提交 0ca9907c 编写于 作者: T Teodor Sigaev

GIN documentation and slightly improving GiST docs.

Thanks to  Christopher Kings-Lynne <chris.kingslynne@gmail.com> for
initial version and Jeff Davis <pgsql@j-davis.com> for inspection
上级 4eef745f
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<chapter Id="runtime-config">
<title>Server Configuration</title>
......@@ -2172,7 +2172,20 @@ SELECT * FROM parent WHERE key = 2400;
</para>
</listitem>
</varlistentry>
<varlistentry id="guc-gin-fuzzy-search-limit" xreflabel="gin_fuzzy_search_limit">
<term><varname>gin_fuzzy_search_limit</varname> (<type>integer</type>)</term>
<indexterm>
<primary><varname>gin_fuzzy_search_limit</> configuration parameter</primary>
</indexterm>
<listitem>
<para>
Soft upper limit of the size of the returned set by GIN index. For more
information see <xref linkend="gin-tips">.
</para>
</listitem>
</varlistentry>
</variablelist>
</sect2>
</sect1>
......
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<!entity history SYSTEM "history.sgml">
<!entity info SYSTEM "info.sgml">
......@@ -78,6 +78,7 @@
<!entity catalogs SYSTEM "catalogs.sgml">
<!entity geqo SYSTEM "geqo.sgml">
<!entity gist SYSTEM "gist.sgml">
<!entity gin SYSTEM "gin.sgml">
<!entity planstats SYSTEM "planstats.sgml">
<!entity indexam SYSTEM "indexam.sgml">
<!entity nls SYSTEM "nls.sgml">
......
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<chapter id="geqo">
<chapterinfo>
......@@ -49,8 +49,8 @@
methods</firstterm> (e.g., nested loop, hash join, merge join in
<productname>PostgreSQL</productname>) to process individual joins
and a diversity of <firstterm>indexes</firstterm> (e.g.,
B-tree, hash, GiST in <productname>PostgreSQL</productname>) as access
paths for relations.
B-tree, hash, GiST and GIN in <productname>PostgreSQL</productname>) as
access paths for relations.
</para>
<para>
......
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<chapter id="GIN">
<title>GIN Indexes</title>
<indexterm>
<primary>index</primary>
<secondary>GIN</secondary>
</indexterm>
<sect1 id="gin-intro">
<title>Introduction</title>
<para>
<acronym>GIN</acronym> stands for Generalized Inverted Index. It is
an index structure storing a set of (key, posting list) pairs, where
'posting list' is a set of rows in which the key occurs. The
row may contain many keys.
</para>
<para>
It is generalized in the sense that a <acronym>GIN</acronym> index
does not need to be aware of the operation that it accelerates.
Instead, it uses custom strategies defined for particular data types.
</para>
<para>
One advantage of <acronym>GIN</acronym> is that it allows the development
of custom data types with the appropriate access methods, by
an expert in the domain of the data type, rather than a database expert.
This is much the same advantage as using <acronym>GiST</acronym>.
</para>
<para>
The <acronym>GIN</acronym>
implementation in <productname>PostgreSQL</productname> is primarily
maintained by Teodor Sigaev and Oleg Bartunov, and there is more
information on their
<ulink url="http://www.sai.msu.su/~megera/oddmuse/index.cgi/Gin">website</ulink>.
</para>
</sect1>
<sect1 id="gin-extensibility">
<title>Extensibility</title>
<para>
The <acronym>GIN</acronym> interface has a high level of abstraction,
requiring the access method implementer to only implement the semantics of
the data type being accessed. The <acronym>GIN</acronym> layer itself
takes care of concurrency, logging and searching the tree structure.
</para>
<para>
All it takes to get a <acronym>GIN</acronym> access method working
is to implement four user-defined methods, which define the behavior of
keys in the tree. In short, <acronym>GIN</acronym> combines extensibility
along with generality, code reuse, and a clean interface.
</para>
</sect1>
<sect1 id="gin-implementation">
<title>Implementation</title>
<para>
Internally, <acronym>GIN</acronym> consists of a B-tree index constructed
over keys, where each key is an element of the indexed value
(element of array, for example) and where each tuple in a leaf page is
either a pointer to a B-tree over heap pointers (PT, posting tree), or a
list of heap pointers (PL, posting list) if the tuple is small enough.
</para>
<para>
There are four methods that an index operator class for
<acronym>GIN</acronym> must provide (prototypes are in pseudocode):
</para>
<variablelist>
<varlistentry>
<term>int compare( Datum a, Datum b )</term>
<listitem>
<para>
Compares keys (not indexed values!) and returns an integer less than
zero, zero, or greater than zero, indicating whether the first key is
less than, equal to, or greater than the second.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Datum* extractValue(Datum inputValue, uint32 *nkeys)</term>
<listitem>
<para>
Returns an array of keys of value to be indexed, nkeys should
contain the number of returned keys.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Datum* extractQuery(Datum query, uint32 nkeys,
StrategyNumber n)</term>
<listitem>
<para>
Returns an array of keys of the query to be executed. n contains
strategy number of operation (see <xref linkend="xindex-strategies">).
Depending on n, query may be different type.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>bool consistent( bool check[], StrategyNumber n, Datum query)</term>
<listitem>
<para>
Returns TRUE if indexed value satisfies query qualifier with strategy n
(or may satisfy in case of RECHECK mark in operator class).
Each element of the check array is TRUE if indexed value has a
corresponding key in the query: if (check[i] == TRUE ) the i-th key of
the query is present in the indexed value.
</para>
</listitem>
</varlistentry>
</variablelist>
</sect1>
<sect1 id="gin-tips">
<title>GIN tips and trics</title>
<variablelist>
<varlistentry>
<term>Create vs insert</term>
<listitem>
<para>
In most cases, insertion into <acronym>GIN</acronym> index is slow because
many GIN keys may be inserted for each table row. So, when loading data
in bulk it may be useful to drop index and recreate it
after the data is loaded in the table.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>gin_fuzzy_search_limit</term>
<listitem>
<para>
The primary goal of development <acronym>GIN</acronym> indices was
support for highly scalable, full-text search in
<productname>PostgreSQL</productname> and there are often situations when
a full-text search returns a very large set of results. Since reading
tuples from the disk and sorting them could take a lot of time, this is
unacceptable for production. (Note that the index search itself is very
fast.)
</para>
<para>
Such queries usually contain very frequent words, so the results are not
very helpful. To facilitate execution of such queries
<acronym>GIN</acronym> has a configurable soft upper limit of the size
of the returned set, determined by the
<varname>gin_fuzzy_search_limit</varname> GUC variable. It is set to 0 by
default (no limit).
</para>
<para>
If a non-zero search limit is set, then the returned set is a subset of
the whole result set, chosen at random.
</para>
<para>
"Soft" means that the actual number of returned results could slightly
differ from the specified limit, depending on the query and the quality
of the system's random number generator.
</para>
</listitem>
</varlistentry>
<variablelist>
</sect1>
<sect1 id="gin-limit">
<title>Limitations</title>
<para>
<acronym>GIN</acronym> doesn't support full scan of index due to it's
extremely inefficiency: because of a lot of keys per value,
each heap pointer will returned several times.
</para>
<para>
When extractQuery returns zero number of keys, <acronym>GIN</acronym> will
emit a error: for different opclass and strategy semantic meaning of void
query may be different (for example, any array contains void array,
but they aren't overlapped with void one), and <acronym>GIN</acronym> can't
suggest reasonable answer.
</para>
<para>
<acronym>GIN</acronym> searches keys only by equality matching. This may
be improved in future.
</para>
</sect1>
<sect1 id="gin-examples">
<title>Examples</title>
<para>
The <productname>PostgreSQL</productname> source distribution includes
<acronym>GIN</acronym> classes for one-dimensional arrays of all internal
types. The following
<filename>contrib</> modules also contain <acronym>GIN</acronym>
operator classes:
</para>
<variablelist>
<varlistentry>
<term>intarray</term>
<listitem>
<para>Enhanced support for int4[]</para>
</listitem>
</varlistentry>
<varlistentry>
<term>tsearch2</term>
<listitem>
<para>Support for inverted text indexing. This is much faster for very
large, mostly-static sets of documents.
</para>
</listitem>
</varlistentry>
</chapter>
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<chapter id="indexes">
<title id="indexes-title">Indexes</title>
......@@ -116,7 +116,7 @@ CREATE INDEX test1_id_index ON test1 (id);
<para>
<productname>PostgreSQL</productname> provides several index types:
B-tree, Hash, and GiST. Each index type uses a different
B-tree, Hash, GIN and GiST. Each index type uses a different
algorithm that is best suited to different types of queries.
By default, the <command>CREATE INDEX</command> command will create a
B-tree index, which fits the most common situations.
......@@ -238,6 +238,37 @@ CREATE INDEX <replaceable>name</replaceable> ON <replaceable>table</replaceable>
classes are available in the <literal>contrib</> collection or as separate
projects. For more information see <xref linkend="GiST">.
</para>
<para>
<indexterm>
<primary>index</primary>
<secondary>GIN</secondary>
</indexterm>
<indexterm>
<primary>GIN</primary>
<see>index</see>
</indexterm>
GIN is a inverted index and it's usable for values which have more
than one key, arrays for example. Like to GiST, GIN may support
many different user-defined indexing strategies and the particular
operators with which a GIN index can be used vary depending on the
indexing strategy.
As an example, the standard distribution of
<productname>PostgreSQL</productname> includes GIN operator classes
for one-dimentional arrays, which support indexed
queries using these operators:
<simplelist>
<member><literal>&lt;@</literal></member>
<member><literal>@&gt;</literal></member>
<member><literal>=</literal></member>
<member><literal>&amp;&amp;</literal></member>
</simplelist>
(See <xref linkend="functions-array"> for the meaning of
these operators.)
Another GIN operator classes are available in the <literal>contrib</>
tsearch2 and intarray modules. For more information see <xref linkend="GIN">.
</para>
</sect1>
......
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<chapter id="mvcc">
<title>Concurrency Control</title>
......@@ -987,6 +987,20 @@ UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<acronym>GIN</acronym> indexes
</term>
<listitem>
<para>
Short-term share/exclusive page-level locks are used for
read/write access. Locks are released immediately after each
index row is fetched or inserted. However, note that GIN index
usually requires several inserts per one table row.
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
......@@ -995,7 +1009,7 @@ UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;
applications; since they also have more features than hash
indexes, they are the recommended index type for concurrent
applications that need to index scalar data. When dealing with
non-scalar data, B-trees are not useful, and GiST indexes should
non-scalar data, B-trees are not useful, and GiST or GIN indexes should
be used instead.
</para>
</sect1>
......
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PostgreSQL documentation
-->
......@@ -192,7 +192,7 @@ CREATE OPERATOR CLASS <replaceable class="parameter">name</replaceable> [ DEFAUL
<para>
The data type actually stored in the index. Normally this is
the same as the column data type, but some index methods
(only GiST at this writing) allow it to be different. The
(GIN and GiST for now) allow it to be different. The
<literal>STORAGE</> clause must be omitted unless the index
method allows a different type to be used.
</para>
......
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<sect1 id="xindex">
<title>Interfacing Extensions To Indexes</title>
......@@ -242,6 +242,44 @@
</tgroup>
</table>
<para>
GIN indexes are similar to GiST in flexibility: it hasn't a fixed set
of strategies. Instead, the <quote>consistency</> support routine
interprets the strategy numbers accordingly with operator class
definition. As an example, strategies of operator class over arrays
is shown in <xref linkend="xindex-gin-array-strat-table">.
</para>
<table tocentry="1" id="xindex-gin-array-strat-table">
<title>GiST Two-Dimensional <quote>R-tree</> Strategies</title>
<tgroup cols="2">
<thead>
<row>
<entry>Operation</entry>
<entry>Strategy Number</entry>
</row>
</thead>
<tbody>
<row>
<entry>overlap</entry>
<entry>1</entry>
</row>
<row>
<entry>contains</entry>
<entry>2</entry>
</row>
<row>
<entry>is contained by</entry>
<entry>3</entry>
</row>
<row>
<entry>equal</entry>
<entry>4</entry>
</row>
</tbody>
</tgroup>
</table>
<para>
Note that all strategy operators return Boolean values. In
practice, all operators defined as index method strategies must
......@@ -349,37 +387,84 @@
</thead>
<tbody>
<row>
<entry>consistent</entry>
<entry>consistent - determine whether key satifies the
query qualifier</entry>
<entry>1</entry>
</row>
<row>
<entry>union</entry>
<entry>union - compute union of of a set of given keys</entry>
<entry>2</entry>
</row>
<row>
<entry>compress</entry>
<entry>compress - computes a compressed representation of a key or value
to be indexed</entry>
<entry>3</entry>
</row>
<row>
<entry>decompress</entry>
<entry>decompress - computes a decompressed representation of a
compressed key </entry>
<entry>4</entry>
</row>
<row>
<entry>penalty</entry>
<entry>penalty - compute penalty for inserting new key into subtree
with given subtree's key</entry>
<entry>5</entry>
</row>
<row>
<entry>picksplit</entry>
<entry>picksplit - determine which entries of a page are to be moved
to the new page and compute the union keys for resulting pages </entry>
<entry>6</entry>
</row>
<row>
<entry>equal</entry>
<entry>equal - compare two keys and returns true if they are equal
</entry>
<entry>7</entry>
</row>
</tbody>
</tgroup>
</table>
<para>
GIN indexes require four support functions,
shown in <xref linkend="xindex-gin-support-table">.
</para>
<table tocentry="1" id="xindex-gin-support-table">
<title>GIN Support Functions</title>
<tgroup cols="2">
<thead>
<row>
<entry>Function</entry>
<entry>Support Number</entry>
</row>
</thead>
<tbody>
<row>
<entry>
compare - Compare two keys and return an integer less than zero, zero, or
greater than zero, indicating whether the first key is less than, equal to,
or greater than the second.
</entry>
<entry>1</entry>
</row>
<row>
<entry>extractValue - extract keys from value to be indexed</entry>
<entry>2</entry>
</row>
<row>
<entry>extractQuery - extract keys from query</entry>
<entry>3</entry>
</row>
<row>
<entry>consistent - determine whether value matches by the
query</entry>
<entry>4</entry>
</row>
</row>
</tbody>
</tgroup>
</table>
<para>
Unlike strategy operators, support functions return whichever data
type the particular index method expects; for example in the case
......
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