Finding similar strings with PostgreSQL quickly

sql

join

performance-boosting

index-maintenance

byNikita Barsukov·Dec 14, 2024

For enhanced PostgreSQL string search efficiency, employ the pg_trgm extension known for trigram-based similarity. The command to activate is:

CREATE EXTENSION pg_trgm;

To fetch similar strings promptly, employ the similarity operator or %:

SELECT * FROM your_table WHERE your_column % 'your_search_term';

For efficient performance, use a GIN trigram index for the column in question:

CREATE INDEX your_index_name ON your_table USING GIN (your_column gin_trgm_ops);

The setup filters similar text speedily by trigram overlap, ensuring optimized query times.

Demystifying `pg_trgm.similarity_threshold`

pg_trgm.similarity_threshold allows you to modify similarity score preference, hence fine-tuning the dataset for accuracy and precision. Query below sets the threshold to 0.8:

SELECT SETVAL(pg_trgm.similarity_threshold, 0.8);

Here be no substandard similarities! Shoo!

Making the right index choice

Flexibility is key while working with GiST indexes. Choose GiST when you prefer a balance between write and read operations. Also, these indexes are lossy, which essentially means, sacrificing the detail level for gaining quicker writes but slightly slower reads.

Efficient query execution

To avoid running expensive functions against your data multiple times, do not refer to output columns within WHERE or HAVING clauses. Go for subqueries or common table expressions (CTEs) to optimize query function.

In my expression, I'm always common but never ordinary.

Prepping prior to joining

Before executing a cross join, think of preconditions as a sieve that refines your dataset, especially while managing a multitude of string matches.

Exploring Index options

GIN: When to use?

Working with large, dense datasets.
Requirements for fast read operations.
Space is less of a concern, performance is king.

GiST: Makes sense, when?

Requirement for balanced read-write operations.
Handling geometric data or full-text search.
Quick index build time and space-saving tops your priority list.

Avoiding voluminous joins

Beware of the cross join, as it's capable of generating enormous row counts. Instead, work with LIMIT, OFFSET or a well-placed WHERE clause to filter the rows before hitting the join.

Bringing % operator into play

The operator % is a phenomenal tool to find similar strings but it's binary in nature. As a tool, it relies on the pg_trgm.similarity_threshold to make the decision on similarity.

Performance-boosting techniques with pg_trgm module

Filtering pairs wisely

Minimize the row set before performing similarity comparisons. Achieve this by filtering on indexed columns or pre-calculated metrics.

Maintaining Index regularly

While GIN indexes enhance read performance, they can increase in size and need regular maintenance. Regularly reindex and vacuum your indexes to keep them efficient.

Run, Forrest, run! No, wait...Rerun, Index, rerun!