Отказоустойчивый кластер PostgreSQL с помощью crmsh

Some OS distributions do not have a pcs configuration utility to create a high availability cluster PostgreSQL. In this case, the crm utility from the crmsh package will help us. It is more difficult to use, but powerful and effective.

In my master class, I will show how to use this utility, as well as configure a failover cluster in different configurations.

Построение автоматического консультанта и инструментов настройки производительности в PostgreSQL

PostgreSQL is a mature and robust RDBMS since it has 30 years of history. Over the year, its query optimizer has been enhanced and usually produces good query plans.

However, can it always come up with good query plans? The optimization process has to use some assumptions to produce plans fast enough. Some of those assumptions are relatively easy to check (e.g. statistics are up-to-date), some harder (e.g. correct indexes are created), and some nearly impossible (e.g. making sure that the statistic samples are representative enough even for skewed data repartition). For now, given those various caveats, DBA sometimes can't always realize easily that they miss a chance to get a meaningful performance improvement.

To help DBA to get a truly good query plan, we'll present below some tools that can help to fix some of those problems by providing a missing index adviser, looking for extended statistics to create, and row estimation error correction information to get appropriate join orders with join methods automatically.

• pg_qualstats: provides a new index and extended statistics suggestions to gather many predicate statistics on the production workload.
• pg_plan_advsr: provides alternative good query plans automatically to analyze iterative query executions information to fix estimation rows error.

In this talk, we will explain how those tools work under the hood and see what can be done, how they can work together. Also, we will mention what other tools also exist for related problems. Therefore, it will be useful for DBA who are interested in improving query performance or want to check whether current settings of indexes and statistics are adequate.

Postgres и искусственный интеллект в современном мире

Artificial intelligence, machine learning, and deep learning are intertwined capabilities that attempt to solve problems that defy traditional computational solutions — problems include fraud detection, voice recognition, and search result recommendations. While they defy simple computation, they are computationally expensive, involving computation of perhaps millions of probabilities and weights. While these computations can be done outside of the database, there are specific advantages of doing machine learning inside the database, close to where the data is stored. This presentation explains how to do machine learning inside the Postgres database.

Сжатие на уровне СУБД в реалиях 1С

Postgres Pro Enterprise has a great compression engine. The year 2020 was devoted to the study of this mechanism in the real work of 1C. We have accumulated some statistical data and of course the subtleties of the use and behavior of 1C compared to other popular DBMS, which I want to share.