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Advanced 22 min readModule: Module 9: Indexing Strategies (B-Tree, Hash, GIN) & EXPLAIN

B-Tree Indexes & Query Execution Plans (EXPLAIN ANALYZE)

Accelerate query performance from seconds to sub-milliseconds using B-Tree indexes and reading EXPLAIN plans.

What You Will Learn in This Lesson

  • How B-Tree indexes enable O(log n) binary search lookup on disk
  • Composite multi-column indexes and the left-prefix rule
  • Reading EXPLAIN ANALYZE output: Seq Scan vs Index Scan vs Bitmap Heap Scan

Introduction & Core Concept

An index is a separate data structure on disk (most commonly a balanced B-Tree) that speeds up the retrieval of rows by column value, at the cost of additional write overhead.
WHY DOES THIS MATTER IN THE REAL WORLD?

On a 10,000,000 row table, an unindexed query performs a sequential scan reading 2GB of disk. An index scan touches only 4 tree pages in <1ms.

Index Creation & EXPLAIN Plan

sql
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-- Create Composite Index
CREATE INDEX idx_orders_customer_status ON orders(customer_id, status);
 
-- Inspect query execution plan
EXPLAIN ANALYZE
SELECT * FROM orders
WHERE customer_id = 'CUST-001' AND status = 'COMPLETED';

Line-by-Line Technical Breakdown

1Indexes speed up SELECT queries but slightly slow down INSERT, UPDATE, and DELETE operations.

Try It Yourself (Interactive Editor)

Modify the code in real-time and click Run to test live browser output and console logs.

Intelligent Code Runner & Live Sandbox[SQL]
SQL SOURCE EDITOR
Interactive Live Code

Industry Best Practices & Professional Standards

  • Index columns frequently used in WHERE filters, JOIN ON keys, and ORDER BY clauses.

Lesson Summary & Core Takeaways

  • Proper indexing is the #1 factor in database scalability and throughput.