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.

Консенсус, Postgres, Мультимастер

Postgres Pro Multimaster is Postgres extension (and a set of core patches) providing high availability (HA) with strong consistency and read scalability. It forms symmetric shared-nothing cluster synchronously replicating the data and automatically performing disaster recovery. During the last year we've put significant efforts ensuring and proving that consistency is preserved in all scenarios. The new version, which will be released as part of Postgres Pro Enterprise 13 uses Paxos algorithm for determining transaction outcome and custom protocol governing the recovery process; we used TLA+ model checker to verify its correctness. I'll tell how these things work and why in some cases multimaster may be an attractive alternative to the traditional streaming replication based HA deployments.

multimaster is now open source, available at https://github.com/postgrespro/mmts

To make the talk less narrow specialized and more appealing to the wide audience, in the first part I will shed some light on how generally modern DBMSs (mostly so-called NewSQL) handle fault tolerance. In particular,

• what is a strongly consistent DBMS and the associated overhead;
• what is distributed consensus, Paxos, Raft;
• how they help here;

I won't do an attempt to explain any algorithms line-by-line; it would be hardly useful given the time frames and there is a lot of literature available anyway. The goal is rather to waymark the field and get you a bit comfortable there.

SQL данными

Almost every business app is essentially just a SQL generator. How to easily build and compose SQL queries? I will explain the "Clojure way" of representing SQL as data (data DSL) and show how it may help you to dynamically build and compose SQL queries up to macros and query analysis.

Как перестать бояться обновлений glibc

PostgreSQL relies on the system collation libraries, such glibc or ICU, for text ordering. One know caveat is that when the library change its sort order for a collation, any index created using the old order is likely to be corrupted when the new version of the library is installed.

In this talk, we'll see the improvements done in PostgreSQL 14 to keep track of the collation versions, detect and fix possible index corruption due to library upgrades and the work currently being done to further improve this area.