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Doc: Hash Indexes.

Browse Source 
A new chapter for Hash Indexes, designed to help users understand how
they work and when to use them.

Backpatch-through 10 where we have made hash indexes durable.

Author: Simon Riggs
Reviewed-By: Justin Pryzby, Amit Kapila
Discussion: https://postgr.es/m/CANbhV-HRjNPYgHo--P1ewBrFJ-GpZPb9_25P7=Wgu7s7hy_sLQ@mail.gmail.com
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doc/src/sgml/filelist.sgml
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<!ENTITY spgist SYSTEM "spgist.sgml">
<!ENTITY gin SYSTEM "gin.sgml">
<!ENTITY brin SYSTEM "brin.sgml">
<!ENTITY hash SYSTEM "hash.sgml">
<!ENTITY planstats SYSTEM "planstats.sgml">
<!ENTITY tableam SYSTEM "tableam.sgml">
<!ENTITY indexam SYSTEM "indexam.sgml">

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<!-- doc/src/sgml/hash.sgml -->
<chapter id="hash-index">
<title>Hash Indexes</title>
<indexterm>
<primary>index</primary>
<secondary>Hash</secondary>
</indexterm>
<sect1 id="hash-intro">
<title>Overview</title>
<para>
<productname>PostgreSQL</productname>
includes an implementation of persistent on-disk hash indexes,
which are fully crash recoverable. Any data type can be indexed by a
hash index, including data types that do not have a well-defined linear
ordering. Hash indexes store only the hash value of the data being
indexed, thus there are no restrictions on the size of the data column
being indexed.
</para>
<para>
Hash indexes support only single-column indexes and do not allow
uniqueness checking.
</para>
<para>
Hash indexes support only the <literal>=</literal> operator,
so WHERE clauses that specify range operations will not be able to take
advantage of hash indexes.
</para>
<para>
Each hash index tuple stores just the 4-byte hash value, not the actual
column value. As a result, hash indexes may be much smaller than B-trees
when indexing longer data items such as UUIDs, URLs, etc. The absence of
the column value also makes all hash index scans lossy. Hash indexes may
take part in bitmap index scans and backward scans.
</para>
<para>
Hash indexes are best optimized for SELECT and UPDATE-heavy workloads
that use equality scans on larger tables. In a B-tree index, searches must
descend through the tree until the leaf page is found. In tables with
millions of rows, this descent can increase access time to data. The
equivalent of a leaf page in a hash index is referred to as a bucket page. In
contrast, a hash index allows accessing the bucket pages directly,
thereby potentially reducing index access time in larger tables. This
reduction in "logical I/O" becomes even more pronounced on indexes/data
larger than shared_buffers/RAM.
</para>
<para>
Hash indexes have been designed to cope with uneven distributions of
hash values. Direct access to the bucket pages works well if the hash
values are evenly distributed. When inserts mean that the bucket page
becomes full, additional overflow pages are chained to that specific
bucket page, locally expanding the storage for index tuples that match
that hash value. When scanning a hash bucket during queries, we need to
scan through all of the overflow pages. Thus an unbalanced hash index
might actually be worse than a B-tree in terms of number of block
accesses required, for some data.
</para>
<para>
As a result of the overflow cases, we can say that hash indexes are
most suitable for unique, nearly unique data or data with a low number
of rows per hash bucket.
One possible way to avoid problems is to exclude highly non-unique
values from the index using a partial index condition, but this may
not be suitable in many cases.
</para>
<para>
Like B-Trees, hash indexes perform simple index tuple deletion. This
is a deferred maintenance operation that deletes index tuples that are
known to be safe to delete (those whose item identifier's LP_DEAD bit
is already set). If an insert finds no space is available on a page we
try to avoid creating a new overflow page by attempting to remove dead
index tuples. Removal cannot occur if the page is pinned at that time.
Deletion of dead index pointers also occurs during VACUUM.
</para>
<para>
If it can, VACUUM will also try to squeeze the index tuples onto as
few overflow pages as possible, minimizing the overflow chain. If an
overflow page becomes empty, overflow pages can be recycled for reuse
in other buckets, though we never return them to the operating system.
There is currently no provision to shrink a hash index, other than by
rebuilding it with REINDEX.
There is no provision for reducing the number of buckets, either.
</para>
<para>
Hash indexes may expand the number of bucket pages as the number of
rows indexed grows. The hash key-to-bucket-number mapping is chosen so that
the index can be incrementally expanded. When a new bucket is to be added to
the index, exactly one existing bucket will need to be "split", with some of
its tuples being transferred to the new bucket according to the updated
key-to-bucket-number mapping.
</para>
<para>
The expansion occurs in the foreground, which could increase execution
time for user inserts. Thus, hash indexes may not be suitable for tables
with rapidly increasing number of rows.
</para>
</sect1>
<sect1 id="hash-implementation">
<title>Implementation</title>
<para>
There are four kinds of pages in a hash index: the meta page (page zero),
which contains statically allocated control information; primary bucket
pages; overflow pages; and bitmap pages, which keep track of overflow
pages that have been freed and are available for re-use. For addressing
purposes, bitmap pages are regarded as a subset of the overflow pages.
</para>
<para>
Both scanning the index and inserting tuples require locating the bucket
where a given tuple ought to be located. To do this, we need the bucket
count, highmask, and lowmask from the metapage; however, it's undesirable
for performance reasons to have to have to lock and pin the metapage for
every such operation. Instead, we retain a cached copy of the metapage
in each backend's relcache entry. This will produce the correct bucket
mapping as long as the target bucket hasn't been split since the last
cache refresh.
</para>
<para>
Primary bucket pages and overflow pages are allocated independently since
any given index might need more or fewer overflow pages relative to its
number of buckets. The hash code uses an interesting set of addressing
rules to support a variable number of overflow pages while not having to
move primary bucket pages around after they are created.
</para>
<para>
Each row in the table indexed is represented by a single index tuple in
the hash index. Hash index tuples are stored in bucket pages, and if
they exist, overflow pages. We speed up searches by keeping the index entries
in any one index page sorted by hash code, thus allowing binary search to be
used within an index page. Note however that there is *no* assumption about
the relative ordering of hash codes across different index pages of a bucket.
</para>
<para>
The bucket splitting algorithms to expand the hash index are too complex to
be worthy of mention here, though are described in more detail in
<filename>src/backend/access/hash/README</filename>.
The split algorithm is crash safe and can be restarted if not completed
successfully.
</para>
</sect1>
</chapter>

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&spgist;
&gin;
&brin;
&hash;
&storage;
&bki;
&planstats;