How do you optimize for query latency?

To optimize query latency, focus on three main areas: database design, query structure, and infrastructure tuning. Each plays a role in reducing the time between a query’s initiation and its result delivery. Start by ensuring your database schema and indexing strategy align with common query patterns. Next, refine queries to minimize unnecessary processing. Finally, use hardware and software configurations to support efficient execution.

First, optimize your database design. Use indexes strategically on columns frequently used in filters (WHERE clauses), joins, or sorting (ORDER BY). For example, an e-commerce app filtering products by price and category benefits from a composite index on both columns. Avoid over-indexing, as it slows write operations. Partition large tables into smaller segments (e.g., by date or region) to reduce the data scanned per query. If a query targets recent orders, partitioning by order_date allows the database to skip older data. Additionally, normalize or denormalize tables based on access patterns. Normalization reduces redundancy but may require more joins; denormalization can speed up reads at the cost of storage.

Second, write efficient queries. Use tools like EXPLAIN (in PostgreSQL) or Query Execution Plans (in SQL Server) to identify bottlenecks like full table scans or inefficient joins. Avoid SELECT * and fetch only necessary columns to reduce data transfer. For example, querying user emails from a profiles table should explicitly select email instead of all fields. Use batch operations for bulk data instead of individual requests. Caching frequent queries (e.g., product listings) in tools like Redis or Memcached reduces repeated database hits. For complex queries, materialized views can precompute and store results, trading storage for speed. For instance, a daily sales report could be pre-aggregated to avoid recalculating totals on every request.

Third, tune infrastructure. Scale vertically (faster CPUs, more RAM) for compute-heavy workloads or horizontally (sharding, read replicas) for high read volumes. A social media app might use read replicas to distribute follower feed queries. Configure memory settings (e.g., increasing buffer pools) to keep frequently accessed data in RAM. Use connection pooling to reduce overhead from repeated database connections. Optimize network latency by hosting databases closer to application servers. For example, deploying a database and backend in the same AWS region minimizes round-trip times. Monitor performance with tools like Prometheus or database-specific profilers to detect and address slow queries in real time.