bloom bloom is a contrib which implements index access method. It comes as example of custom access methods and generic WAL records usage. But it is also useful itself. Implementation of Bloom filter allows fast exclusion of non-candidate tuples. Since signature is a lossy representation of all indexed attributes, search results should be rechecked using heap information. User can specify signature length (in uint16, default is 5) and the number of bits, which can be setted, per attribute (1 < colN < 2048). This index is useful if table has many attributes and queries can include their arbitary combinations. Traditional btree index is faster than bloom index, but it'd require too many indexes to support all possible queries, while one need only one bloom index. Bloom index supports only equality comparison. Since it's a signature file, not a tree, it always should be readed fully, but sequentially, so index search performance is constant and doesn't depend on a query. bloom indexes accept following parameters in WITH clause. length Length of signature in uint16 type values col1 — col16 Number of bits for corresponding column Example of index definition is given below. CREATE INDEX bloomidx ON tbloom(i1,i2,i3) WITH (length=5, col1=2, col2=2, col3=4); Here, we create bloom index with signature length 80 bits and attributes i1, i2 mapped to 2 bits, attribute i3 - to 4 bits. Example of index definition and usage is given below. 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) 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) Bloom opclass interface is simple. It requires 1 supporting function: hash function for indexing datatype. And it provides 1 search operator: 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 comes with contrib. However, users may define more of them. Only = operator is supported for search now. But it's possible to add support of arrays with contains and intersection operations in 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