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If you think this code is idiomatic, elegant and beautiful, read this article!

FindProcess(ByTitle(title))

FindProcess(ByPID(pid))

Learning Shopify has been on my bucket list for a few years now. Plenty of people in my circle — friends, colleagues, and fellow devs — are all somehow involved with Shopify in one way or the other. Earlier this year, I finally had some breathing room between projects, so I figured it was the perfect time to give Shopify a proper look.

I started exploring the platform by setting up a dev store, poking around the admin, and skimming through the API manual. While this was a quick and easy start, it didn’t give me a deeper understanding of the platform. Plus, clicking my way through the UI felt repetitive and tedious.

That got me thinking: is there a more efficient, developer-centric way to manage a store? Something that I could run in a terminal, plug into a CI/CD pipeline, or script my way out of those mundane tasks.

While in 2024 using XML looks a bit outdated, it still happens. And sometimes it happens we are to deal with XML having "free-structure", i.e. it couldn't be parsed into tree of user-defined structs. For JSON there still is a way to parse it with a single call into map[string]any and work with it using careful type assertions. Regretfully, there is no similar feature for XML (in Golang). Here I'll draft suitable function and demonstrate it - both for others and for myself if I ever need this again (recreating it from scratch may be somewhat painful).

Clean Go

Hey guys, I recently dove into 'Clean Code' by Robert C. Martin and found some valuable insights. The book is originally in Java, but I decided to reinterpret the principles in Go. Here's my take on the clean code concepts and how they can improve our coding practices.

1. Clean Code

The gist: Clean code is more than just working code; it's code that other developers can easily read, understand, and modify.

Openjob is a new  distributed task scheduling framework based on Akka architecture. Supports multiple cronjob, delay task, workflow, lightweight distributed computing, unlimited horizontal scaling, with high scalability and fault tolerance. Also has complete management, powerful alarm monitoring, and support multiple languages

Openjob is a new  distributed task scheduling framework based on Akka architecture. Supports multiple cronjob, delay task, workflow, lightweight distributed computing, unlimited horizontal scaling, with high scalability and fault tolerance. Also has complete management, powerful alarm monitoring, and support multiple languages

If you are looking for a high-performance distributed task scheduling framework that supports cronjob, delay task, lightweight computing, workflow, and supports multiple programming languages, then Openjob is definitely the way to go.

gookit/goutil An extended library of Go's common functionality. Contains: number, string, slice/array, Map, struct, reflection, text, file, error, time and date, test, CLI, command run, system information, formatting, common information acquisition, etc.

Openjob is a distributed and high-performance task scheduling framework that supports multiple cronjob, delay task, workflow, lightweight distributed computing, unlimited horizontal scaling, with high scalability and fault tolerance. Also has perfect permission management, powerful alarm monitoring, and support multiple languages

This is a translation of my article in Russian

In this article, I want to write about my experience of interacting with the LeetCode platform, and describe my preparation for an interview in FAANG similar companies by breaking it down into levels.

The whole article is written based on my experience, the numbers are very rough, I do not pretend to be objective, perhaps there are best practices on how to solve LeetCode problems, it would be cool if you share your experience in the comments.

This is a series of articles dedicated to the optimal choice between different systems on a real project or an architectural interview.

At work or at a System Design interview, you often have to choose the best message broker. I plunged into this issue and will tell you what and why. What is better in each case, what are the advantages and disadvantages of these systems, and which one to choose, I will show with several examples.

This is a series of articles dedicated to the optimal choice between different systems on a real project or an architectural interview.

This topic seemed relevant to me because such tasks can be encountered both at work and at an interview for System Design Interview and you will have to choose between these two types of DBMS. I plunged into this issue and will tell you what and how. What is better in each case, what are the advantages and disadvantages of these systems and which one to choose, I will show with several examples at the end of the article.

SQL or NoSQL?

1) OpenTracing (OT) != Logs but they are very similar.

2) Every application has 2 types of scopes: ApplicationScope (AScope) and RequestScope (RScope).

This is an article that describes my vision of building a system that actively uses Go as the main programming language and SOA/microservices as a design paradigm. 

Here I will try to cover 4 chapters that together allow us to build a solid and reliable system.

JiraCLI is an interactive command line tool for Atlassian Jira that will help you avoid Jira UI to some extent. This tool is not yet considered complete but has all the essential features required to improve your workflow with Jira.

The tool started with the idea of making issue search and navigation as straightforward as possible. However, the tool now includes all necessary features like issue creation, cloning, linking, ticket transition, and much more.

The tool supports both jira cloud and on-premise jira installation since the latest release.

OpenAPI specification

One of the nicest features of Go is the power of code generation. go generate command serves as a Swish knife allowing you to generate enums, mocks and stubs. In this article, we will employ this feature to generate a Go code from OpenAPI specification. OpenAPI specification is a modern industrial standard for REST API. This standard has fantastic tooling support and allows you to conveniently render and validate the spec. We are going to befriend the power of Go code generation with the elegance and clarity of the OpenAPI specification. In this way, you don't have to manually update the Go boilerplate code after every change in the spec. You also ensure that your docs and your code are a single entity, as your code is being begotten from the docs.

Let's start dead-simple: we have a service that accepts order requests. Let's declare endpoint order/10045234 that accepts PUT requests, where 10045234 is an ID of a particular order. We expect to receive an order as a JSON payload in the following format.

    {"item":  "Tea Table Green", "price":  106}

How can describe this endpoint in the OpenAPI spec?

In this series, I would like to discuss some reaches of Go programming language. There is no shortage of Go-Language-Of-Cloud style articles in which you can explore the great benefits that Go indeed provides. However, there are lees to every wine, and Go does not go without blemish. In this highly opinionated series, we cover some controversies and, dare I say, pitfalls of the original Go design.

We start tough and begin with the essence of Go — it's inbuild data types. In this article, we put slice to the test. Let's move a step further from the Go Tour and use slice more extensively. For example, there is no separate data type as stack in Go, because slice type is intended to cover all its usage scenarios.

Let's briefly recap the usage of the stack. We can create a stack in two seconds using a couple of paper stickers. You write "buy milk" on the first sticker and put at the desk, and then "make the dishes" on the second and pile it on the first sticker. Now, you have a stack: the dishes sticker came last but will be served first, as it is on the top of the stack. Thus, there is an alternative name for stack — LIFO, Last-In-First-Out. To compare, there is the "opposite" data structure queue or FILO — first in, first out. In programming, stacks are everywhere, either in the explicit form or in the implicit as stack trace of the execution of a recursive function.

Ok, let's put slice into use and implement stack.

This article is a part of Algorithms in Go series where we discuss common algorithmic problems and their solution patterns.

In this edition, we take a closer look at bit manipulations. Bit operations can be extremely powerful and useful in an entire class of algorithmic problems, including problems that at first glance does not have to do anything with bits.

Let's consider the following problem: six friends meet in the bar and decide who pays for the next round. They would like to select a random person among them for that. How can they do a random selection using only a single coin?

The solution to this problem is not particularly obvious (for me:), so let's simplify a problem for a moment to develop our understanding. How would we do the selection if there were only three friends? In other words, how would we "mimic" a three-sided coin with a two-sided coin?

In this series, we will be discussing interesting aspects and corner cases of Golang. Some questions will be obvious, and some will require a closer look even from an experienced Go developer. These question will help to deeper the understanding of the programming language, and its underlying philosophy. Without much ado, let's start with the first part.

Value assignment

What value y will have at the end of the execution?

func main() {
    var y int
    for y, z := 1, 1; y < 10; y++ {
        _ = y
        _ = z
    }
    fmt.Println(y)
}

According to the specification,

Most solutions to algorithmic problems can be grouped into a rather small number of patterns. When we start to solve some problem, we need to think about how we would classify them. For example, can we apply fast and slow аlgorithmic pattern or do we need to use cyclic sortpattern? Some of the problems have several solutions based on different patterns. In this series, we discuss the most popular algorithmic patterns that cover more than 90% of the usual problems.

It is different from High-School Algorithms 101 Course, as it is not intended to cover things like Karatsuba algorithm (fast multiplication algorithm) or prove different methods of sorting. Instead, Algorithmic Patterns focused on practical skills needed for the solution of common problems. For example, when we set up a Prometheus alert for high request latency we are dealing with Sliding Window Pattern. Or let say, we organize a team event and need to find an available time slot for every participant. At the first glance, it is not obvious that in this case, we are actually solving an algorithmic problem. Actually, during our day we usually solve a bunch of algorithmic problems without realizing that we dealing with algorithms.

The knowledge about Algorithmic Patterns helps one to classify a problem and then apply the appropriate method.

But probably most importantly learning algorithmic patterns boost general programming skills. It is especially helpful when you are debugging some production code, as it trains you to understand the execution flow.

Patterns covered so far:

Sliding Window I

Sliding Window II

Merge Intervals

Dutch National Flag

Matrix Spiral

Iterative Postorder Traversal

Bit Manipulation

Stay tuned :)

<Promo> If you interested to work as a backend engineer, there is an open position in my squad. Prior knowledge of Golang is not required. I am NOT an HR and DO NOT represent the company in any capacity. However, I can share my personal experience as a backend engineer working in the company. </Promo>

In this article, we discuss the postorder traversal of a binary tree. What does postorder traversal mean? It means that at first, we process the left subtree of the node, then the right subtree of the node, and only after that we process the node itself.

Why would we need to do it in this order? This approach solves an entire class of algorithmic problems related to the binary trees. For example, to find the longest path between two nodes we need to traverse the tree in a postorder manner. In general, postorder traversal is needed when we cannot process the node without processing its children first. In this manner, for example, we can calculate the height of the tree. To know the height of a node, we need to calculate the height of its children and increment it by one.

Let's start with a recursive approach. We need to process the left child, then the right child and finally we can process the node itself. For simplicity, let's just save the values into slice out.