Hi everyone! My name is Dmitrii Konstantinov and this is a personal story of experience and growth in a cozy startup. How we made a business pivot and adapted the server and mobile apps for it. What challenges did we face and how did we solve them.

When talking about high-performance software we probably think of server software (such as nginx) which processes millions requests from thousands clients in parallel. Surely, what makes server software work so fast is high-end CPU running with huge amount of memory and a very fast network link. But even then, the software must utilize these hardware resources at maximum efficiency level, otherwise it will end up wasting the most of the valuable CPU power for unnecessary kernel-user context switching or while waiting for slow I/O operations to complete.

Thankfully, the Operating Systems have a solution to this problem, and it's called kernel event queue. Server software and OS kernel use this mechanism together to achieve minimum latency and maximum scalability (when serving a very large number of clients in parallel). In this article we are going to talk about FreeBSD, macOS and kqueue, Linux and epoll, Windows and I/O Completion Ports. They all have their similarities and differences which we're going to discuss here. The goal of this article is for you to understand the whole mechanism behind kernel queues and to understand how to work with each API.

Many people are familiar with the situation when there is no Internet, and a small dinosaur appears on the Google Chrome screen. Today we will tell you how to implement this game on the Cyclone IV FPGA board.

We are Yegor Blinov, Egor Kuziakov, and Inga Ezhova - the first-year students of Innopolis University. In our program, there was a course "Computer Architecture", where we had labs with FPGA boards Cyclone IV and MAX10. We were inspired by this equipment and decided to implement the project on one of the boards.

As you know, YouTube doesn't have a feature for capturing an RTSP stream, but we would like to change this and help YouTube to make their viewers happy.

EAP 11 of the Big Data Tools plugin for IntelliJ IDEA Ultimate, PyCharm, and DataGrip is available starting today. You can install it from the JetBrains Plugin Repository or inside your IDE.

Big Data Tools is a new JetBrains plugin that allows you to connect to Hadoop and Spark clusters and monitor nodes, applications, and jobs. It also brings support for editing and running Zeppelin notebooks inside IntelliJ IDEA and DataGrip, so you can create, edit, and run Zeppelin notebooks without ever having to leave your favorite IDE. The plugin offers smart navigation, code completion, inspections, quick-fixes, and refactoring inside notebooks.

People who use Kibana in our company have different background — some of them are technical who process data, some are managers who simply want to monitor some KPIs. And all have various questions. In spite of Kibana is rather popular in IT companies, there are not many articles or courses about it. To fill the gap I have created Kibana Tips & Tricks — weekly letters with frequently asked questions or themes. Such letters help our users to become more familiar with Kibana. There are no secrets — just detailed description of how you may work with your data.

Many thanks to Elena Indrupskaya for the translation. Russian version is here.

Implementation a web UI is faced with more and more complex tasks that require the use of more and more complex tools. The trend of leaving the MVC application architecture, as well as the desire of developers to use separate libraries for each layer of tasks, led to changes, against which the Vue.JS framework appeared and became popular. Let's try to understand why Vue.JS appeared and what problems it carefully solves.

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.

• .NET Core 3.1 SDK and Runtime
• Docker container images
• Snap installer

• .NET Core 3.1 release notes
• GitHub issue for .NET Core 3.1 issues
• GitHub release

Tuple header

I frequently hear questions like "How to implement authentication in an Android app?", "Where to store a PIN?", "Hey man, will I be secure if I implement an authentication feature in such a way?" and a lot of the kind. I got really tired answering these questions so I decided to write all my thoughts about it once to share with all questioners.