indexing

Full-text search finds documents matching text queries. PostgreSQL tsvector stores processed documents optimized for search. I use tsquery for search queries with operators—AND, OR, NOT. GIN indexes on tsvector columns enable fast search. Text search

I treat pandas as the default workbench for structured data. The goal is to make loading explicit, indexes predictable, and selection operations readable under maintenance pressure. I prefer stable column naming, typed parsing for dates, and avoiding

Indexes dramatically speed up queries but slow down writes. B-tree indexes handle equality and range queries—default for most databases. I create indexes on foreign keys, frequently queried columns, and WHERE/ORDER BY clauses. Composite indexes order

PostgreSQL JSONB stores binary JSON efficiently with indexing support. I use JSONB for semi-structured data, dynamic attributes, event logs. JSONB operators enable querying nested data—->, ->>, @>, ?. GIN indexes accelerate JSONB queries.

JSON and JSONB store semi-structured data. JSONB is binary format—faster, indexable. I use JSONB for flexible schemas, API responses, configuration. JSON operators extract values, filter documents. GIN indexes enable fast JSONB queries. Containment op