Postgresql similar8/17/2023 There are two tables, the first (“ foo” ) with tens of millions of rows, and the second(“ bar” ) with only about 1600 rows. If the tables contain a lot of rows, then the search time can be quite significant. This is what we thought when we started solving a performance issue for our client. These extensions are included in the standard contrib package and you can easily install them by executing:ĬREATE EXTENSION btree_gin –(or btree_gist) For more effective searching Postgres can mix Btree and GiST/GIN indexes in a general index using the btree_gist / btree_gin extension. GiST/GIN indexes support trigram-based index searches for LIKE, ILIKE, ~ and ~* queries. These indexes can be used with the pg_trgm extension. Of course, we are talking about the GiST and GIN indexes. PostgreSQL has other indexes that can handle these joins. Unfortunately, PostgreSQL does not support such index joins. SELECT * FROM foo WHERE value ILIKE ‘%Windows%XP%’ īut what if we need to join two tables using LIKE? In this case, using B-tree indexes is not possible. But in this case index can be used if the LIKE predicate is a plain text. Postgres can use B-Tree index for like/ilike search only with text_pattern_ops/varchar_pattern_ops operators or “C” COLLATION. This construct lends itself well to indexing. SELECT ‘Microsoft Windows XP’ ILIKE ‘%Windows%XP%’ Slow JOIN using LIKE operatorĪ fairly common situation of using the LIKE/ILIKE operator is a join of two strings when one contains another string. Let’s take a closer look at this below and understand how it lead us to PostgreSQL FTS Part 1. The formula for this issue is simple: LIKE/ILIKE operator + JOIN operator. This issue seemed simple at first glance, but it took a lot of time and research. If max_d is negative then the behavior is the same as levenshtein.In the scope of the migration project from Oracle to PostgreSQL, one of our clients’ DBA team faced a complicated performance issue. If the actual distance is less than or equal to max_d, then levenshtein_less_equal returns the correct distance otherwise it returns some value greater than max_d. Levenshtein_less_equal is an accelerated version of the Levenshtein function for use when only small distances are of interest. You can omit the cost parameters, as in the second version of the function in that case they all default to 1. The cost parameters specify how much to charge for a character insertion, deletion, or substitution, respectively. Levenshtein_less_equal(text source, text target, int max_d) returns intīoth source and target can be any non-null string, with a maximum of 255 characters. Levenshtein_less_equal(text source, text target, int ins_cost, int del_cost, int sub_cost, int max_d) returns int ![]() Levenshtein(text source, text target) returns int Levenshtein(text source, text target, int ins_cost, int del_cost, int sub_cost) returns int This function calculates the Levenshtein distance between two strings: SELECT * FROM s WHERE difference(s.nm, 'john') > 2 SELECT * FROM s WHERE soundex(nm) = soundex('john') SELECT soundex('Anne'), soundex('Margaret'), difference('Anne', 'Margaret') SELECT soundex('Anne'), soundex('Andrew'), difference('Anne', 'Andrew') SELECT soundex('Anne'), soundex('Ann'), difference('Anne', 'Ann') (Thus, the function is misnamed - similarity would have been a better name.) Since Soundex codes have four characters, the result ranges from zero to four, with zero being no match and four being an exact match. The difference function converts two strings to their Soundex codes and then reports the number of matching code positions. ![]() The soundex function converts a string to its Soundex code. The fuzzystrmatch module provides two functions for working with Soundex codes: Note that Soundex is not very useful for non-English names. ![]() It was initially used by the United States Census in 1880, 1900, and 1910. The Soundex system is a method of matching similar-sounding names by converting them to the same code.
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