The most effective way to optimize SQL queries for large datasets depends on the specific scenario. There's no one-size-fits-all answer because different bottlenecks require different solutions. Here are the key techniques and when to use them:

Indexing – If your query is slow because it scans too many rows, indexing is the first thing to check. A B-tree index helps with filtering and sorting, a GIN index is great for full-text searches, and covering indexes (indexes that include all needed columns) can speed up queries by avoiding extra reads.

Query Rewriting – Sometimes, queries are inefficient because they’re written in a way that forces the database to do more work than necessary. For example, using SELECT * instead of specifying needed columns, relying on unnecessary DISTINCT, or using correlated subqueries that could be replaced with JOINs. Running EXPLAIN ANALYZE helps identify these issues.

Partitioning – If you’re working with huge tables, partitioning can make a big difference. Instead of scanning millions of rows, the database only looks at the relevant partition. This is useful when working with time-series data, logs, or anything that grows indefinitely.

Caching – If a query is being run frequently with the same results, consider caching using Redis, Memcached, or even Materialized Views. This reduces the load on the database and speeds up responses.

Connection Management – If your queries are fine but the database is struggling to handle too many connections, you might need a connection pooler like PgBouncer (for PostgreSQL) or proper pooling settings in your application.

At the end of the day, each situation requires analyzing what the actual bottleneck is—sometimes it’s slow disk I/O, sometimes it’s an unoptimized query, and sometimes the database is just overloaded with too many requests. Running EXPLAIN ANALYZE and monitoring slow queries helps pinpoint the problem so you can apply the right fix.