How to optimize PostgreSQL queries for large datasets with millions of rows?

With 50 million rows, you definitely need a multi-pronged approach:

Table Partitioning: Partition your transactions table by date range (monthly or quarterly). This is the single biggest win for large tables. PostgreSQL 16 has excellent declarative partitioning support.

Materialized Views: For reporting queries, create materialized views that pre-compute your aggregations. Refresh them on a schedule (every hour or daily depending on freshness requirements).

Query Optimization: Use EXPLAIN ANALYZE to identify bottlenecks. Look for sequential scans on large tables and missing index usage. Consider partial indexes for commonly filtered subsets.

Also enable pg_stat_statements to identify your slowest queries systematically.

I managed a PostgreSQL database with 200 million rows and here is what worked for us:

Connection Pooling: Use PgBouncer in transaction mode. This alone improved our throughput by 3x by reducing connection overhead.

Read Replicas: Route all reporting and analytics queries to read replicas. This frees up your primary for writes and transactional reads.

BRIN Indexes: For time-series data, BRIN indexes are incredibly space-efficient and fast. They work perfectly when your data is naturally ordered by insertion time.

Our application has grown to handle over 50 million rows in our main transactions table. Query performance has degraded significantly, especially for reporting queries that involve multiple JOINs and aggregations.

We have already added indexes on frequently queried columns, but some complex queries still take over 30 seconds. We are running PostgreSQL 16 on a dedicated server with 64GB RAM.

What strategies should we consider? Partitioning, materialized views, query restructuring, or should we look into a different architecture entirely?