Java *

If you utilize Apache Spark, you probably have a few applications that consume some data from external sources and produce some intermediate result, that is about to be consumed by some applications further down the processing chain, and so on until you get a final result.

We suspect that because we have a similar pipeline with lots of processes like this one:

Click here for a bit larger version

Each rectangle is a Spark application with a set of their own execution parameters, and each arrow is an equally parametrized dataset (externally stored highlighted with a color; note the number of intermediate ones). This example is not the most complex of our processes, it’s fairly a simple one. And we don’t assemble such workflows manually, we generate them from Process Templates (outlined as groups on this flowchart).

So here comes the One Ring, a Spark pipelining framework with very robust configuration abilities, which makes it easier to compose and execute a most complex Process as a single large Spark job.

And we just made it open source. Perhaps, you’re interested in the details.

Introduction

In my opinion transaction management is a really important topic for each backend developer. In general, people don’t pay attention to it while using Spring framework.

But I think, it is important to know how to use transactions properly. Because sometimes can happen that there was an exception thrown inside your method, but transaction was not rolled back and it is not clear why? Or some other “strange” cases.

<dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
</dependency>

 <dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
     <classifier>javadoc</classifier>
</dependency>

<dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
     <classifier>sources</classifier>
</dependency>

• Map

Parameters used

Working

How to implement?

const map = (transformFunction, array1) =>
  array1.reduce((newArray1, xyz) => 
{
	newArray1.push(transformFunction(xyz));

	return newArray1;
  }, 
[]
);

Use case:

const double = (x) => x * 2;
const reverseString = (string) =>
  string
	.split('')
	.reverse()
	.join('');

map(double, [200, 300, 400]);

Output: [400, 600, 800]

map(reverseString, ['Hello Alka', 'I love cooking']);
// ['alkA olleH', ‘gnikooc evol I']

Future Software Development Is Here!

Let’s Try Out With Some Code

X=np.array([[0,1,1,0],[0,1,1,1],[1,0,0,1]])
y=np.array([[0],[1],[1]])

def sigmoid ():
   return 1/(1 + np.exp(-x))
def derivatives_sigmoid ():
   return x * (1-x)

epoch=10000
lr=0.1
inputlayer_neurons = X.shape[1]
hiddenlayer_neurons = 3
output_neurons = 1

wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))

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 article presents a business-driven approach to software architecture, focusing on maximizing profitability through technical decisions. It advocates for:

Distributed, stateless services with immutable models for scalability

Isolated third-party integrations without disrupting core logic

Simple, modular design 

Best for:

✔ Tech leads designing scalable systems
✔ Managers optimizing dev efficiency
✔ Stakeholders evaluating ROI on architecture

When we go abroad for vacation or meet a foreigner on the street who doesn’t speak Russian but is trying to ask, “Where is the restroom? How do I get to…”, we wonder how to explain things to them in Russian in a way they would understand.

I asked myself a similar question when trying to explain something to a colleague using SQL while they were working with Java. The main goal of my work was to create a quality test model. Without it, there would be no proper regression testing later on.

I started by building a framework filling it with test cases. We held a meeting where we discussed priority of positive and negative test cases briefly. When developing the test scenarios, I used the incremental model, but as practice showed, this approach also required an iterative method. For example, it is like having the outline of the Mona Lisa first, then adding colors, painting the background, and so on.

It’s better to maintain the checklist in Excel format to add columns, write notes, and more. And let’s not forget that, as we take on the role of Leonardo da Vinci, we use different colors and get creative.

I am a manager by profession specializating in Production Management. My motivator is the Theory of Constraints (TOC) methodology, which focuses on identifying and managing the key constraint of a system to determine the efficiency of the entire system as a whole:

Exception handling in Java is the most effective way to handle runtime errors occurring in the application. This is used to protect the abnormal flow of the execution of the application and continue the application in normal flow. This is the process of handling runtime errors such as ClassNotFoundException, IOException, etc. The throw and throws keywords are used to handle exceptions in Java.
In this topic, we will learn how to use throw and throws keywords in Java with examples.

In this tutorial, I want to talk about the practical experience of native compilation of a production application written in Kotlin with Spring Boot and Gradle using GraalVM. I’ll start right away with the pros and cons of the native compilation feature itself and where it can be useful, and then I’ll move directly to the build process for MacOS and Windows.

At the end of the article, in the afterword block, I will talk in more detail about the project and why such a need arose, given quite a few limitations and pitfalls of supporting native compilation both from Spring Boot and from GraalVM.

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

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.

In this article, I want to describe my experience of paying off technical debt on our project in the form of a guide. In this guide, I will highlight some of the most common cases of technical debt and suggest methods for solving them. Since this is a rather extensive topic, I will recommend several books for study, because I do not see it possible to talk about everything within the framework of this article. Everything described applies to the BackEnd part, but it may be suitable for other developers. I would be glad if you share your experience on this topic in the comments.

In microservice world the problem of high load is exteremely big especially when we have a REST API which is accessed quite extensively. Why do we need throttling? The main answer is to decrease the load of the service at the moment.

Different frameworks have different solutions, mostly some additional libraries. Also there is a Guava RateLimiter and Bucket4J . What is interesting Spring MVC being one of the most popular solutions for building REST APIs (thank you Spring Boot) doesn't have any built in rate limiter. As for external solutions there not that many ways around.

Today, I would like to present a tiny experimental library specific for Spring MVC. It is called SpringRateLimitter. The library is very tiny ,works in runtime. The idea is to annotate entire rest controller or specific method , than count the number of incoming requests for the annotated URI and based on the values check if we exceed the allowed number of calls. In case of exceeding an HTTP error code 429 is thrown and after the throttling period is over , the endpoint is available again.

So How does it look like. As first step Maven dependency must be added

If you feel like the New Year just came, and you missed the first half of January, then all this time you've been busy looking for tricky bugs in the code you maintain. It also means that our article is what you need. PVS-Studio has checked the ELKI open source project to show you errors that may occur in the code, how cunningly they can hide there, and how you can deal with them.

SWIG is a tool for generating cross-language interfaces - it provides interoperability of C++ and other languages (C++ and Java in our case). SWIG just simplifies and automizes cross-language interaction; otherwise, you may end up with thousands of lines of handwritten JNI code - but SWIG covers this for you.

This guide is for newbies (Part 1) and for those who experienced in SWIG (part 2). I'm starting from basic setup and usage and ending with complex & weird cases encountered in development. The latter cases are not so complex, rather usual for modern languages, which SWIG doesn't support yet (as lambdas).

This guide is practical. In opposition to overcomplicated huge-volume SWIG documentation, this guide is showing the cases practically. The bits developed by myself while working on the different projects or taken from StackOverflow. This guide allows you to quick-start an Android Studio project and giving practical examples of using SWIG. The link to the Android Studio project is here.

This guide is Android-first. The goal was to make it simple to onboard for Android developers. There are many articles about SWIG, but they are mainly for desktop Java applications, and it is quite an overhead to just try them on Android to check if the solution for the particular problem is working. While this guide includes an Android Studio project, with which you can play around instantly. Of course, all the information given here applies to any Java application.

Warning! I should warn you, that nowadays cross-platform development offers powerful tools. If you are developing a new application it is much more cost-efficient in practice to use ReactNative, Flutter of Kotlin-Native than the SWIG. While SWIG is more suitable to connect the C++ library or existing C++ application core.