The pg_bigm extension provides fast full-text search functionality using 2-gram (bigram) matching in PostgreSQL databases. It’s particularly useful when you need to perform similarity searches or fuzzy string matching on large text data. Your Nile database arrives with the pg_bigm extension already enabled.

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Understanding pg_bigm

A bigram is a pair of consecutive characters in a string. For example, the word “hello” contains the following bigrams: “he”, “el”, “ll”, “lo”. pg_bigm creates an index of these bigrams, enabling fast similarity searches and partial matching queries.

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Key Features

  • Fast Full-Text Search: Efficient searching using bigram matching
  • Similarity Calculation: Built-in functions to measure string similarity
  • Partial Matching: Find strings containing specific patterns
  • Language Agnostic: Works well with any language, including non-Latin scripts
  • GIN Index Support: Fast search performance using GIN indexes

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Usage Examples

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Creating a Table with Text Search

CREATE TABLE articles (
  tenant_id uuid,
  id integer,
  title text,
  content text,
  PRIMARY KEY (tenant_id, id)
);

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Creating a GIN Index

pg_bigm supports full-text search indexes:

  • gin must be used as an index method. GiST is not available for pg_bigm.
  • gin_bigm_ops must be used as an operator class.
CREATE INDEX articles_content_idx ON articles USING gin (content gin_bigm_ops);

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Inserting Sample Data

-- We need to create a tenant first
INSERT INTO tenants (id, name) VALUES
  ('d1c06023-3421-4fbb-9dd1-c96e42d2fd02', 'Tenant 1');

INSERT INTO articles (tenant_id, id, title, content) VALUES
  ('d1c06023-3421-4fbb-9dd1-c96e42d2fd02', 1, 'PostgreSQL Tutorial', 'Learn about PostgreSQL database management...'),
  ('d1c06023-3421-4fbb-9dd1-c96e42d2fd02', 2, 'Database Design', 'Best practices for designing databases...'),
  ('d1c06023-3421-4fbb-9dd1-c96e42d2fd02', 3, 'Query Optimization', 'Tips for optimizing database queries...');

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Search Examples

Simple partial matching:

SELECT title, content 
FROM articles 
WHERE content LIKE '%database%';

Using similarity search:

SELECT title, content, similarity(content, 'postgresql database') as sim
FROM articles
WHERE content % 'postgresql database'
ORDER BY sim DESC;

Finding similar strings:

SELECT title
FROM articles
WHERE similarity(title, 'PostgeSQL') > 0.3;  -- Will match 'PostgreSQL' despite typo

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Functions and Operators

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Main Functions

  • similarity(text, text): Returns similarity between two strings (0.0 to 1.0)
  • show_bigm(text): Shows all bigrams in a string
  • bigm_similarity_threshold: Sets threshold for % operator (default: 0.3)

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Operators

  • LIKE: Standard pattern matching
  • %: Similarity search operator
  • =~: Regular expression match with bigram index support

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Configuration Parameters

  • pg_bigm.similarity_threshold: Default similarity threshold (0.0 to 1.0)
  • pg_bigm.enable_recheck: Whether to recheck similarity in search results
  • pg_bigm.gin_key_limit: Maximum number of bigrams for GIN index

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Use Cases

  • Fuzzy text search
  • Spell-check functionality
  • Similar content matching
  • Auto-complete suggestions
  • Typo-tolerant search
  • Multi-language text search

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Performance Considerations

  • GIN indexes can be large, plan storage accordingly
  • Index creation might be slow for large tables
  • Index only necessary columns
  • Monitor index size and search performance
  • Adjust similarity threshold to balance precision and recall
  • Consider using pg_bigm.enable_recheck for better accuracy

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Limitations

  • Indexes can be larger compared to traditional B-tree indexes
  • Not suitable for exact matching (use standard indexes instead)
  • May require more memory during search operations
  • Performance depends on similarity threshold and data size

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Additional Resources

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