work out fine." Let’s try to answer the question we posed at the very beginning. As you may have realized from this rather extensive article, each approach has its clear advantages and very specific disadvantages. In this matter, the best option is a golden mean. For critical services that process huge amounts of data, stability is the top priority—both in code, where data definition errors are simply unacceptable and in infrastructure, where faster system response time is always better. For such areas, the concept of RPC in its modern implementation—gRPC—is undoubtedly more convenient. However, where business logic and complex multi-level interactions reside, REST becomes the preferable choice with its rigid and limited means of expression. The best strategy is to apply both approaches wisely and flexibly, allowing your information system to benefit from each concept's strengths (or architectural solution). When discussing pure RPC and REST, we have deliberately abstracted from infrastructure, programming languages, machines, memory, processors, and other technical details. However, in real-world business, these aspects are equally important. Most often, REST API and RPC API are deployed either in containers (Docker, Podman, and similar technologies) or on so-called VPS (Virtual Private Servers). Less frequently, they run on dedicated or rented hardware. Infrastructure-as-a-Service (IaaS) is a convenient and relatively inexpensive way to manage projects. Hostman’s networking services provide an ideal solution for this. Here, you can precisely calculate the expected load and plan your expenses accordingly. The VPC (Virtual Private Cloud) from Hostman allows containers and VPS to be interconnected while ensuring that all traffic within this network remains completely isolated from the Internet. An ideal solution for RPC, REST, or…? The decision is, of course, yours to make. But as for how to deploy everything and ensure the uninterrupted operation of your services—Hostman has you covered.
01 April 2025 · 15 min to read
Microservices

Developing an HTTP Client in Go: From Installation to First Requests

Using APIs to communicate with external services is becoming more and more crucial when creating applications. With APIs, applications can transmit and receive data across a network and communicate with each other. One of the most popular standards for creating and using APIs is REST (Representational State Transfer), which is based on the HTTP protocol. Go has established itself as a powerful programming language for web development due to its performance, simplicity, and built-in support for network protocols. One of the key tasks that Go developers often need to solve is creating HTTP clients to interact with third-party REST APIs. In this article, we will help developers who are new to Go and REST APIs build their first HTTP client. We will start with the basics and progress to more advanced topics, such as sending different types of HTTP requests, handling responses, and automating requests. Additionally, we will explore practical examples and best practices to help you create secure and reliable HTTP clients. Setting Up the Environment First, let’s set up our working environment. We need to install Go tools, configure a development environment, and initialize a new project. Installing the Go Compiler Go supports all major operating systems: Windows, Linux, and macOS. We’ll briefly show the installation process for all of them. Let’s start with  Windows. Follow these steps: Go to the official Go website. Download the installation package for your operating system (either 32-bit or 64-bit version). Run the downloaded file and follow the installation wizard's instructions. Verify the installation was successful by checking the Go version. go version For macOS, you can either download and run the installer or use a package manager like Brew or MacPorts: brew install go Or: sudo port install go For Linux distributions, use a package manager: Ubuntu: sudo snap install go --classic Debian: sudo apt-get install golang-go CentOS/AlmaLinux:  sudo dnf install golang Arch Linux: sudo pacman -S go Configuring an IDE or Text Editor You don’t exactly have to use an IDE (integrated development environment) as Go provides a flexible set of tools for building applications using the command line.  However, an IDE or a text editor with Go support can still enhance your development experience, making it more convenient and efficient. Below are some popular options: Visual Studio Code (VSCode): A lightweight yet powerful editor with excellent Go support through extensions. This is the editor we use in this article. Vim/Neovim: Highly customizable editors with plugin support for Go, such as vim-go. Emacs: A powerful and customizable text editor widely used for text editing, with Go support available through various packages and extensions. If you decide to use VSCode, install the official "Go" extension from the Go development team to enable autocomplete, debugging, and other useful features. To do this: Open VSCode. Go to the Extensions tab or press Ctrl+Shift+X. Search for the Go extension and install it. Initializing a New Project Now that your development environment is ready, let's create a new Go project to develop our HTTP client. Create and navigate to your project directory: mkdir httpclient && cd httpclient Initialize a new Go module: go mod init httpclient After running this command, a go.mod file should appear, which will store information about the module and its dependencies. Create and open the main project file using VSCode: code main.go If everything is working correctly, intermediate command outputs should look normal. Open the main.go file in your editor and add the following code: package main import ( "fmt" ) func main() { fmt.Println("Hello, HTTP Client in Go!") } Run the program to verify everything is working correctly: go run main.go If you have followed the steps correctly, you should see the message: Hello, HTTP Client in Go! Now, you have a fully set up Go development environment and an initialized project. In the next chapters, we will start building a full-fledged HTTP client, sending requests to an API, and handling responses. Sending HTTP Requests in Go In this section, you will learn how to send different HTTP requests (GET, POST, PUT, DELETE) using Go’s standard net/http library. We will start with basic methods and gradually move on to more complex scenarios. Creating and Configuring an HTTP Client: GET and POST Requests Before sending requests, you need to create an instance of an HTTP client. In Go, this is done using the http.Client{} struct. For this example, we will use JSONPlaceholder, a free test API that provides basic resources accessible via HTTP methods. Such APIs are an excellent solution for testing and understanding how different requests work. No special tokens, registration, or authentication are required — you can run all the code on your local machine to see how it works in practice. The GET method is used to retrieve data. Here’s how it is implemented in Go using the http.Get() function. In your main.go file, add the following code: package main import ( "context" "fmt" "net/http" "time" "httpclient/client" ) func main() { // Initialize a custom HTTP client httpClient := client.NewHTTPClient(&http.Client{ Timeout: 10 * time.Second, }) ctx := context.Background() // Fetch an existing blog post using the custom HTTP client blogPost, _, err := httpClient.GetBlogPost(ctx, 1) if err != nil { fmt.Println("Error:", err) return } fmt.Println("Blog Post:") fmt.Printf(" ID: %d\n", blogPost.ID) fmt.Printf(" Title: %s\n", blogPost.Title) fmt.Printf(" Body: %s\n", blogPost.Body) fmt.Printf(" User ID: %d\n", blogPost.UserID) // Attempt to fetch a non-existing post blogPost, _, err = httpClient.GetBlogPost(ctx, -1) if err != nil { fmt.Println("Error:", err) return } fmt.Println("Blog Post:", blogPost) } Now, create a client.go file inside the client subdirectory and add the following code: package client import ( "bytes" "context" "encoding/json" "errors" "fmt" "io" "net/http" "net/url" "strings" ) const ( defaultBaseURL = "https://jsonplaceholder.typicode.com/" ) type HTTPClient struct { client *http.Client BaseURL *url.URL } // Initialize a new HTTP client func NewHTTPClient(baseClient *http.Client) *HTTPClient { if baseClient == nil { baseClient = &http.Client{} } baseURL, _ := url.Parse(defaultBaseURL) return &HTTPClient{ client: baseClient, BaseURL: baseURL, } } // Create a new HTTP request func (c *HTTPClient) NewRequest(method, urlStr string, body any) (*http.Request, error) { if !strings.HasSuffix(c.BaseURL.Path, "/") { return nil, fmt.Errorf("BaseURL must have a trailing slash, but %q does not", c.BaseURL) } u, err := c.BaseURL.Parse(urlStr) if err != nil { return nil, err } var buf io.ReadWriter if body != nil { buf = &bytes.Buffer{} err := json.NewEncoder(buf).Encode(body) if err != nil { return nil, err } } req, err := http.NewRequest(method, u.String(), buf) if err != nil { return nil, err } if body != nil { req.Header.Set("Content-Type", "application/json") } return req, nil } // Execute the HTTP request func (c *HTTPClient) Do(ctx context.Context, req *http.Request, v any) (*http.Response, error) { if ctx == nil { return nil, errors.New("context must be non-nil") } req = req.WithContext(ctx) resp, err := c.client.Do(req) if err != nil { select { case <-ctx.Done(): return nil, ctx.Err() default: } return nil, err } defer resp.Body.Close() err = CheckResponse(resp) if err != nil { return resp, err } switch v := v.(type) { case nil: case io.Writer: _, err = io.Copy(v, resp.Body) default: decErr := json.NewDecoder(resp.Body).Decode(v) if decErr == io.EOF { decErr = nil // Ignore EOF errors caused by empty response body } if decErr != nil { err = decErr } } return resp, err } // Check if the HTTP response indicates an error func CheckResponse(resp *http.Response) error { if c := resp.StatusCode; 200 <= c && c <= 299 { return nil } return fmt.Errorf("%s %s: %s", resp.Request.Method, resp.Request.URL, resp.Status) } // BlogPost represents a blog post entity type BlogPost struct { ID int64 `json:"id"` Title string `json:"title"` Body string `json:"body"` UserID int64 `json:"userId"` } // Fetch a blog post by ID func (c *HTTPClient) GetBlogPost(ctx context.Context, id int64) (*BlogPost, *http.Response, error) { u := fmt.Sprintf("posts/%d", id) req, err := c.NewRequest(http.MethodGet, u, nil) if err != nil { return nil, nil, err } b := new(BlogPost) resp, err := c.Do(ctx, req, b) if err != nil { return nil, nil, err } defer resp.Body.Close() return b, resp, nil } main.go: Contains the application's entry point, initializes the HTTP client, and performs basic operations. client.go: Handles the HTTP client logic, defining its structure, initialization functions, and request methods. This modular approach allows for easy reuse in other projects and makes testing the client independent of the main application. The problem with http.DefaultClient is that it is a global variable, meaning any changes to it affect the entire program which creates security and stability risks. Besides, http.DefaultClient lacks flexible configuration options, such as setting timeouts, TLS settings, proxies, or cookie management. By initializing our own HTTP client with http.Client{} and custom settings, we avoid these issues and ensure greater flexibility and security in our application. The POST method is used to send data to a server. In Go, there are two ways to send a POST request: Post() — Used for sending data in various formats (JSON, XML, binary). Features: Requires explicitly setting the Content-Type header (e.g., application/json). Data is sent as a byte array ([]byte). Allows custom request headers. PostForm() — Optimized for submitting HTML form data (application/x-www-form-urlencoded). Features: Automatically sets the Content-Type header. Accepts data as a url.Values structure (similar to map[string][]string). Simplifies working with form parameters (login, registration, search). To send POST requests, we need to add functions that allow us to send data to a server. Below, we will implement two types of POST requests: CreateBlogPost: Sends JSON data. PostForm: Sends form-encoded data. Copy the following function into your client.go file: func (c *HTTPClient) CreateBlogPost(ctx context.Context, input *BlogPost) (*BlogPost, *http.Response, error) { req, err := c.NewRequest(http.MethodPost, "posts/", input) if err != nil { return nil, nil, err } b := new(BlogPost) resp, err := c.Do(ctx, req, b) if err != nil { return nil, nil, err } defer resp.Body.Close() return b, resp, nil } Copy the following function into your client.go file: func (c *HTTPClient) PostForm(myUrl string, formData map[string]string) (string, error) { form := url.Values{} for key, value := range formData { form.Set(key, value) } resp, err := c.client.PostForm(myUrl, form) if err != nil { return "", fmt.Errorf("error making POST form request: %w", err) } defer resp.Body.Close() body, err := io.ReadAll(resp.Body) if err != nil { return "", fmt.Errorf("error reading response body: %w", err) } return string(body), nil } Don’t forget to import the net/url package in client.go. Now, modify your main.go file to call the CreateBlogPost function: package main import ( "context" "fmt" "net/http" "time" "httpclient/client" ) func main() { // Initialize a custom HTTP client httpClient := client.NewHTTPClient(&http.Client{ Timeout: 10 * time.Second, }) ctx := context.Background() input := &client.BlogPost{ Title: "foo", Body: "bar", UserID: 1, } // Create a new blog post using the custom HTTP client blogPost, _, err := httpClient.CreateBlogPost(ctx, input) if err != nil { fmt.Println("Error:", err) return } fmt.Println("Created Blog Post:") fmt.Printf(" ID: %d\n", blogPost.ID) fmt.Printf(" Title: %s\n", blogPost.Title) fmt.Printf(" Body: %s\n", blogPost.Body) fmt.Printf(" User ID: %d\n", blogPost.UserID) } After running the program (go run .), you should see an output similar to this: Working with Other Request Types (PUT, DELETE, etc.) Similarly to GET and POST, you can send other HTTP requests.  PUT is used to completely replace a resource or create it if it does not exist. DELETE is used to remove a resource at the specified URL. To work with PUT and DELETE, use a universal approach with http.NewRequest. Add the following functions to client.go: func (c *HTTPClient) PutJSON(myUrl string, jsonData []byte) (string, error) { req, err := http.NewRequest(http.MethodPut, myUrl, bytes.NewBuffer(jsonData)) if err != nil { return "", fmt.Errorf("error creating PUT request: %w", err) } req.Header.Set("Content-Type", "application/json") resp, err := c.client.Do(req) if err != nil { return "", fmt.Errorf("error making PUT request: %w", err) } defer resp.Body.Close() body, err := io.ReadAll(resp.Body) if err != nil { return "", fmt.Errorf("error reading response body: %w", err) } return string(body), nil } func (c *HTTPClient) Delete(myUrl string) (string, error) { req, err := http.NewRequest(http.MethodDelete, myUrl, nil) if err != nil { return "", fmt.Errorf("error creating DELETE request: %w", err) } resp, err := c.client.Do(req) if err != nil { return "", fmt.Errorf("error making DELETE request: %w", err) } defer resp.Body.Close() body, err := io.ReadAll(resp.Body) if err != nil { return "", fmt.Errorf("error reading response body: %w", err) } return string(body), nil } Modify your main.go file to call these new functions: package main import ( "fmt" "net/http" "time" "httpclient/client" ) func main() { httpClient := client.NewHTTPClient(&http.Client{ Timeout: 10 * time.Second, }) // Example PUT request jsonToPut := []byte(`{"id": 1, "title": "foo", "body": "bar", "userId": 1}`) putResp, err := httpClient.PutJSON("https://jsonplaceholder.typicode.com/posts/1", jsonToPut) if err != nil { fmt.Println("Error:", err) } else { fmt.Println("PUT Response:", putResp) } // Example DELETE request deleteResp, err := httpClient.Delete("https://jsonplaceholder.typicode.com/posts/1") if err != nil { fmt.Println("Error:", err) } else { fmt.Println("DELETE Response:", deleteResp) } } After running the program (go run .), you should see the following output: For more complex scenarios, you can configure: Client timeouts Retry logic Custom authorization headers This section has covered how to create and configure an HTTP client and send different types of HTTP requests. Now, you can move on to more advanced REST API interactions. Interacting with REST API in Go Now that we understand how to send HTTP requests in Go, let's explore how to interact with a REST API. We will: Create data models to handle API responses Convert received data into structured objects Demonstrate an example of usage We will start by sending a request to retrieve a list of posts and processing the received response. Creating Data Models for API Responses In Go, API responses are typically processed using structs. Defining structs to store data allows us to handle API responses more conveniently and safely. Here is an example of a Post struct: package main type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } This struct matches the JSON format returned by the API. The attributes are marked with JSON tags to ensure correct data conversion. Converting API Responses into Structured Data Now, let's send a GET request to the API and convert the response into a Go struct. Here is the full main.go implementation: package main import ( "fmt" "net/http" "time" "httpclient/client" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } func main() { // Initialize HTTP client httpClient := client.NewHTTPClient(&http.Client{ Timeout: 10 * time.Second, }) // Fetch post data post, err := httpClient.GetBlogPost(1) if err != nil { fmt.Println("Error:", err) return } // Print post details fmt.Printf("Post ID: %d\n", post.ID) fmt.Printf("User ID: %d\n", post.UserID) fmt.Printf("Title: %s\n", post.Title) fmt.Printf("Body: %s\n", post.Body) } Modify the GetBlogPost function in client.go: func (c *HTTPClient) GetBlogPost(postID int) (*Post, error) { resp, err := c.Client.Get(fmt.Sprintf("https://jsonplaceholder.typicode.com/posts/%d", postID)) if err != nil { return nil, fmt.Errorf("error making GET request: %w", err) } defer resp.Body.Close() var post Post err = json.NewDecoder(resp.Body).Decode(&post) if err != nil { return nil, fmt.Errorf("error decoding response body: %w", err) } return &post, nil } In this example, we: Initialize the HTTP client Send a GET request Retrieve post data Convert the JSON response into a Post struct Print the post details After running the program (go run .), you should see output similar to this: Handling API Responses in Go In this chapter, we will explore how to process responses from a REST API in Go. We will cover topics such as checking HTTP status codes, handling response bodies, and managing and logging HTTP errors. Checking HTTP Status Codes An HTTP status code indicates the result of an HTTP request. It helps determine whether an operation was successful or if an error occurred. Two of the most common HTTP status codes are: 200 (OK) indicates that the request was successful. 404 (Not Found) means the requested resource does not exist. The main.go file: package main import ( "fmt" "net/http" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } func main() { httpClient := NewHTTPClient() // GET request response, err := httpClient.Get("https://jsonplaceholder.typicode.com/posts/1") if err != nil { fmt.Println("Error:", err) return } defer response.Body.Close() if response.StatusCode != http.StatusOK { fmt.Printf("Error: Received non-200 response code: %d\n", response.StatusCode) return } fmt.Printf("Received a successful response. Status code: %d\n", response.StatusCode) } In the client.go file, we will define a  simple Get() method: func (c *HTTPClient) Get(url string) (*http.Response, error) { resp, err := c.Client.Get(url) if err != nil { return nil, fmt.Errorf("error making GET request: %w", err) } return resp, nil } In this example, we send a GET request and check the response status code. Depending on whether the request is successful or not, you will see different output messages. Processing the Response Body (XML) Once we have checked the HTTP status code, we can move on to processing the response body. Most APIs return data in JSON format, but some may use XML or other formats. Previously, we demonstrated handling JSON responses. Here, we will cover XML processing instead. Since JSONPlaceholder does not support XML, we will use a different public API in main.go that can work with XML: package main import ( "fmt" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } type Response struct { XMLName xml.Name `xml:"objects"` Objects []Object `xml:"object"` } type Object struct { ID int `xml:"id"` Name string `xml:"name"` Email string `xml:"email"` Avatar string `xml:"avatar"` CreatedAt string `xml:"created-at"` UpdatedAt string `xml:"updated-at"` } func main() { httpClient := NewHTTPClient() var response Response err := httpClient.GetXML("https://thetestrequest.com/authors.xml", &response) if err != nil { fmt.Println("Error:", err) return } for _, obj := range response.Objects { fmt.Printf("ID: %d, Name: %s, Email: %s, Avatar: %s, CreatedAt: %s, UpdatedAt: %s\n", obj.ID, obj.Name, obj.Email, obj.Avatar, obj.CreatedAt, obj.UpdatedAt) } } In client.go, we’ll define a new function for a GET request, in XML: func (c *HTTPClient) GetXML(url string, v any) error { req, err := http.NewRequest("GET", url, nil) if err != nil { return fmt.Errorf("error creating GET request: %w", err) } resp, err := c.Client.Do(req) if err != nil { return fmt.Errorf("error making GET request: %w", err) } defer resp.Body.Close() if resp.StatusCode != http.StatusOK { return fmt.Errorf("received non-200 response code: %d", resp.StatusCode) } body, err := io.ReadAll(resp.Body) if err != nil { return fmt.Errorf("error reading response body: %w", err) } err = xml.Unmarshal(body, v) if err != nil { return fmt.Errorf("error unmarshalling XML response: %w", err) } return nil } In this example, we: Read the response body. Convert the XML response into our predefined structure. Print the formatted data to the console for better readability. After running the code, you will see the following output: To learn more about JSON and XML, their key differences, and best use cases, check out our article: "JSON vs. XML: Comparing Popular Data Exchange Formats." Handling HTTP Errors and Logging Proper error handling is a critical part of integrating with an API. Let's break it down into three key failure points: Request Sending Errors — Occur due to network issues, incorrect URLs, or an unreachable server. Response Reading Errors — Even a successful 200 OK status does not always guarantee valid data. Data Conversion Errors — A common issue when working with JSON/XML responses. Proper error handling is important as it prevents application crashes and simplifies debugging when something goes wrong with API communication. We will implement error logging using the following code: package main import ( "fmt" "log" "os" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } func main() { if err := run(); err != nil { log.Printf("Error: %v", err) os.Exit(1) } } func run() error { client := NewHTTPClient() post, err := client.GetBlogPost(1) if err != nil { return fmt.Errorf("error occurred while getting post: %w", err) } fmt.Printf("ID: %d\nUser ID: %d\nTitle: %s\nBody: %s\n", post.ID, post.UserID, post.Title, post.Body) return nil } In this example, we use the log package to log errors. The log.Errorf function outputs an error message. The result of the code execution will remain the same as before since there will be no errors in the requests, but you can try changing variables to see error messages. Automating HTTP Requests In this chapter, we will explore the possibility of automating the sending of multiple HTTP requests. We will look at different approaches, including using loops, utilizing goroutines for parallel requests, and asynchronous handling of requests and responses. Using Loops to Send Multiple Requests To send multiple HTTP requests, we can use loops: package main import ( "fmt" "log" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } func main() { client := NewHTTPClient() for i := 1; i <= 5; i++ { post, err := client.GetBlogPost(i) if err != nil { log.Printf("Error getting post %d: %v", i, err) continue } fmt.Printf("Request to post %d returned:\nID: %d \n%s \n\n", i, post.ID, post.Title) } } We use the for loop to send requests to different URLs. Then, we print the requests with the number, PostID, and title to the console. After execution, you will receive the following message: Using Goroutines for Parallel HTTP Requests Go provides built-in capabilities for parallel task execution through goroutines. This allows sending multiple requests simultaneously, significantly speeding up the program's execution. package main import ( "fmt" "log" "sync" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } // fetchPost handles fetching a post using the GetBlogPost method and outputs the result. func fetchPost(client *HTTPClient, postID int, wg *sync.WaitGroup) { defer wg.Done() post, err := client.GetBlogPost(postID) if err != nil { log.Printf("Error getting post %d: %v", postID, err) return } fmt.Printf("Request to post %d returned:\nID: %d\nUser ID: %d\nTitle: %s\nBody: %s\n\n", postID, post.ID, post.UserID, post.Title, post.Body) } func main() { client := NewHTTPClient() var wg sync.WaitGroup postIDs := []int{1, 2, 3, 4, 5} for _, postID := range postIDs { wg.Add(1) go fetchPost(client, postID, &wg) } wg.Wait() } In this example, we create the fetchPost function, which sends a request and prints the status.  sync.WaitGroup is used to wait for the completion of all goroutines. Run this code and compare the execution speed with the previous solution. The script output may vary due to its asynchronous nature. Example of Asynchronous Request and Response Handling Asynchronous processing allows sending requests and processing responses as they arrive. Let's look at an example using a channel to transmit results: package main import ( "fmt" "log" "sync" ) type Post struct { UserID int `json:"userId"` ID int `json:"id"` Title string `json:"title"` Body string `json:"body"` } type Result struct { PostID int Post *Post Err error } // fetchPost handles fetching a post through the GetBlogPost method and sends the result to the channel. func fetchPost(client *HTTPClient, postID int, results chan<- Result, wg *sync.WaitGroup) { defer wg.Done() post, err := client.GetBlogPost(postID) results <- Result{PostID: postID, Post: post, Err: err} } func main() { client := NewHTTPClient() var wg sync.WaitGroup postIDs := []int{1, 2, 3, 4, 5} results := make(chan Result, len(postIDs)) // Launch goroutines for parallel request execution for _, postID := range postIDs { wg.Add(1) go fetchPost(client, postID, results, &wg) } // Function to close the channel after all goroutines finish go func() { wg.Wait() close(results) }() // Process results as they arrive for result := range results { if result.Err != nil { log.Printf("Error fetching post %d: %v\n", result.PostID, result.Err) continue } fmt.Printf("Request to post %d returned:\nID: %d\nUser ID: %d\nTitle: %s\nBody: %s\n\n", result.PostID, result.Post.ID, result.Post.UserID, result.Post.Title, result.Post.Body) } } In this example, we introduce a new Result structure to store requests' results and use the results channel to pass results from goroutines to the main function. At first glance, the last two approaches might seem very similar, and they are to some extent, but there are still differences: Result Handling: In the asynchronous approach with channels, results are processed in the main thread as they arrive, while in the approach without channels, results are processed within the goroutines. Synchronization: Channels provide built-in mechanisms for safely passing data between goroutines, whereas in the approach without channels, sync.WaitGroup is needed. Resource Usage: Asynchronous processing with channels may handle resources more effectively. In the first approach, all tasks are executed in parallel, but the results may be processed less efficiently. Due to the asynchronous nature, results are processed as they arrive from the channel, meaning the order of posts may not always be the same when rerunning the code. One possible output is shown below: Advanced Features and Tips The guide above is enough to write your first HTTP client. However, if you plan to advance in this area, you will be interested in exploring advanced features and best practices for development. This chapter includes the use of third-party libraries, debugging and optimization techniques, as well as security considerations. Using Third-Party Libraries for Working with APIs The Go standard library provides basic functionality for working with HTTP requests, but sometimes it's more convenient to use third-party libraries that offer advanced features and simplify the process. One such library is go-resty. To install the library, use the following command: go get -u github.com/go-resty/resty/v2 Some of the advantages of go-resty include: Automatic serialization (the process of converting data structures) and deserialization. Session management (cookie support) and retries for failed requests. Asynchronous requests. Flexible configuration of timeouts, headers, parameters, and other options. Built-in debugging features, including logging. Testing tools such as mocking. Here is an example for sending GET and POST requests using the go-resty library: package main import ( "fmt" "log" "github.com/go-resty/resty/v2" ) func main() { client := resty.New() // GET request resp, err := client.R(). SetQueryParam("userId", "1"). Get("https://jsonplaceholder.typicode.com/posts") if err != nil { log.Fatalf("Error on GET request: %v", err) } fmt.Println("GET Response Info:") fmt.Println("Status Code:", resp.StatusCode()) fmt.Println("Body:", resp.String()) // POST request post := map[string]any{ "userId": 1, "title": "foo", "body": "bar", } resp, err = client.R(). SetHeader("Content-Type", "application/json"). SetBody(post). Post("https://jsonplaceholder.typicode.com/posts") if err != nil { log.Fatalf("Error on POST request: %v", err) } fmt.Println("POST Response Info:") fmt.Println("Status Code:", resp.StatusCode()) fmt.Println("Body:", resp.String()) } The library significantly simplifies working with HTTP requests and provides many useful features. Debugging and optimization are crucial aspects of development, so let's look at some examples. Logging Requests and Responses For debugging purposes, it's helpful to log requests and responses. We can do this using the library we installed earlier: client := resty.New(). SetDebug(true) Also, use http.Transport to manage the number of open connections: client := resty.New() transport := &http.Transport{ MaxIdleConns: 10, IdleConnTimeout: 30 * time.Second, DisableKeepAlives: false, } client.SetTransport(transport) client.SetTimeout(10 * time.Second) Best Practices for Developing Secure and Reliable HTTP Clients An example of a secure and reliable HTTP client using go-resty: Error Handling: Resty automatically handles errors, simplifying response checks. Using TLS: Resty supports custom transport settings to enable TLS. Secure methods for storing and transmitting authentication tokens: package main import ( "crypto/tls" "fmt" "log" "net/http" "github.com/go-resty/resty/v2" ) func main() { // Create client with configured TLS client := resty.New() // Configure security transport layer client.SetTransport(&http.Transport{ // Using standard TLS configuration TLSClientConfig: &tls.Config{ // Additional configuration parameters can be set here MinVersion: tls.VersionTLS12, // Example: minimum TLS version 1.2 }, }) token := "your_auth_token_here" // Sending GET request with error handling and TLS verification resp, err := client.R(). SetHeader("Authorization", "Bearer "+token). Get("https://jsonplaceholder.typicode.com/posts/1") if err != nil { log.Fatalf("Error: %v", err) } if resp.StatusCode() != http.StatusOK { log.Fatalf("Non-200 response: %d", resp.StatusCode()) } // Handle response body fmt.Printf("Response: %s\n", resp.String()) } Using the SetHeader method to set the "Authorization" header with a bearer token is a standard and secure practice, provided other security aspects are followed: Proper and secure storage of tokens. On the client side, this could be a secure container protected from unauthorized access. Transmitting tokens through secure channels, such as HTTPS. Minimizing token lifespan and regularly updating tokens. Using time-limited tokens and periodic rotation increases overall security. Additional recommendations for reliable HTTP clients: Timeouts: client.SetTimeout(15 * time.Second) Retries: client.R().SetRetryCount(3).Get("https://jsonplaceholder.typicode.com/posts/1") Logging Requests and Responses: client.SetDebug(true) Using go-resty significantly simplifies the process of creating an HTTP client in Go. The library provides extensive capabilities and features for flexible configuration according to your needs. Additionally, go-resty allows you to handle more complex requests, such as file uploads, multipart forms, or custom requests, and it automatically manages headers with minimal code and effort. Conclusion Developing HTTP clients in Go is an essential skill for any developer working with web services and APIs. In this article, we covered all key aspects of creating an HTTP client, from the basics to the advanced features of the language.  For further study and a deeper understanding of the topic, we recommend the following resources: Official Go documentation net/http package documentation GitHub repository for go-resty
13 March 2025 · 27 min to read
Microservices

Designing the Ideal Microservice: Part 2

This is the second part of the article "Designing the Ideal Microservice." In the first part, we discussed technical aspects related to design principles, communication between microservices, and key patterns. In this part, we will explore security, automation, and monitoring and discuss the future of microservices and emerging trends. Security and Reliability of Microservices Security and reliability are critical aspects of microservices design. In this chapter, we examine the main approaches and best practices that help secure microservices against threats and enhance their resilience to failures. Authentication and Authorization To ensure microservices security, you should implement reliable authentication and authorization mechanisms: OAuth2 and OpenID Connect: Standards for authentication and authorization that provide secure access to resources. Advantages: Support for various scenarios, widespread use, and strong library and framework support. Disadvantages: Complexity in configuration and token management. JSON Web Tokens (JWT): A token format used for transferring information between parties as JSON. Advantages: Compact, flexible data inclusion and broad support. Disadvantages: Limited token lifespan and the need for secure storage and transmission. API Gateway: A centralized node through which all requests to microservices pass, allowing it to handle authentication and authorization. Advantages: Centralized security management, reducing complexity for microservices. Disadvantages: Potential bottleneck, complex configuration, and management. Data Encryption Data encryption is crucial for protecting information in a microservices architecture. Data Encryption in Transit: Using HTTPS and TLS protocols to secure data transmission between microservices. Advantages: Protection against interception and man-in-the-middle attacks. Disadvantages: Slightly increased latency and resource consumption. Data Encryption at Rest: Encrypting data stored in databases and other storage solutions. Advantages: Protection against unauthorized access in case of storage compromise. Disadvantages: Increased complexity in key management and cryptographic operations. Threat Detection and Prevention To protect microservices from internal and external threats, monitoring and incident response mechanisms are essential: Web Application Firewalls (WAF): Filters and monitors HTTP requests to web applications. Advantages: Protection against threats like SQL injection and XSS. Disadvantages: Potential false positives and complex configuration. Intrusion Detection and Prevention Systems (IDS/IPS): Monitor and analyze traffic to detect and prevent suspicious activity. Advantages: Anomaly and attack detection, protecting networks and applications. Disadvantages: High resource requirements and constant tuning and updating needs. Reliability and Fault Tolerance Ensuring reliability and fault tolerance is critical for the successful operation of microservices. Here are the main approaches and patterns: Circuit Breaker Pattern: Protects against cascading failures by blocking calls to an unstable microservice. Advantages: Increases system resilience and prevents overloads. Disadvantages: Complex state management and the need for monitoring. Retry Pattern: Repeats failed requests after a certain interval. Advantages: Improves reliability by automatically recovering from temporary failures. Disadvantages: Can increase system load and requires careful timeout and interval management. Bulkhead Pattern: Isolates system components to prevent cascading failures. Advantages: Fault isolation and improved system resilience. Disadvantages: Increased management complexity and potential resource duplication. Service Mesh: An infrastructure layer that manages communication between microservices, including load balancing, routing, and monitoring. Advantages: Improved observability, automated communication management, enhanced security. Disadvantages: Complex configuration and maintenance, and additional resource requirements. Monitoring and Logging Monitoring and logging enable timely issue detection and response, as well as performance analysis and microservices behavior insights. Centralized Logging: Collects and stores logs from all microservices in one place. Advantages: Simplifies error analysis and event correlation. Disadvantages: Requires effective log volume and logging system performance management. Metrics Monitoring: Collects and analyzes performance and health metrics of microservices. Advantages: Early issue detection and resolution, performance improvement. Disadvantages: Requires setup and management of the monitoring system. Alerting: Configures notifications for rapid incident and anomaly response. Advantages: Quick issue detection and resolution, minimizing downtime. Disadvantages: Potential for false alarms and the need for precise threshold configuration. Ensuring the security and reliability of microservices requires a comprehensive approach, including authentication and authorization, data encryption, threat detection and prevention, and fault tolerance patterns and monitoring. Testing and Debugging Microservices Testing and debugging microservices are crucial aspects of their successful development and operation. Microservice architecture offers numerous advantages, such as modularity and component independence, but also presents challenges in testing and debugging due to the distributed nature of the system. In this chapter, we will explore the main approaches and tools for testing and debugging microservices. Types of Testing Effective microservice testing requires various types of tests to validate functionality, performance, and reliability. Unit Testing: Testing individual modules or functions of a microservice in isolation from other components. Advantages: Fast execution, ease of debugging, early detection of errors during development. Disadvantages: Limited verification of interactions between components. Integration Testing: Testing the interaction between microservices and other system components. Advantages: Detection of integration issues and verification of interaction correctness. Disadvantages: More complex and time-consuming execution, requiring test environment setup. Contract Testing: Testing microservice APIs against predefined contracts between services. Advantages: Ensures consistency between services and prevents issues during updates. Disadvantages: Requires prior contract definition and can face discrepancies with changes. End-to-End (E2E) Testing: Testing the entire system, including all microservices and external components. Advantages: Comprehensive system validation and detection of complex bugs. Disadvantages: Long test execution times and complex test environment setup and maintenance. Load Testing: Testing system performance under high load conditions. Advantages: Identifies bottlenecks and verifies resilience under high loads. Disadvantages: Requires significant resources and complex result analysis. Tools for Testing Microservices A wide range of tools is used for testing microservices, each addressing specific tasks. JUnit: A framework for unit testing in Java. Advantages: Widely used with integration support for various tools. Disadvantages: Limited to Java usage. Mockito: A library for creating mock objects in tests. Advantages: Easy to use with support for different testing frameworks. Disadvantages: Limited to Java usage. Postman: A tool for API testing that allows sending requests and validating responses. Advantages: User-friendly interface and automation support. Disadvantages: Primarily for API testing with limited integration testing capabilities. JMeter: A tool for load testing. Advantages: Extensive testing scenario creation and protocol support. Disadvantages: Complex setup and result analysis. Docker: A platform for application containerization used to create test environments. Advantages: Easy creation of isolated environments and CI/CD support. Disadvantages: Requires learning and configuring Docker. Debugging Microservices Debugging microservices can be challenging due to their distributed nature and multiple component interactions. Here are the main approaches and tools for debugging microservices: Logging: Recording events and errors in logs for later analysis. Advantages: Detailed analysis of microservices behavior and error detection. Disadvantages: Log volume management and complex data analysis. Distributed Tracing: Tracking requests across all microservices involved in processing. Advantages: Visibility of the entire request processing chain. Disadvantages: Complex setup and integration, requiring additional monitoring. Profiling Tools: Analyzing microservice performance, identifying bottlenecks, and optimizing code. Advantages: Detailed performance analysis and optimization potential. Disadvantages: High resource requirements and extensive data analysis. Using API Gateway Services: Monitoring and managing traffic between microservices via API Gateway. Advantages: Centralized management and monitoring with traffic analysis. Disadvantages: Potential bottleneck and complex setup and maintenance. CI/CD and Test Automation Test automation and CI/CD (Continuous Integration/Continuous Deployment) processes increase the efficiency of microservice development and deployment. Jenkins: A tool for CI/CD automation. Advantages: Extensive configuration options and plugin support. Disadvantages: Complex setup and management, requiring additional resources. GitLab CI/CD: An automation tool integrated with the GitLab version control system. Advantages: GitLab integration, easy setup, and powerful automation features. Disadvantages: Limited usage with other version control systems. Travis CI: A cloud service for CI/CD automation. Advantages: Easy to use with GitHub integration. Disadvantages: Limited free builds and dependency on cloud infrastructure. CircleCI: A cloud service for CI/CD automation. Advantages: Fast build execution and integration with various version control systems. Disadvantages: Limitations in the free version and cloud infrastructure dependency. Testing and debugging microservices require careful preparation and the use of different approaches and tools. Test automation and the implementation of CI/CD processes enhance microservice quality and reliability while reducing development and deployment time. In the next chapter, we will explore the management and monitoring aspects of microservices, which also play a key role in their successful operation. Management and Monitoring of Microservices Managing and monitoring microservices are essential aspects that ensure their stable operation. In this chapter, we will take a closer look at the key approaches and tools for managing and monitoring microservices, which help maintain high performance and reliability of the system. Managing Microservices Managing microservices requires various methodologies and tools to automate and optimize deployment, configuration, and administration processes. Container Orchestration Kubernetes: A platform for automating deployment, scaling, and managing containerized applications. Advantages: Automation of container management, high scalability, and support for various infrastructures. Disadvantages: Complex setup and management with a steep learning curve. Docker Swarm: A container orchestration tool integrated with Docker. Advantages: Ease of setup and integration with Docker. Disadvantages: Limited functionality compared to Kubernetes and lower popularity. Configuration Management Systems Ansible: A tool for automating configuration management and application deployment. Advantages: Ease of use and flexibility for various automation tasks. Disadvantages: Limited capabilities for complex orchestration scenarios. Chef: A platform for automating configuration management, deployment, and infrastructure management. Advantages: Extensive automation capabilities and support for complex scenarios. Disadvantages: Complex setup and usage with a steep learning curve. Puppet: A tool for configuration management and infrastructure automation. Advantages: High scalability and support for various platforms. Disadvantages: Complex setup and administration requiring specialized training. Monitoring Microservices Monitoring microservices is crucial for tracking their status, performance, and identifying issues at early stages. Here are the main tools and approaches for monitoring microservices. Monitoring Systems Prometheus: A monitoring and alerting system with time-series support. Advantages: High performance, flexible configuration, and integration with Kubernetes. Disadvantages: Complex setup and management, requiring additional components for full functionality (e.g., Grafana for visualization). Grafana: A platform for metrics visualization and monitoring. Advantages: Advanced visualization options and integration with various data sources. Disadvantages: Requires configuration and integration with monitoring systems. ELK Stack (Elasticsearch, Logstash, Kibana): A suite of tools for collecting, analyzing, and visualizing logs and metrics. Advantages: Comprehensive data analysis and visualization, supporting scalability. Disadvantages: Complex setup and management with high resource requirements. Incident Detection and Response PagerDuty: A platform for incident management and alerting. Advantages: Efficient incident management and integration with various monitoring systems. Disadvantages: High cost with the need for configuration and integration. Opsgenie: A service for incident management and alerting. Advantages: Flexible alert configuration and integration with multiple monitoring tools. Disadvantages: Requires training and configuration. Distributed Tracing Jaeger: A distributed tracing platform designed to track requests across microservices. Advantages: In-depth performance analysis and microservices dependency mapping, integrating Kubernetes and other tools. Disadvantages: Complex setup and management requiring specialized training. Zipkin: A distributed tracing tool for collecting latency data in distributed systems. Advantages: Easy integration and support for various programming languages. Disadvantages: Limited functionality compared to Jaeger with a need for configuration and management. Metrics and Alerting Effective microservices monitoring requires defining and tracking key metrics and configuring alerting for quick incident response. Performance Metrics CPU and Memory Usage: Resource consumption by microservices. Response Time: Latency between request and response. Throughput: Number of processed requests per unit time. Reliability Metrics Uptime: The time during which the microservice is available. Failure Rate: Number of microservice failures over a specified period. Mean Time to Recovery (MTTR): Average time required to restore a microservice after failure. Alert Configuration Threshold Values: Setting thresholds for key metrics that trigger alerts when exceeded. Notification Integration: Setting up notifications via email, SMS, messaging apps, or incident management systems. Alert Management: Grouping and prioritizing alerts to minimize false positives and ensure rapid response to critical incidents. Configuration and Secrets Management Managing microservice configurations and secrets requires secure and flexible tools and approaches. HashiCorp Vault: A tool for secrets management and data protection. Advantages: High security and support for various types of secrets. Disadvantages: Complex setup and management requiring specialized training. Kubernetes Secrets: A mechanism for managing secrets in Kubernetes. Advantages: Seamless integration with Kubernetes and ease of use. Disadvantages: Limited functionality compared to specialized tools. Configuration Files and Management Systems: Using configuration files and management systems for centralized microservices configuration. Advantages: Flexibility and scalability with compatibility with various tools. Disadvantages: Requires versioning and security management for configuration files. Effective management and monitoring of microservices are key to their successful operation. Organizations can leverage modern tools and approaches to maintain high performance and reliability, respond promptly to incidents, and optimize configuration and secrets management. Future Trends and Development of Microservice Architecture Microservice architecture continues to evolve, offering new opportunities and addressing emerging challenges. In this chapter, we will explore the key trends and future prospects of microservices that will help you stay at the forefront of technology and adapt to changing business requirements. Serverless and Functions as a Service (FaaS) Serverless and FaaS represent the next step in the evolution of microservice architecture, offering greater abstraction from infrastructure and simplifying development. Serverless: A computing model where developers write code and cloud providers manage servers and infrastructure. Advantages: Reduced infrastructure management costs, on-demand scalability, and pay-as-you-go pricing. Disadvantages: Execution time limitations for functions and dependency on cloud providers. FaaS: A subset of serverless, where code is deployed as individual functions that run in response to events. Advantages: Rapid deployment, automatic scaling, and high flexibility. Disadvantages: Limited capabilities for complex tasks and potential issues with cold starts. Multi-Cloud and Hybrid Architectures Multi-cloud and hybrid architectures are becoming increasingly popular as companies seek to avoid vendor lock-in and leverage the benefits of different cloud platforms. Multi-Cloud Strategies: Using multiple cloud providers to host different parts of the infrastructure. Advantages: Resilience to outages from a single provider, cost optimization, and flexibility in choosing the best services. Disadvantages: Complexity in management and integration, and the need for compatibility compliance. Hybrid Architectures: Integrating on-premises infrastructure with public cloud services. Advantages: Resource management flexibility, enhanced security, and compliance with regulatory requirements. Disadvantages: Complex integration requiring additional management tools. Service Mesh Service Mesh is becoming an essential part of the microservice infrastructure, providing traffic management, security, and observability at the network interaction level. Istio: One of the most popular platforms for building service meshes. Advantages: Flexible configuration, comprehensive monitoring and security features, and integration with Kubernetes. Disadvantages: Complex setup and management with high resource requirements. Linkerd: A lightweight alternative to Istio for building service meshes. Advantages: Ease of use, high performance, and Kubernetes integration. Disadvantages: Fewer features compared to Istio and less configuration flexibility. DevOps and GitOps DevOps and GitOps continue to evolve, offering new approaches to managing and automating microservice development and operations processes. DevOps: A professional approach to software development that brings together developers and operations teams for improved collaboration and process automation. Advantages: Enhanced development quality and speed, reduced time-to-market, and CI/CD automation. Disadvantages: Requires changes in organizational culture and training in new tools. GitOps: An approach to managing infrastructure and applications using Git as the single source of truth. Advantages: Increased transparency and repeatability, simplified configuration management, and automated deployments. Disadvantages: Dependency on Git and the need to configure and manage GitOps tools. Artificial Intelligence and Machine Learning (AI/ML) Integrating AI/ML into microservice architecture opens up new opportunities for automation and process optimization. AI/ML for Monitoring and Failure Prediction: Using machine learning algorithms to analyze metrics and logs to predict failures and automate responses. Advantages: Increased system resilience and proactive incident management. Disadvantages: Complex model setup and the need for large datasets for training. Automated CI/CD with AI/ML: Applying AI/ML to optimize deployment and testing processes. Advantages: Faster development cycles, improved testing quality, and automated error detection and correction. Disadvantages: Complex integration and the need for specialized expertise. Future of Microservice Architecture The future of microservice architecture is tied to continuous evolution and adaptation to new challenges and requirements. Implementing new technologies, improving automation, and integrating with various platforms and services will enable the creation of more flexible, scalable, and reliable systems capable of efficiently solving modern business challenges. Conclusion Designing the perfect microservice is a multifaceted process that requires a careful approach to every aspect of development, deployment, and operation. Throughout this article, we have explored the key principles and practices that help build high-quality, flexible, and scalable microservices. For successful design and operation of microservices, consider the following recommendations: Focus on Business Value: Develop microservices that clearly align with business objectives and help achieve specific outcomes. Invest in Automation: Automating deployment, testing, and monitoring processes significantly reduces time and costs for microservice management. Ensure Security: Data security and secure communication between microservices should be prioritized throughout development and operation. Continuous Learning and Adaptation: Technologies and practices are constantly evolving. Stay updated with new trends and apply them to enhance your architecture. Maintain Documentation: Good documentation simplifies maintenance, improves team collaboration, and enhances the quality of the final project.
19 February 2025 · 18 min to read

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