intarray The intarray module provides a number of useful functions and operators for manipulating null-free arrays of integers. There is also support for indexed searches using some of the operators. All of these operations will throw an error if a supplied array contains any NULL elements. Many of these operations are only sensible for one-dimensional arrays. Although they will accept input arrays of more dimensions, the data is treated as though it were a linear array in storage order. The functions provided by the intarray module are shown in , the operators in . Function Return Type Description Example Result icount(int[])icount int number of elements in array icount('{1,2,3}'::int[]) 3 sort(int[], text dir)sort int[] sort array — dir must be asc or desc sort('{1,2,3}'::int[], 'desc') {3,2,1} sort(int[]) int[] sort in ascending order sort(array[11,77,44]) {11,44,77} sort_asc(int[])sort_asc int[] sort in ascending order sort_desc(int[])sort_desc int[] sort in descending order uniq(int[])uniq int[] remove adjacent duplicates uniq(sort('{1,2,3,2,1}'::int[])) {1,2,3} idx(int[], int item)idx int index of first element matching item (0 if none) idx(array[11,22,33,22,11], 22) 2 subarray(int[], int start, int len)subarray int[] portion of array starting at position start, len elements subarray('{1,2,3,2,1}'::int[], 2, 3) {2,3,2} subarray(int[], int start) int[] portion of array starting at position start subarray('{1,2,3,2,1}'::int[], 2) {2,3,2,1} intset(int)intset int[] make single-element array intset(42) {42} Operator Returns Description int[] && int[] boolean overlap — true if arrays have at least one common element int[] @> int[] boolean contains — true if left array contains right array int[] <@ int[] boolean contained — true if left array is contained in right array # int[] int number of elements in array int[] # int int index (same as idx function) int[] + int int[] push element onto array (add it to end of array) int[] + int[] int[] array concatenation (right array added to the end of left one) int[] - int int[] remove entries matching right argument from array int[] - int[] int[] remove elements of right array from left int[] | int int[] union of arguments int[] | int[] int[] union of arrays int[] & int[] int[] intersection of arrays int[] @@ query_int boolean true if array satisfies query (see below) query_int ~~ int[] boolean true if array satisfies query (commutator of @@) (Before PostgreSQL 8.2, the containment operators @> and <@ were respectively called @ and ~. These names are still available, but are deprecated and will eventually be retired. Notice that the old names are reversed from the convention formerly followed by the core geometric data types!) The operators &&, @> and <@ are equivalent to PostgreSQL's built-in operators of the same names, except that they work only on integer arrays that do not contain nulls, while the built-in operators work for any array type. This restriction makes them faster than the built-in operators in many cases. The @@ and ~~ operators test whether an array satisfies a query, which is expressed as a value of a specialized data type query_int. A query consists of integer values that are checked against the elements of the array, possibly combined using the operators & (AND), | (OR), and ! (NOT). Parentheses can be used as needed. For example, the query 1&(2|3) matches arrays that contain 1 and also contain either 2 or 3. intarray provides index support for the &&, @>, <@, and @@ operators, as well as regular array equality. Two GiST index operator classes are provided: gist__int_ops (used by default) is suitable for small- to medium-size data sets, while gist__intbig_ops uses a larger signature and is more suitable for indexing large data sets (i.e., columns containing a large number of distinct array values). The implementation uses an RD-tree data structure with built-in lossy compression. There is also a non-default GIN operator class gin__int_ops supporting the same operators. The choice between GiST and GIN indexing depends on the relative performance characteristics of GiST and GIN, which are discussed elsewhere. As a rule of thumb, a GIN index is faster to search than a GiST index, but slower to build or update; so GIN is better suited for static data and GiST for often-updated data. -- a message can be in one or more sections CREATE TABLE message (mid INT PRIMARY KEY, sections INT[], ...); -- create specialized index CREATE INDEX message_rdtree_idx ON message USING GIST (sections gist__int_ops); -- select messages in section 1 OR 2 - OVERLAP operator SELECT message.mid FROM message WHERE message.sections && '{1,2}'; -- select messages in sections 1 AND 2 - CONTAINS operator SELECT message.mid FROM message WHERE message.sections @> '{1,2}'; -- the same, using QUERY operator SELECT message.mid FROM message WHERE message.sections @@ '1&2'::query_int; The source directory contrib/intarray/bench contains a benchmark test suite. To run: cd .../bench createdb TEST psql TEST < ../_int.sql ./create_test.pl | psql TEST ./bench.pl The bench.pl script has numerous options, which are displayed when it is run without any arguments. All work was done by Teodor Sigaev (teodor@sigaev.ru) and Oleg Bartunov (oleg@sai.msu.su). See for additional information. Andrey Oktyabrski did a great work on adding new functions and operations.