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How to Analyze Data with Metabase? A Comparison To 5 Most Popular Analytical Services

How to Analyze Data with Metabase? A Comparison To 5 Most Popular Analytical Services
Hostman Team
Technical writer
Infrastructure

What is Metabase? How to connect it to your database and use it for your analytics? What are the most popular Metabase alternatives and how do they compare? Read this article to find out everything about Metabase.

What is Metabase and how does it work?

Without the right tools, a database can be as impenetrable as a medieval dungeon crawling with carnivorous spiders.

But fear not, brave adventurer — Metabase is here to help you find and unlock all of the riches in your database.

With its intuitive UI, Metabase is your master key to accessing, presenting, and analyzing everything that lives inside your databases. Metabase is the open-source application that unlocks the full potential of your databases, allowing you to access, search, and share data in the easiest way possible. 

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It is like having an intelligent, proactive and efficient digital analyst who’s always on the alert, and who can help you process and retrieve any of your data quickly and painlessly.

The simple and intuitive UI makes it possible to query even the tiniest piece of data in your database. More importantly, it presents the information in a clear and understandable way, so that you and your team can get the full benefit from the results of your query.

What makes Metabase such a popular tool?

I.T. professionals are known for their logical and analytical thinking. So when they get excited about something like Metabase, you can bet they have solid arguments to back it up.

Here are just a handful of features that have made Metabase the tool of choice for so many I.T. professionals:

  • Advanced query system that is equally effective with generic searches and laser-targeted database interrogations. Accessing data is as simple as asking a question about anything in your database. The Metabase query builder will serve up information you need in a way that is easy to digest for both analysts and non-technical users.

  • One-time-setup automated report generation. Metabase will automatically create reports about data changes in your database. Set it and forget it.

  • Intelligent tracking of important data changes with alerts. Set up alerts to keep owners up to date on changes in key data for which they are responsible.

  • Charts and dashboards that are as useful as they are visually appealing. With a strong focus on UI and UX, Metabase excels at presenting data and changes in a style that is clear and immediately understandable.

  • Craft dedicated embedded analytics. Metabase can also be used very effectively as a full-fledged data collector and manager for your clients.

How to set up Metabase

Before you can start working with Metabase, you need to follow a simple deployment and setup procedure.

Here’s everything you need to know.

Deploying

There are many ways to launch Metabase on your production platform.

The simplest way is to use cloud services that automated all the processes. All you need to do is to sign up to the service, select Metabase, and it will create an instance of the application on a fast and reliable server. Next, you will need to configure Metabase after the deployment is over.

Another way to install Metabase is to use the dedicated JAR file:

  1. Download the file from the official Metabase website

  2. Run the following command: java -jar metabase.jar

Alternatively, you can use the Docker image of Metabase If you’re used to working with containers.

There are also other methods for running Metabase. You can find them in the official Metabase documentation.

Setting up

Once you’ve set up Metabase on your server, you’ll be able to access it via localhost:3000.

Just open that address in your browser to begin.

Metabase will ask you to create an admin account. You’ll need to insert the standard personal details — name, last name, email, password, etc.

The next step is to connect your database. To do so, you’ll have to specify:

  • the hostname of the server with the database

  • the port to connect to the database

  • the database name

  • the username and password for accessing the database.

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And, that’s it. Once you’ve connected your database, you can check out the Metabase interface and start exploring all of its exciting functionality.

How to ask Metabase questions

Asking Questions is a key element of the Metabase system. It is like “queries on jet fuel” for your database software.

As an analyst, formulating the right Metabase Questions will be one of your main activities. They are the tool that will help you extract all of the important insights from the data you’re inspecting. While Metabase Questions are extremely powerful, creating them is an incredibly simple and intuitive process.

Let’s say you have a table with order data. It contains columns for subtotal, tax, total, etc., and you want to find all the orders with a tax of more than 5 dollars.

Using the filter system, you can ask Metabase to check the orders table for how many rows there are with a tax exceeding 5 dollars. To do this, you click on the Filter button, choose a column, choose the criteria to filter, and then click “Add filter”. Next, you might want to use the “Summarize” option to add up all of the rows with a tax of more than 5 dollars.

Filters in Metabase allow you to pick out the necessary data and get direct answers to your questions.

To help you get the most out of Metabase, we’ve prepared for you an in-depth Metabase query syntax tutorial.

How to visualize data

Presenting your data in a way that is appealing and easy to digest, is one of the key features of Metabase. All of the numbers, columns, rows, and cells are organized in a logical manner to facilitate understanding and data-driven decision-making.

Both visualization tools in Metabase are optimized for analyzing and monitoring any volume of data.

Creating charts

Metabase Charts depend heavily on the questions you ask. You can use built-in query editors to visualize data as charts.

To create a chart, you need to choose the “Visualize” option. Next, you’ll be able to choose one of the chart views that will present the data it gets from the question you ask. Finally, you will need to formulate the question.

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Let’s say you have an orders table with various categories of goods that your company sells. You can ask Metabase to filter some categories, summarize their performance characteristics and Visualize as a histogram.

Most importantly, you’ll be able to drill deeper into the data presented in your chart. You can click through to find exactly the number you need, and zoom in to get closer to the information around a certain period of time, or vice versa.

Creating dashboards

Business intelligence dashboards help you monitor the outcome of your actions so that you can make informed decisions about the further development of your company or product.

Dashboards are visually similar to charts. However, instead of focusing on a few specific elements, dashboards allow you to present an array of different types of information in different visual forms on one screen. This approach helps to monitor sensitive performance indicators on one screen. Metabase dashboard filters will help out on this task. And all the data in a dashboard will always be up to date.

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In Metabase, you can find many ready-made dashboards for efficiently presenting different data collections. These dashboards are made by other Metabase users. And since they’re based on real-world scenarios, you’re likely to find something that closely fits your use case in no time.

Metabase API

There are many platforms out there that are great at what they do, but fail miserably when it comes to integrating with your environment.

That’s why Metabase comes with its own API for integrating its features into other products.

The API allows you to ask for any data that is passed through Metabase via a different application. You can also create custom queries and pass them into Metabase by means of the API.

Moreover, developers can use curl requests to set users, groups, and permissions; even generate reports.

You’ll find a ton of API use cases in the official Metabase documentation.

How does Metabase compare with similar top industry solutions?

Metabase is a great tool but it’s neither the first nor the only one of its kind.

There are many other business intelligence tools that help businesses collect and analyze data. But Metabase isn’t afraid of competition. In fact, in the next section, we’re putting Metabase toe-to-toe with some of the best, most powerful and most popular data analysis platforms.

Punches will fly, but you’ll find that Metabase puts up a strong show of force.

Metabase vs Tableau

These two platforms have a lot in common. Both were created for the purpose of presenting a large amount of data via the most visually comprehensive tools.

Tableau launched in 2003. By 2021 it had earned the trust and admiration of many businesses.

By comparison, Metabase is a relatively recent addition to the scene. While it doesn’t have the huge exposure and reputation that Tableau has built over the years, Metabase has the advantage of having been built on the lessons learned from other platforms (including Tableau).

You could say Metabase stands on the shoulders of giants, but reaches higher because of that.

Metabase vs Superset

Superset is a free alternative to Metabase. It is a quite popular tool made by developers of Airbnb and now belongs to Apache. It is open source too and in many cases functionally similar to Metabase.

People love Superset due to its easy migrating system. If you’re migrating to Superset, the process is painless and straightforward.

Superset users are particularly fond of a feature called “Time Dimensions”, which allows you to monitor data from several time segments without having to update the whole dashboard at the same time.

While it’s a brilliant tool, Superset suffers in the documentation department. This becomes a real problem when dealing with some of the more advanced or obscure functionality.

On the flip side, Metabase boasts clear and detailed documentation. More importantly, we’ve placed huge emphasis on UI/UX, to the extent that most functions can be performed without spending too much time digging through documentation. Metabase’s easy query system and intuitive charts and dashboards have won over many users from Superset, simply because they were tired of all the guesswork.

Metabase vs Redash

One of Redash's main claims to fame is that it supports JSON files as a data source. In other words, it can be connected to NoSQL databases like MongoDB, which many users consider an asset.

Metabase and Redash also have a number of useful features in common, such as the “Query Snippet” function, which helps to create reusable bits of SQL queries to quickly recreate requests to the database.

In Redash it is easy to set up query parameters. Therefore, it is simpler to pass the arguments and data sources into SQL and NoSQL requests.

Unfortunately, Redash falls short when it comes to the visual element of the applications. In a side-by-side comparison, you’ll see that Metabase’s charts and dashboards are much better presented and more informative (hence, more useful) than the ones that Redash provides.

Metabase vs Looker

True to its name, Looker is a very well-presented tool that is loved by thousands of users. Its main focus is data modeling and it is actually good at it.

Metabase is also very good at data modeling. In fact, Looker and Metabase have a lot of strong points in common. Where Metabase outclasses Looker, is in performance. Put the two head-to-head and you’ll find Metabase much faster and more comfortable to use.

Many Looker users love it because of its LookML language — a proprietary syntax that is used to pass queries to databases. It has quite a steep learning curve, but many businesses consider it to be the most powerful and efficient way to work with a large amount of information. Unfortunately, it’s also pretty expensive.

By comparison, Metabase is free as long as you host it yourself, and still brings very powerful features bundled with a well-designed UI/UX.

Metabase vs Power BI

Power BI is Microsoft's business intelligence tool, created for those who primarily work within Microsoft’s ecosystem.

It is a feature-rich and massive product, but its power comes with an equally steep learning curve. As a result, the product is very hard to penetrate, which means that most users will rarely be able to get the full benefit of its powerful features. Just getting Power BI up and running is a mammoth of a task, requiring a considerable investment in time, effort and money to get it to work efficiently.

Just like many other Microsoft products, Power BI has its niche of users for whom it's an excellent fit. But it’s definitely not for everyone.

On the flip side, Metabase was designed with a very low barrier to entry. The intuitive UI makes it easy to deploy and start using within minutes. And of course, it’s not lacking in powerful features either.

The best way to try out Metabase

Metabase is a powerful tool that will dramatically change the way you work with databases. But you shouldn’t take our word for it. That’s why we recommend that you try out Metabase for yourself and come to your own conclusions.

How do you do that?

With Hostman.

As part of its suite of hosting services, Hostman has just launched a Marketplace where administrators and developers can find a variety of tools such as OpenVPN, Docker, Metabase and many more, which can be deployed in one click.

All you have to do is:

  1. Visit the Metabase page in the Hostman Marketplace.

  2. Click “Deploy to Hostman”.

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Nothing else is necessary.

You won’t need to download Java and JAR files, or create Docker containers. Everything will be set up for you. 

The Hostman Marketplace also carries loads of other exceptional tools that you can easily deploy and use. You can try any of them for free for 7 days. And if you like what you see, you can continue to use it for just 5 dollars per month.

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A simple C# console application looks like this: using System; class Program { static void Main() { Console.Write("May I have your name?: "); string name = Console.ReadLine(); Console.WriteLine($"Welcome, {name}!"); } } C and C++: For Systems Programming C and C++ are compiled programming languages that are closely related. C++ is an extended, object-oriented version of the procedural C language. C was created at Bell Labs by Dennis Ritchie in 1972, while C++ was introduced by Bjarne Stroustrup in 1983. High Performance Unlike Python, JavaScript, and Java, C and C++ do not require an interpreter or a virtual machine. Their code is compiled directly into processor instructions. In other words, these languages are as close to the hardware as possible, allowing low-level control of system resources. That’s also why these languages are considered complex—manual control and lack of automation demand high programmer skill. Memory Management C and C++ give full control over computing resources. They do not include a garbage collector that automatically frees unused memory. This reduces overhead but increases the risk of memory leaks. Due to their performance and control, C and C++ are preferred for high-load computing, like OS kernels (Linux, Windows, macOS, Android), game engines (Unreal Engine), and financial systems. In short, C and C++ remain the go-to languages when speed and efficiency are critical. Foundation for Modern Languages Originally developed for Unix-like OS development, C became the ancestor of many modern languages. Its syntax is the foundation of many popular languages: C++, Java, C#, JavaScript, Go, Swift. Example of simple C++ code using classes: #include <iostream> #include <string> class Car { private: std::string brand; public: Car(std::string carBrand) { brand = carBrand; } void showInfo() { std::cout << "Car brand: " << brand << std::endl; } }; int main() { Car myCar("Toyota"); myCar.showInfo(); return 0; } Swift: For iOS Development Swift is a modern high-level, statically typed language that is compiled into machine instructions. Apple’s Official Language Before Swift, Apple’s main language was Objective-C, dating back to the 1980s. Despite its power, it had outdated principles and lacked support for modern syntax and safe memory handling. In 2014, Apple introduced Swift, a modern, safe, and convenient language aimed at improving code writing, safety, performance, and memory management. In short, Swift was created as Apple’s new official language for iOS, macOS, watchOS, and tvOS development. Simplified Syntax Compared to Objective-C Objective-C: NSString *name = @"John"; NSInteger age = 25; NSArray *fruits = @[@"Apple", @"Banana", @"Orange"]; - (void)greet:(NSString *)name { NSLog(@"Hello, %@", name); } [self greet:@"Alice"]; Swift: let name = "John" var age = 25 let fruits = ["Apple", "Banana", "Orange"] func greet(name: String) { print("Hello, \(name)") } greet(name: "Alice") As a result, Swift has cleaner and more understandable syntax, which means faster development. A Great Choice for Mobile Development Swift is optimized for Apple’s custom chips. It’s the main language for developing native iOS applications and games. Apple actively supports and develops the Swift ecosystem, and it is fully integrated into Xcode, Apple’s official IDE. Go: A Modern Language from Google Go, or Golang, is a high-level, statically typed programming language designed with concurrency in mind. It was developed in 2007 by Google engineers Robert Griesemer, Ken Thompson, and Rob Pike. Google created Go to address speed, concurrency, and development convenience issues found in other languages, like: C++: Powerful but complex Java: Bytecode execution is slower than native Python: Simple but slow due to interpretation As a company focused on cloud services, Google made Go with server-side development in mind. Simplicity and Minimalism Go has automatic garbage collection, a simple syntax, and convenient abstractions, but it's not a classical OOP language. There are no classes, no this keyword, no method/operator overloading. Instead, Go uses structs with methods attached to them: package main import "fmt" type Person struct { Name string Age int } func (p Person) Greet() { fmt.Println("Hi, my name is", p.Name) } func main() { person := Person{Name: "Anna", Age: 35} person.Greet() } Go minimizes complexity and accelerates development by unifying syntax: No classes/inheritance: use structs and interfaces instead. No method/operator overloading: one name per method. No exceptions: use explicit error checks Exceptions are errors thrown by an application during execution that can be caught and handled by user-written code without terminating the program. At first glance, such simplification may seem to limit the programmer’s capabilities. However, in reality, a strict definition of application logic provides greater flexibility in possible implementations and solutions. This is likely why Go, along with Python and JavaScript, is considered one of the best programming languages for beginners. High Performance Go code is compiled to machine instructions. The lack of heavy features like overloading and exceptions makes Go programs high-performing. The garbage collector is optimized for minimal delays. Instead of OS level threads, Go uses goroutines—lightweight threads that use only a few kilobytes of memory and can be spawned in large numbers. Great for Server-Side Development Compiled code, concurrency support, and minimal syntax make Go ideal for backend development. Built-in packages for web servers, networking, databases, and encoding (like net/http, database/sql, encoding/json) allow out-of-the-box server app development. Which Programming Language Should You Choose First? So. When choosing a programming language, several important factors should be taken into account: Development field. In what area will you be working? Web applications, systems programming, game development? And if it’s game development, what kind? Mobile or desktop? Or maybe even console games? Future tasks. The tasks you’ll face will depend on the area of development. But the type of tasks can also vary. For example, web development includes both frontend and backend. In game development, there’s engine development and gameplay mechanics creation. Entry threshold. Depending on personal aptitudes and existing skills, learning a specific programming language will be different for every developer. For instance, effective use of C and C++ requires deeper computer science knowledge: understanding memory management, algorithm complexity, and mathematical computations. The showcased languages can be compared across several key parameters: Language Syntax Complexity Execution Model Type System Year Released Official Site Primary Field Additional Knowledge Python Low Interpretation Dynamic 1991 python.org Data Analysis Machine Learning, Big Data JavaScript Low Interpretation Dynamic 1995 - Web Development Layout, Network Protocols Java Medium Compilation Static 1995 java.com Mobile Development Android, DevOps C# Medium Compilation Static 2002 microsoft.com Desktop Development .NET C/C++ High Compilation Static 1972 / 1985 isocpp.org Systems Programming Mathematics Swift Medium Compilation Static 2014 swift.com Mobile Development macOS, iOS Go Medium Compilation Static 2012 go.dev Servers, Microservices RESTful APIs, Containerization Learning the syntax of a specific language is best done with the help of books.  You can clarify various details through tutorial articles and videos. And when problems arise during learning or work tasks, you can ask questions on platforms like StackOverflow. Ultimately, the choice always comes down to personal preference. In the long run, it’s better to pursue something you are actually interested in rather than just something profitable. Otherwise, you'll just burn out.
14 May 2025 · 16 min to read
Infrastructure

What is Code Review and When Is It Needed?

You can write code. You can edit existing code. You can even rewrite it from scratch. There’s a lot you can do with code. But what’s the point if the code lives in its own echo chamber? If the same person writes, views, and edits it, many critical errors can drift from one version to another unnoticed without external evaluation. Code locked within the confines of a single text editor is highly likely to stagnate, accumulating inefficient constructs and architectural decisions, even if written by an experienced developer. This is why every developer should understand what code review is, how it’s done, and what tools are needed. Presenting your code properly to others, gathering feedback, and making changes wisely is important. Only this way can code remain “fresh” and efficient, and applications based on it — secure and high-performing. Code review is the process of examining code by one or more developers to identify errors, improve quality, and increase readability. Types of Code Review 1. Formal Review A formal review is a strict code-checking process with clearly defined stages. It’s used in critical projects where errors can have serious consequences — for example, in finance or healthcare applications. The analysis covers not just the code but also the architecture, performance, and security. Reviewers often include not just developers but also testers and analysts. For example, a company developing a banking app might follow these steps: Development: A developer completes a new authentication module and submits a pull request via GitHub. Analysis: A review group (2 senior developers + 1 security specialist) is notified and checks the code for logic, readability, and security (e.g., resistance to SQL injection and XSS attacks). Discussion: Reviewers meet the developer over Zoom and give feedback. Documentation: All notes are posted in GitHub comments and tracked in Jira. For instance, some RESTful requests may be flagged as vulnerable with a recommendation to use parameterized queries. Fixes: The developer updates the code and the pull request; the cycle repeats until approval. Approval: Once reviewers are satisfied, the code is merged into the main branch. 2. Informal Review Informal code review is less strict and more flexible, usually involving: Quick code discussions in chat or meetings Showing code to a colleague in person Asking an expert a technical question This kind of review happens often in day-to-day work and is characterized by spontaneity, lack of documentation, informal reviewer choice, and shallow checks. In simpler terms, it’s more like seeking advice than a formal third-party audit. It's a form of knowledge sharing. Types include: Over-the-Shoulder Review: One developer shows their code to another in real time (via screen share, chat message, or simply turning the monitor). Ad-hoc Review: A developer sends code to a colleague asking them to check it when convenient, e.g., I wrote this handler, but there’s an error. Can you take a look? Unstructured Team Review: Code is discussed at a team meeting, casually and collaboratively, often with knowledge sharing. Feedback is given as recommendations, not mandates. Developers can ignore or reject suggestions. Although informal reviews are less reliable than formal ones, they’re quicker and easier, and often complement formal reviews. Examples of integration: Preliminary Checks: Before a pull request, a dev shows code to a colleague to discuss and fix issues. Informal Discussion During Formal Review: Reviewers may chat to resolve issues more efficiently. Quick Fixes: Developers make changes right after oral feedback instead of long comment exchanges. 3. Pair Programming Pair programming is when two developers work together on one machine: one writes code, and the other reviews it in real-time. It’s literally simultaneous coding and reviewing, which helps catch bugs early. Roles: Driver: Writes code, focused on syntax and implementation. Navigator: Reviews logic, looks for bugs, suggests improvements, and thinks ahead. Roles can be switched regularly to keep both engaged. Variants: Strong Style: Navigator makes decisions, and the driver just types. It works well if one of the developers is more experienced. Loose Pairing: Both share decision-making, swapping roles as needed. Though rare, pair programming has advantages: Instant Feedback: Bugs are fixed immediately. In-depth Review: The second dev is deeply involved in writing the code. On-the-job Learning: Juniors learn directly from experienced peers. It’s more of a collaborative development method than a strict review. 4. Automated Review Automated code review uses tools that analyze code for errors, style, and vulnerabilities without human intervention. These tools are triggered automatically (e.g., after compilation, commit, or pull request). They analyze, run tests (e.g., unit tests), and generate reports. Some tools can even auto-merge code if it passes checks. Automated code review is part of DevOps and is common in CI/CD pipelines before deploying to production. Types: Static Analysis: Checks code without executing it — syntax errors, bad patterns, etc. Dynamic Analysis: Runs code to detect memory leaks, threading issues, and runtime errors. However, for now, tools can't catch business logic or architectural issues. As AI evolves, tools will likely become better at "understanding" code. When is Code Review Needed? Ideally, you should conduct code reviews both in small and large-scale projects. The only exceptions might be personal side-projects (pet projects), although even these can benefit from outside input. Automated testing has become standard, from JavaScript websites to C++ libraries. Still, code review can be skipped for: Trivial changes (e.g., formatting, UI text updates) Peripheral code (e.g., throwaway scripts, config files) Auto-generated code — unless manually modified In short, review the code only if it plays a critical or central role in the app and a human wrote it. Main Stages of Conducting Code Review Regardless of whether a review is formal, informal, or automated, there are several common stages. Preparation for Review Whether the written code is a new component for a production application or a modification of an existing method in a personal project, the developer is usually motivated to have it reviewed, either by fellow developers or by using automated testing tools. Accordingly, the developer has goals for the review and a rough plan for how it should be conducted, at least in broad terms. It’s important to understand who will participate in the review and whether they have the necessary competencies and authority. In the case of automated testing, it’s crucial to choose the right tools. Otherwise, the goals of the review may not be achieved, and critical bugs might remain in the code. Time constraints also matter: when all reviewers and testing tools will be ready to analyze the code, and how long it will take. It’s best to coordinate this in advance. Before starting the actual review, it can also be helpful to self-review—go over the code yourself and try to spot any flaws. There might be problems that can be fixed immediately. Once the developer is ready for the review, they notify the reviewers via chat, pull request, or just verbally. Code Analysis and Error Detection Reviewers study the code over a period of time. During this process, they prepare feedback in various formats: suggested fixes in an IDE, chat comments, verbal feedback, or testing reports. The format of the feedback depends on the tools used by the development team, which vary from project to project. Discussion of Edits and Recommendations Reviewers and the developer conduct a detailed discussion of the reviewed codebase. The goal is to improve the code while maintaining a productive dialogue. For instance, the developer might justify certain controversial decisions and avoid making some changes. Reviewers might also suggest non-obvious improvements that the developer hadn't considered. Documentation and Task Preparation All identified issues should be clearly documented and marked. Based on this, a list of tasks for corrections is prepared. Kanban boards or task managers are often used for this, e.g., Jira, Trello, and GitHub Issues. Again, the documentation format depends on the tools used by the team. Even a solo developer working on a personal project might write tasks down in a physical notebook—or, of course, in a digital one. Though keeping tasks in your head is also possible, it’s not recommended. Nowadays, explicit tracking is better than implicit assumptions. Relying on memory and intuition can lead to mistakes. Applying Fixes and Final Approval Once the list of corrections is compiled, the developer can begin making changes. They often also leave responses to comments. Bringing code to an acceptable state may take several review rounds. The process is repeated until both reviewers and the developer are satisfied. It’s crucial to ensure the code is fully functional and meets the team’s quality standards. After that, the final version of the code is merged into the main branch—assuming a version control system is being used. Tools for Code Review In most cases, code review is done using software tools. Broadly speaking, they fall into several categories: Version control systems: Most cloud platforms using version control systems (typically Git) offer built-in review tools for viewing, editing, and commenting on code snippets. Collaboration tools: Development teams often use not just messengers but also task managers or Kanban boards. These help with discussing code, assigning tasks, and sharing knowledge. Automated analyzers: Each programming language has tools for static code analysis to catch syntax issues, enforce style rules, and identify potential vulnerabilities. Automated tests: Once statically checked, the code is run through automated tests, usually via language-specific unit testing libraries. This article only covers the most basic tools that have become standard regardless of domain or programming language. GitHub / GitLab / Bitbucket GitHub, GitLab, and Bitbucket are cloud-based platforms for collaborative code hosting based on Git. Each offers tools for convenient code review. On GitHub and Bitbucket, this is called a Pull Request, while on GitLab it’s a Merge Request. Process: The developer creates a Pull/Merge Request documenting code changes, reviewer comments, and commit history. Reviewers leave inline comments and general feedback. After discussion, reviewers either approve the changes or request revisions. Each platform also provides CI/CD tools for running automated tests: GitHub Actions GitLab CI/CD Bitbucket Pipelines These platforms are considered the main tools for code reviews. The choice depends on team preferences. The toolas are generally similar but differ in details. Crucible Atlassian Crucible is a specialized tool dedicated solely to code review. It supports various version control systems: Git, SVN, Mercurial, Perforce. Crucible suits teams needing a more formalized review process, with detailed reports and customizable settings. It integrates tightly with Jira for project management. Unlike GitHub/GitLab/Bitbucket, Crucible is a self-hosted solution. It runs on company servers or private clouds. Pros and cons: Platform Deployment Managed by Maintenance Complexity GitHub / GitLab / Bitbucket Cloud Developer Low Atlassian Crucible On-premise End user/admin High Crucible demands more setup but allows organizations to enforce internal security and data policies. Other Tools Each programming language has its own specialized tools for runtime and static code analysis: C/C++: Valgrind for memory debugging Java: JProfiler, YourKit for profiling; Checkstyle, PMD for syntax checking Python: PyInstrument for performance; Pylint, Flake8 for quality analysis These tools often integrate into CI/CD pipelines run by systems like GitHub Actions, GitLab CI, CircleCI, Jenkins. Thus, formal code review tools are best used within a unified CI/CD pipeline to automatically test and build code into a final product. Best Practices and Tips for Code Review 1. Make atomic changes Smaller changes are easier and faster to review. It’s better to submit multiple focused reviews than one large, unfocused one. This aligns with the “Single Responsibility Principle” in SOLID. Each review should target a specific function so reviewers can focus deeply on one area. 2. Automate everything you can Automation reduces human error. Static analyzers, linters, and unit tests catch issues faster and more reliably. Automation also lowers developers’ cognitive load and allows them to focus on more complex coding tasks. 3. Review code, not the developer Code reviews are about the code, not the person writing it. Criticism should target the work, not the author. Maintain professionalism and use constructive language. A good review motivates and strengthens teamwork. A bad one causes stress and conflict. 4. Focus on architecture and logic Beautiful code can still have flawed logic. Poor architecture makes maintenance and scaling difficult. Pay attention to structure—an elegant algorithm means little in a badly designed system. 5. Use checklists for code reviews Checklists help guide your review and ensure consistency. A basic checklist might include: Is the code readable? Is it maintainable? Is there duplication? Is it covered by tests? Does it align with architectural principles? You can create custom code review checklists for specific projects or teams. 6. Discuss complex changes in person Sometimes it’s better to talk in person (or via call) than exchange messages—especially when dealing with broad architectural concerns. For specific code lines, written comments might be more effective due to the ability to reference exact snippets. 7. Code should be self-explanatory Good code speaks for itself. The simpler it is, the fewer bugs it tends to have. When preparing code for review, remember that other developers will read it. The clarity of the code affects the quality of the review. Put yourself in the reviewers’ shoes and ensure your decisions are easy to understand. Conclusion Code review is a set of practices to ensure code quality through analysis and subsequent revisions. It starts with syntax and architecture checks and ends with performance and security testing. Reviews can be manual, automated, or both. Typically, new code undergoes automated tests first, then manual review—or the reverse. If everything is in order, the code goes into production. If not, changes are requested, code is updated, and the process is repeated until the desired quality is achieved.
13 May 2025 · 12 min to read

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