Admin

This post is handling the following situation - how to setup up simple Mysql services with group replication being dockerized. In our case, we’ll take the latest Mysql (version 8.x.x)

FYI: all mentioned code (worked and tested manually) located here.

I will skip not interested steps like ‘what is Mysql, Docker and why we choose them, etc’. We want to set up possibly trouble proof DB. That’s our plan.

Challenges of real-time AI/ML computations

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

Introduction

What do these terms mean? And what is the difference?

Intro

Every Linux professional write scripts. Someеimes light, linear. Sometimes complex script with functions and libs(yes, you can write your bash-library for use in other scripts).

But some of the scripts need a configuration file to work. For instance, I wrote a script that builds the ubuntu image for pxe, and I need to change the build process without build-script changes. The best way to resolve this task is to add configuration files.

• our requirements for the database, universal solutions, and the CAP theorem
• whether the database + application server in one approach is a silver bullet
• the evolution of the platform and the databases used in it
• the number of Tarantools we use and how we came to this

Academic data warehouse design recommends keeping everything in a normalized form, with links between. Then the roll forward of changes in relational math will provide a reliable repository with transaction support. Atomicity, Consistency, Isolation, Durability — that's all. In other words, the storage is explicitly built to safely update the data. But it is not optimal for searching, especially with a broad gesture on the tables and fields. We need indices, a lot of indices! Volumes expand, recording slows down. SQL LIKE can not be indexed, and JOIN GROUP BY sends us to meditate in the query planner.

Intro

PXE is a great solution for booting a diskless computer (or a computer without an OS installed). This method is often used for terminal stations and OS mass installation.

Stock ubuntu (16.04) in pxe-mode can mount rootfs only from NFS. But this is not a great idea: any difficulties with the network/NFS server and the user gets problems.

In my opinion, it's best to use other protocols, such as http/ftp. Once booting, you will have an independent system

The Global Blockchain Adoption

We have all been there  —  having a nice idea for a hackathon, hobby or a side project and having a burning desire to start coding as soon as possible. And how many possibilities (Heroku, Glitch and others) are there now to bootstrap your app and deploy it immediately.

When Alex Garland’s series Devs (on FX and Hulu) came out this year, it gave developers their own sexy Hollywood workup. Who knew that coders could get snarled into murder plots and love triangles just for designing machine learning programs? Or that their software would cause a philosophical crisis? Sure, the average day of a developer is more code writing than murder but what a thrill to author powerful new program.