Tell about data storage in PostgreSQL: what you designed, and about PostgreSQL optimization — why you decided to optimize and how you did it?

In projects with PostgreSQL, I designed databases considering normalization to minimize redundancy and ensure data integrity. I usually started with an ER diagram, identified main entities and relationships, and considered data types and indexes to speed up queries.

I optimized PostgreSQL when faced with slow queries or high load. The main steps:

• Analyzed query execution plans using EXPLAIN and EXPLAIN ANALYZE.
• Added indexes (B-tree, GIN, GiST) on frequently used fields, especially for filtering and sorting.
• Used table partitioning for large volumes of data to accelerate queries.
• Tuned PostgreSQL configuration parameters (work_mem, shared_buffers, effective_cache_size) based on load and server memory.
• Performed vacuuming and analyzing of tables to update statistics.

Example of an index to speed up text field searches:

CREATE INDEX idx_users_email ON users USING btree(email);

Optimization reduced query response times from several seconds to fractions of a second, which is critical for high-load applications.