bloom bloom is a module which implements an index access method. It comes as an example of custom access methods and generic WAL records usage. But it is also useful in itself. The implementation of a Bloom filter allows fast exclusion of non-candidate tuples via signatures. Since a signature is a lossy representation of all indexed attributes, search results must be rechecked using heap information. The user can specify signature length (in uint16, default is 5) and the number of bits, which can be set per attribute (1 < colN < 2048). This index is useful if a table has many attributes and queries include arbitrary combinations of them. A traditional btree index is faster than a bloom index, but it can require many indexes to support all possible queries where one needs only a single bloom index. A Bloom index supports only equality comparison. Since it's a signature file, and not a tree, it always must be read fully, but sequentially, so that index search performance is constant and doesn't depend on a query. bloom indexes accept the following parameters in the WITH clause. length Length of signature in uint16 type values col1 — col16 Number of bits for corresponding column An example of an index definition is given below. CREATE INDEX bloomidx ON tbloom USING bloom (i1,i2,i3) WITH (length=5, col1=2, col2=2, col3=4); Here, we created a bloom index with a signature length of 80 bits, and attributes i1 and i2 mapped to 2 bits, and attribute i3 to 4 bits. Here is a fuller example of index definition and usage: CREATE TABLE tbloom AS SELECT random()::int as i1, random()::int as i2, random()::int as i3, random()::int as i4, random()::int as i5, random()::int as i6, random()::int as i7, random()::int as i8, random()::int as i9, random()::int as i10, random()::int as i11, random()::int as i12, random()::int as i13 FROM generate_series(1,1000); CREATE INDEX bloomidx ON tbloom USING bloom (i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12); SELECT pg_relation_size('bloomidx'); CREATE index btree_idx ON tbloom(i1,i2,i3,i4,i5,i6,i7,i8,i9,i10,i11,i12); SELECT pg_relation_size('btree_idx'); =# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 20 AND i10 = 15; QUERY PLAN ----------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on tbloom (cost=1.50..5.52 rows=1 width=52) (actual time=0.057..0.057 rows=0 loops=1) Recheck Cond: ((i2 = 20) AND (i10 = 15)) -> Bitmap Index Scan on bloomidx (cost=0.00..1.50 rows=1 width=0) (actual time=0.041..0.041 rows=9 loops=1) Index Cond: ((i2 = 20) AND (i10 = 15)) Total runtime: 0.081 ms (5 rows) Seqscan is slow. =# SET enable_bitmapscan = off; =# SET enable_indexscan = off; =# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 20 AND i10 = 15; QUERY PLAN -------------------------------------------------------------------------------------------------- Seq Scan on tbloom (cost=0.00..25.00 rows=1 width=52) (actual time=0.162..0.162 rows=0 loops=1) Filter: ((i2 = 20) AND (i10 = 15)) Total runtime: 0.181 ms (3 rows) A btree index will be not used for this query. =# DROP INDEX bloomidx; =# CREATE INDEX btree_idx ON tbloom(i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12); =# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 20 AND i10 = 15; QUERY PLAN -------------------------------------------------------------------------------------------------- Seq Scan on tbloom (cost=0.00..25.00 rows=1 width=52) (actual time=0.210..0.210 rows=0 loops=1) Filter: ((i2 = 20) AND (i10 = 15)) Total runtime: 0.250 ms (3 rows) The Bloom opclass interface is simple. It requires 1 supporting function: a hash function for the indexing datatype. It provides 1 search operator: the equality operator. The example below shows opclass definition for text datatype. CREATE OPERATOR CLASS text_ops DEFAULT FOR TYPE text USING bloom AS OPERATOR 1 =(text, text), FUNCTION 1 hashtext(text); For now, only opclasses for int4, text come with the module. However, users may define more of them. Only the = operator is supported for search at the moment. But it's possible to add support for arrays with contains and intersection operations in the future. Teodor Sigaev teodor@postgrespro.ru, Postgres Professional, Moscow, Russia Alexander Korotkov a.korotkov@postgrespro.ru, Postgres Professional, Moscow, Russia Oleg Bartunov obartunov@postgrespro.ru, Postgres Professional, Moscow, Russia