Database Performance Tuning: Indexing Strategies and Query Optimization Techniques

Database performance tuning minimizes I/O latency and CPU cycles by optimizing data structures and execution paths. Effective tuning reduces table scans, lowers buffer pool churn, and ensures the query optimizer selects efficient execution plans.

Indexing Strategies

Indexes are ordered data structures that enable rapid data retrieval without full table scans. The goal is to maximize index seeks while minimizing scans and lookups.

B-Tree vs. Hash Indexes

B-Tree is the default index structure for most relational databases. It supports equality, range, and sort operations efficiently. Use B-Tree for columns frequently used in WHERE, ORDER BY, and JOIN clauses.

Hash indexes are optimized strictly for equality comparisons. They are faster than B-Tree for point lookups but cannot handle range queries or sorting. Use Hash indexes only for columns accessed via exact matches (=).

Covering Indexes

A covering index includes all columns required by a query (SELECT, JOIN, WHERE) within the index structure itself. This eliminates the "Key Lookup" or "Bookmark Lookup" step where the engine retrieves non-indexed columns from the base table (heap).

Composite Indexes and Column Order

Composite indexes contain multiple columns. The order of columns is critical due to the Left-most Prefix rule. An index on (A, B, C) supports queries on:

• WHERE A = ?
• WHERE A = ? AND B = ?
• WHERE A = ? AND B = ? AND C = ?

It does not efficiently support queries starting with B or C alone. Place columns with highest cardinality (most unique values) or most frequent equality filters first.

Query Optimization Techniques

Query optimization focuses on rewriting SQL to align with how the database engine processes data.

SARGable Predicates

SARGable (Search ARGument ABLE) predicates allow the optimizer to use indexes efficiently. Avoid wrapping indexed columns in functions within the WHERE clause, as this forces a scan.

Non-SARGable:

WHERE YEAR(order_date) = 2026

SARGable:

WHERE order_date >= '2026-01-01' AND order_date < '2027-01-01'

Execution Plan Analysis

Use EXPLAIN ANALYZE to inspect the execution plan. Look for:

• Seq Scan: Indicates a full table scan; usually requires an index.
• Index Scan: Efficient for small range retrievals.
• Index Only Scan: Best case; indicates a covering index usage.
• Hash Join: Efficient for large, unsorted datasets.
• Nested Loop: Efficient for small tables with indexed joins.

Join Optimization

• Nested Loop Join: Best for joining small tables or when the inner table is indexed on the join key.
• Hash Join: Best for large, unsorted datasets where building an in-memory hash table is cheaper than repeated index lookups.
• Merge Join: Best for large, pre-sorted datasets.

Pagination

Avoid OFFSET for deep pagination. As the offset increases, the database must scan and discard rows. Use Keyset Pagination (seek method) instead.

Implementation Examples

Creating a Covering Index

This index supports queries filtering by status and created_at while retrieving email without hitting the table.

CREATE INDEX idx_user_status_created 
ON users (status, created_at) 
INCLUDE (email);

Keyset Pagination

Replace OFFSET with a filter on the indexed column.

-- Inefficient with OFFSET
SELECT * FROM orders ORDER BY id LIMIT 10 OFFSET 1000;

-- Efficient Keyset Pagination
SELECT * FROM orders 
WHERE id > 1000 
ORDER BY id LIMIT 10;

Analyzing a Query

Identify bottlenecks by examining the actual execution time and row counts.

EXPLAIN ANALYZE 
SELECT u.name, o.total 
FROM users u 
JOIN orders o ON u.id = o.user_id 
WHERE u.status = 'active';

Getting Started

1. Identify Slow Queries: Use database monitoring tools or logs to find queries with high duration or CPU usage.
2. Analyze Plans: Run EXPLAIN ANALYZE on top slow queries to identify full table scans or expensive joins.
3. Apply SARGable Rewrites: Refactor WHERE clauses to remove functions on indexed columns.
4. Implement Targeted Indexes: Create composite or covering indexes based on actual query predicates and access patterns.
5. Update Statistics: Ensure database statistics are current after bulk data loads so the optimizer makes informed decisions.