Database Scaling Guide for Growing Applications

Step 1: Query Optimization (Free Performance)

Before you throw hardware at the problem, optimize your queries. Most database slowdowns are caused by a handful of expensive queries that scan entire tables instead of using indexes. Run EXPLAIN ANALYZE on your slow queries and look for sequential scans on large tables.

Common fixes: add missing indexes (especially on foreign keys and frequently-filtered columns), rewrite N+1 queries (the "select all users, then select each user's orders individually" pattern), and avoid SELECT * in favor of selecting only the columns you need.

PostgreSQL's pg_stat_statements extension shows you which queries consume the most time. Start there. In our experience, optimizing the top 5 slowest queries typically reduces overall database load by 50-70%.

Step 2: Caching (The Biggest Win)

Caching is the highest-impact scaling strategy for most applications. If the same data is read 100 times for every write, you can serve 99% of reads from cache instead of hitting the database.

Redis is the standard caching layer. Cache expensive query results with a TTL (time-to-live) that matches your data freshness requirements. For real-time data, use short TTLs (30 seconds to 5 minutes). For reference data, use longer TTLs (hours to days).

Cache invalidation is the hard part. The simplest approach: write-through caching (update the cache whenever you update the database). For complex scenarios, use event-driven invalidation or accept eventual consistency with short TTLs.

Step 3: Read Replicas

When your read volume exceeds what a single database can handle (even with caching), add read replicas. The primary database handles writes, replicas handle reads. Most applications are read-heavy (90%+ reads), so this can multiply your capacity by 3-5x.

PostgreSQL streaming replication creates near-real-time replicas (usually less than 1 second behind). Route read queries to replicas and write queries to the primary. Be aware of replication lag — if a user writes data and immediately reads it, they should read from the primary to avoid stale data.

Step 4: When to Consider Database Migration

You should only consider database migration or sharding after exhausting optimization, caching, and read replicas. Most applications serving millions of users never need to go beyond step 3.

If you do need to go further: vertical scaling (bigger machine) is simpler than horizontal scaling (sharding). PostgreSQL can handle databases in the hundreds of gigabytes to low terabytes on a single well-configured machine. Only consider sharding when you've outgrown the biggest available instance.

If your application has fundamentally different data access patterns (e.g., high-volume time-series data alongside relational data), consider using specialized databases for specific workloads (e.g., TimescaleDB for time-series) rather than making your primary database handle everything.