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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>F.7. bloom — bloom filter index access method</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="basic-archive.html" title="F.6. basic_archive — an example WAL archive module" /><link rel="next" href="btree-gin.html" title="F.8. btree_gin — GIN operator classes with B-tree behavior" /></head><body id="docContent" class="container-fluid col-10"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">F.7. bloom — bloom filter index access method</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="basic-archive.html" title="F.6. basic_archive — an example WAL archive module">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="contrib.html" title="Appendix F. Additional Supplied Modules and Extensions">Up</a></td><th width="60%" align="center">Appendix F. Additional Supplied Modules and Extensions</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 16.3 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="btree-gin.html" title="F.8. btree_gin — GIN operator classes with B-tree behavior">Next</a></td></tr></table><hr /></div><div class="sect1" id="BLOOM"><div class="titlepage"><div><div><h2 class="title" style="clear: both">F.7. bloom — bloom filter index access method <a href="#BLOOM" class="id_link">#</a></h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="bloom.html#BLOOM-PARAMETERS">F.7.1. Parameters</a></span></dt><dt><span class="sect2"><a href="bloom.html#BLOOM-EXAMPLES">F.7.2. Examples</a></span></dt><dt><span class="sect2"><a href="bloom.html#BLOOM-OPERATOR-CLASS-INTERFACE">F.7.3. Operator Class Interface</a></span></dt><dt><span class="sect2"><a href="bloom.html#BLOOM-LIMITATIONS">F.7.4. Limitations</a></span></dt><dt><span class="sect2"><a href="bloom.html#BLOOM-AUTHORS">F.7.5. Authors</a></span></dt></dl></div><a id="id-1.11.7.17.2" class="indexterm"></a><p>
<code class="literal">bloom</code> provides an index access method based on
<a class="ulink" href="https://en.wikipedia.org/wiki/Bloom_filter" target="_top">Bloom filters</a>.
</p><p>
A Bloom filter is a space-efficient data structure that is used to test
whether an element is a member of a set. In the case of an index access
method, it allows fast exclusion of non-matching tuples via signatures
whose size is determined at index creation.
</p><p>
A signature is a lossy representation of the indexed attribute(s), and as
such is prone to reporting false positives; that is, it may be reported
that an element is in the set, when it is not. So index search results
must always be rechecked using the actual attribute values from the heap
entry. Larger signatures reduce the odds of a false positive and thus
reduce the number of useless heap visits, but of course also make the index
larger and hence slower to scan.
</p><p>
This type of index is most useful when a table has many attributes and
queries test arbitrary combinations of them. A traditional btree index is
faster than a bloom index, but it can require many btree indexes to support
all possible queries where one needs only a single bloom index. Note
however that bloom indexes only support equality queries, whereas btree
indexes can also perform inequality and range searches.
</p><div class="sect2" id="BLOOM-PARAMETERS"><div class="titlepage"><div><div><h3 class="title">F.7.1. Parameters <a href="#BLOOM-PARAMETERS" class="id_link">#</a></h3></div></div></div><p>
A <code class="literal">bloom</code> index accepts the following parameters in its
<code class="literal">WITH</code> clause:
</p><div class="variablelist"><dl class="variablelist"><dt><span class="term"><code class="literal">length</code></span></dt><dd><p>
Length of each signature (index entry) in bits. It is rounded up to the
nearest multiple of <code class="literal">16</code>. The default is
<code class="literal">80</code> bits and the maximum is <code class="literal">4096</code>.
</p></dd></dl></div><div class="variablelist"><dl class="variablelist"><dt><span class="term"><code class="literal">col1 — col32</code></span></dt><dd><p>
Number of bits generated for each index column. Each parameter's name
refers to the number of the index column that it controls. The default
is <code class="literal">2</code> bits and the maximum is <code class="literal">4095</code>.
Parameters for index columns not actually used are ignored.
</p></dd></dl></div></div><div class="sect2" id="BLOOM-EXAMPLES"><div class="titlepage"><div><div><h3 class="title">F.7.2. Examples <a href="#BLOOM-EXAMPLES" class="id_link">#</a></h3></div></div></div><p>
This is an example of creating a bloom index:
</p><pre class="programlisting">
CREATE INDEX bloomidx ON tbloom USING bloom (i1,i2,i3)
WITH (length=80, col1=2, col2=2, col3=4);
</pre><p>
The index is created with a signature length of 80 bits, with attributes
i1 and i2 mapped to 2 bits, and attribute i3 mapped to 4 bits. We could
have omitted the <code class="literal">length</code>, <code class="literal">col1</code>,
and <code class="literal">col2</code> specifications since those have the default values.
</p><p>
Here is a more complete example of bloom index definition and usage, as
well as a comparison with equivalent btree indexes. The bloom index is
considerably smaller than the btree index, and can perform better.
</p><pre class="programlisting">
=# CREATE TABLE tbloom AS
SELECT
(random() * 1000000)::int as i1,
(random() * 1000000)::int as i2,
(random() * 1000000)::int as i3,
(random() * 1000000)::int as i4,
(random() * 1000000)::int as i5,
(random() * 1000000)::int as i6
FROM
generate_series(1,10000000);
SELECT 10000000
</pre><p>
A sequential scan over this large table takes a long time:
</p><pre class="programlisting">
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
------------------------------------------------------------------------------------------------------
Seq Scan on tbloom (cost=0.00..2137.14 rows=3 width=24) (actual time=16.971..16.971 rows=0 loops=1)
Filter: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Filter: 100000
Planning Time: 0.346 ms
Execution Time: 16.988 ms
(5 rows)
</pre><p>
</p><p>
Even with the btree index defined the result will still be a
sequential scan:
</p><pre class="programlisting">
=# CREATE INDEX btreeidx ON tbloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('btreeidx'));
pg_size_pretty
----------------
3976 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
------------------------------------------------------------------------------------------------------
Seq Scan on tbloom (cost=0.00..2137.00 rows=2 width=24) (actual time=12.805..12.805 rows=0 loops=1)
Filter: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Filter: 100000
Planning Time: 0.138 ms
Execution Time: 12.817 ms
(5 rows)
</pre><p>
</p><p>
Having the bloom index defined on the table is better than btree in
handling this type of search:
</p><pre class="programlisting">
=# CREATE INDEX bloomidx ON tbloom USING bloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('bloomidx'));
pg_size_pretty
----------------
1584 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on tbloom (cost=1792.00..1799.69 rows=2 width=24) (actual time=0.388..0.388 rows=0 loops=1)
Recheck Cond: ((i2 = 898732) AND (i5 = 123451))
Rows Removed by Index Recheck: 29
Heap Blocks: exact=28
-> Bitmap Index Scan on bloomidx (cost=0.00..1792.00 rows=2 width=0) (actual time=0.356..0.356 rows=29 loops=1)
Index Cond: ((i2 = 898732) AND (i5 = 123451))
Planning Time: 0.099 ms
Execution Time: 0.408 ms
(8 rows)
</pre><p>
</p><p>
Now, the main problem with the btree search is that btree is inefficient
when the search conditions do not constrain the leading index column(s).
A better strategy for btree is to create a separate index on each column.
Then the planner will choose something like this:
</p><pre class="programlisting">
=# CREATE INDEX btreeidx1 ON tbloom (i1);
CREATE INDEX
=# CREATE INDEX btreeidx2 ON tbloom (i2);
CREATE INDEX
=# CREATE INDEX btreeidx3 ON tbloom (i3);
CREATE INDEX
=# CREATE INDEX btreeidx4 ON tbloom (i4);
CREATE INDEX
=# CREATE INDEX btreeidx5 ON tbloom (i5);
CREATE INDEX
=# CREATE INDEX btreeidx6 ON tbloom (i6);
CREATE INDEX
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on tbloom (cost=24.34..32.03 rows=2 width=24) (actual time=0.028..0.029 rows=0 loops=1)
Recheck Cond: ((i5 = 123451) AND (i2 = 898732))
-> BitmapAnd (cost=24.34..24.34 rows=2 width=0) (actual time=0.027..0.027 rows=0 loops=1)
-> Bitmap Index Scan on btreeidx5 (cost=0.00..12.04 rows=500 width=0) (actual time=0.026..0.026 rows=0 loops=1)
Index Cond: (i5 = 123451)
-> Bitmap Index Scan on btreeidx2 (cost=0.00..12.04 rows=500 width=0) (never executed)
Index Cond: (i2 = 898732)
Planning Time: 0.491 ms
Execution Time: 0.055 ms
(9 rows)
</pre><p>
Although this query runs much faster than with either of the single
indexes, we pay a penalty in index size. Each of the single-column
btree indexes occupies 2 MB, so the total space needed is 12 MB,
eight times the space used by the bloom index.
</p></div><div class="sect2" id="BLOOM-OPERATOR-CLASS-INTERFACE"><div class="titlepage"><div><div><h3 class="title">F.7.3. Operator Class Interface <a href="#BLOOM-OPERATOR-CLASS-INTERFACE" class="id_link">#</a></h3></div></div></div><p>
An operator class for bloom indexes requires only a hash function for the
indexed data type and an equality operator for searching. This example
shows the operator class definition for the <code class="type">text</code> data type:
</p><pre class="programlisting">
CREATE OPERATOR CLASS text_ops
DEFAULT FOR TYPE text USING bloom AS
OPERATOR 1 =(text, text),
FUNCTION 1 hashtext(text);
</pre></div><div class="sect2" id="BLOOM-LIMITATIONS"><div class="titlepage"><div><div><h3 class="title">F.7.4. Limitations <a href="#BLOOM-LIMITATIONS" class="id_link">#</a></h3></div></div></div><p>
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
Only operator classes for <code class="type">int4</code> and <code class="type">text</code> are
included with the module.
</p></li><li class="listitem"><p>
Only the <code class="literal">=</code> operator is supported for search. But
it is possible to add support for arrays with union and intersection
operations in the future.
</p></li><li class="listitem"><p>
<code class="literal">bloom</code> access method doesn't support
<code class="literal">UNIQUE</code> indexes.
</p></li><li class="listitem"><p>
<code class="literal">bloom</code> access method doesn't support searching for
<code class="literal">NULL</code> values.
</p></li></ul></div><p>
</p></div><div class="sect2" id="BLOOM-AUTHORS"><div class="titlepage"><div><div><h3 class="title">F.7.5. Authors <a href="#BLOOM-AUTHORS" class="id_link">#</a></h3></div></div></div><p>
Teodor Sigaev <code class="email"><<a class="email" href="mailto:teodor@postgrespro.ru">teodor@postgrespro.ru</a>></code>,
Postgres Professional, Moscow, Russia
</p><p>
Alexander Korotkov <code class="email"><<a class="email" href="mailto:a.korotkov@postgrespro.ru">a.korotkov@postgrespro.ru</a>></code>,
Postgres Professional, Moscow, Russia
</p><p>
Oleg Bartunov <code class="email"><<a class="email" href="mailto:obartunov@postgrespro.ru">obartunov@postgrespro.ru</a>></code>,
Postgres Professional, Moscow, Russia
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