Hash Indexes {#hash-index}
============

index

Hash

Overview {#hash-intro}
========

PostgreSQL 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.

Hash indexes support only single-column indexes and do not allow
uniqueness checking.

Hash indexes support only the `=` operator, so WHERE clauses that
specify range operations will not be able to take advantage of hash
indexes.

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.

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.

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.

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.

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.

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.

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.

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.

Implementation {#hash-implementation}
==============

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.

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.

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.

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.

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
`src/backend/access/hash/README`. The split algorithm is crash safe and
can be restarted if not completed successfully.
