Exploring Index Types and Storage Methods in PostgreSQL


3 min read

PostgreSQL, like many relational database systems, uses a variety of techniques to store data efficiently and to provide fast access to data through indexing. Here’s a detailed explanation of how PostgreSQL handles storage and indexing of tables:

Data Storage in PostgreSQL

  1. Tables and TOAST (The Oversized-Attribute Storage Technique):

    • Table Structure: PostgreSQL stores table data in a format known as a heap. Each table's heap contains one or more pages (blocks), where each page is typically 8KB in size. This size can be altered when compiling PostgreSQL from source.

    • Rows and Pages: Each row of a table is stored in one of these pages. If a row is too large to fit into a single page, PostgreSQL uses TOAST to handle it. TOAST automatically compresses and stores large field values (such as long text or binary data) in a secondary storage area, and keeps pointers in the main table—significantly optimizing the storage of large fields.

  2. MVCC (Multi-Version Concurrency Control):

    • Row Versions: PostgreSQL implements MVCC to handle data consistency and concurrency. MVCC allows each transaction to see a snapshot of the database, providing transaction isolation and enabling long-running queries to view consistent data without locks.

    • XID (Transaction IDs): Each row version has associated XID stamps that determine during which transactions the row version was valid, aiding in concurrency and recovery.

Indexing in PostgreSQL

PostgreSQL provides several index types, each suitable for different kinds of queries:

  1. B-tree Indexes:

    • Default and General Purpose: B-tree indexes are the default and are highly effective for a variety of data types and queries, particularly for equality and range queries.

    • Structure: B-trees organize data in a balanced tree structure that enables quick searches, inserts, deletions, and sequential access.

  2. Hash Indexes:

    • Use Case: Effective for simple equality comparisons. Hash indexes provide faster lookups than B-trees for these cases, but they do not support ordering.

    • Structure: Data is stored in a hash table format, mapping hash values to table rows.

  3. GiST (Generalized Search Tree) Indexes:

    • Flexible Indexing: Allows for building general, balanced tree structures. It supports a variety of data types and is extensible.

    • Applications: Useful for indexing geometric data, text, full-text search, and more.

  4. GIN (Generalized Inverted Index) Indexes:

    • Designed For: Optimized for handling data types that contain multiple values per row such as arrays and JSONB.

    • Structure: Similar to an inverted index, efficient at handling searches within composite values (like searching for an element in an array).

  5. BRIN (Block Range Index) Indexes:

    • Purpose: Designed for very large tables where values are naturally correlated with their physical locations in the database.

    • Efficiency: Stores summaries of physical blocks of table data rather than individual row data, which is highly space-efficient for large datasets.

Performance Considerations

  • Write Performance: Indexes can impact write performance because each INSERT, UPDATE, or DELETE in a table may require corresponding updates to indexes. The choice and number of indexes should balance read performance benefits against write performance costs.

  • Maintenance: PostgreSQL periodically requires index maintenance (REINDEX) especially after extensive changes to the table data, which can fragment the index leading to degraded performance.