Speed up your fast text search queries with RUM index

Fast text search queries can be made even faster with indexing on lexemes inside compound records of tsvector format. RUM index is an open-source PostgreSQL extension. It represents a big improvement of GIN index and it can index lexemes with additional information e.g. tsvector lexeme weight-mark. So it can support tsvector capabilities more.

Until recently it was needed to recheck results of weight-containing queries by table. My modification (2020) is to make the processing of this kind of queries index-only and therefore much faster.

Also, I will describe and provide benchmarks for different usage cases for fast text search. We'll see how RUM index can improve the performance and compare it with PostgreSQL internal GIN index.

How query execution history can help with re-execution

Postgres is able to build optimal query plans for most practical cases. However, sometimes, for objective reasons, for complex queries or because of open issues in the planner itself, it can make mistakes and produce a suboptimal plan. Because of this, the execution time of such a request can increase tenfold. If the query is executed frequently, then from time to time this query takes longer than it could, and the DBMS as a whole produces a lower TPS. If the planner is able to record his mistakes and take them into account in the subsequent planning of the same query, then this will improve the characteristics of the DBMS during its operation. We present the results of the development of a PostgreSQL DBMS extension that stores the query execution history and implements the planner recommendation mechanism. We show how knowledge about previously executed queries can improve the performance of subsequent ones.

How to use pspg

pspg is unix pager specially developed for usage in psql Postgres client. Today it allows more than usual browsing data. It can work in application mode or it work as csv or tsv viewer too. I will try to show the main possibilities of this application.

SQL as data

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.