Strategic Index Selection and Design

Building on PostgreSQL Index Access Methods Explained, this lesson dives into how to pick the right index for real‑world workloads and how to design indexes that both speed reads and keep write overhead low.

Matching Index Types to Query Patterns

  1. B‑tree – default choice for equality and range predicates on scalar columns. Example: CREATE INDEX ON orders (order_date); supports WHERE order_date BETWEEN '2024-01-01' AND '2024-01-31'.
  2. GIN – ideal for columns that store multiple values, such as jsonb or arrays. A common pattern is WHERE tags @> '{"postgres"}'. Creating it with CREATE INDEX ON articles USING GIN (tags); lets a single row contribute many index entries.
  3. GiST – used for geometric data and overlap queries, e.g., WHERE geom && ST_MakeEnvelope(... ).
  4. BRIN – perfect for huge, append‑only tables where rows are naturally ordered. CREATE INDEX ON sensor_log USING BRIN (timestamp); can shrink index size to a few megabytes even when the table holds billions of rows.

Designing Composite, Covering, and Partial Indexes

  • Composite (multi‑column) indexes obey the left‑most prefix rule. If queries filter on country then city, the index should be (country, city). Reversing the order would make the city predicate un‑usable for index scans.
  • Covering indexes add non‑key columns with INCLUDE. For a frequent lookup SELECT id, status FROM jobs WHERE id = $1; the index CREATE INDEX ON jobs (id) INCLUDE (status); enables an index‑only scan, eliminating heap access.
  • Partial indexes limit entries to rows that actually appear in queries. If only active users are queried, CREATE INDEX ON users (last_login) WHERE active = true; reduces index size dramatically and cuts write cost.

Step‑by‑Step Index Evaluation

  1. Identify the dominant WHERE clause columns.
  2. Determine cardinality: high‑cardinality columns benefit most from B‑tree or GIN; low‑cardinality (e.g., boolean) usually do not need an index.
  3. Choose the access method based on data type and query shape.
  4. Decide on composite vs single‑column, and whether INCLUDE or WHERE clauses add value.
  5. Test with EXPLAIN (ANALYZE, BUFFERS) and compare query latency before and after.

In the next lesson, Optimizing with Advanced and Specialized Indexes, we will explore expression indexes, hash‑based partition pruning, and how to combine multiple index types for complex analytical workloads.