PGConf.Russia 2023

My presentation "Explaining the Postgres Query Optimizer" covers the details of query optimization, optimizer statistics, joins, and indexes. This talk covers 40 other operations the optimizer can choose to handle complex queries, large data sets, and to enhance performance. These include merge append, gather, memoize, and hash aggregate. It explains their purpose and shows queries that can generate these operations.

This is a new talk; draft slides are at https://momjian.us/main/writings/pgsql/beyond.pdf

Historically PostgreSQL was intended to transactional OLTP workload. This thesis is confirmed by row-based kind of storage and impossibility (or some complication) in building distributed engine of query execution based on MPP principles. However, due to extensibility of PostgreSQL core (first of all, by using of pluggable access methods) and tolerant license policy similar to BSD there were appeared new different forks and extensions allowing effective processing of big data in analytical manner.

In current talk I'm going to review the PostgreSQL fork called Greenplum and Citus and TimescaleDB extensions from system developer's perspective by comparing their common analytical engine features: column storage, data compression, distributed query execution and so on. The results of such overview will be helpful to database architects seeking PostgreSQL-based DBMS for analytical workload.

I will talk about the common mistakes when setting up a Corosync-Pacemaker failover cluster. Often these mistakes lead to fatal consequences, and, as a result, to the rejection of the chosen solution in favor of others. Would you like to get a recipe for the "right" cluster?