doc/src/sgml/hash.sgml

   1 <!-- doc/src/sgml/hash.sgml -->
   2
   3 <sect1 id="hash-index">
   4 <title>Hash Indexes</title>
   5
   6    <indexterm>
   7     <primary>index</primary>
   8     <secondary>Hash</secondary>
   9    </indexterm>
  10
  11 <sect2 id="hash-intro">
  12  <title>Overview</title>
  13
  14  <para>
  15   <productname>PostgreSQL</productname>
  16   includes an implementation of persistent on-disk hash indexes,
  17   which are fully crash recoverable. Any data type can be indexed by a
  18   hash index, including data types that do not have a well-defined linear
  19   ordering. Hash indexes store only the hash value of the data being
  20   indexed, thus there are no restrictions on the size of the data column
  21   being indexed.
  22  </para>
  23
  24  <para>
  25   Hash indexes support only single-column indexes and do not allow
  26   uniqueness checking.
  27  </para>
  28
  29  <para>
  30   Hash indexes support only the <literal>=</literal> operator,
  31   so WHERE clauses that specify range operations will not be able to take
  32   advantage of hash indexes.
  33  </para>
  34
  35  <para>
  36   Each hash index tuple stores just the 4-byte hash value, not the actual
  37   column value. As a result, hash indexes may be much smaller than B-trees
  38   when indexing longer data items such as UUIDs, URLs, etc. The absence of
  39   the column value also makes all hash index scans lossy. Hash indexes may
  40   take part in bitmap index scans and backward scans.
  41  </para>
  42
  43  <para>
  44   Hash indexes are best optimized for SELECT and UPDATE-heavy workloads
  45   that use equality scans on larger tables. In a B-tree index, searches must
  46   descend through the tree until the leaf page is found. In tables with
  47   millions of rows, this descent can increase access time to data. The
  48   equivalent of a leaf page in a hash index is referred to as a bucket page. In
  49   contrast, a hash index allows accessing the bucket pages directly,
  50   thereby potentially reducing index access time in larger tables. This
  51   reduction in "logical I/O" becomes even more pronounced on indexes/data
  52   larger than shared_buffers/RAM.
  53  </para>
  54
  55  <para>
  56   Hash indexes have been designed to cope with uneven distributions of
  57   hash values. Direct access to the bucket pages works well if the hash
  58   values are evenly distributed. When inserts mean that the bucket page
  59   becomes full, additional overflow pages are chained to that specific
  60   bucket page, locally expanding the storage for index tuples that match
  61   that hash value. When scanning a hash bucket during queries, we need to
  62   scan through all of the overflow pages. Thus an unbalanced hash index
  63   might actually be worse than a B-tree in terms of number of block
  64   accesses required, for some data.
  65  </para>
  66
  67  <para>
  68   As a result of the overflow cases, we can say that hash indexes are
  69   most suitable for unique, nearly unique data or data with a low number
  70   of rows per hash bucket.
  71   One possible way to avoid problems is to exclude highly non-unique
  72   values from the index using a partial index condition, but this may
  73   not be suitable in many cases.
  74  </para>
  75
  76  <para>
  77   Like B-Trees, hash indexes perform simple index tuple deletion. This
  78   is a deferred maintenance operation that deletes index tuples that are
  79   known to be safe to delete (those whose item identifier's LP_DEAD bit
  80   is already set). If an insert finds no space is available on a page we
  81   try to avoid creating a new overflow page by attempting to remove dead
  82   index tuples. Removal cannot occur if the page is pinned at that time.
  83   Deletion of dead index pointers also occurs during VACUUM.
  84  </para>
  85
  86  <para>
  87   If it can, VACUUM will also try to squeeze the index tuples onto as
  88   few overflow pages as possible, minimizing the overflow chain. If an
  89   overflow page becomes empty, overflow pages can be recycled for reuse
  90   in other buckets, though we never return them to the operating system.
  91   There is currently no provision to shrink a hash index, other than by
  92   rebuilding it with REINDEX.
  93   There is no provision for reducing the number of buckets, either.
  94  </para>
  95
  96  <para>
  97   Hash indexes may expand the number of bucket pages as the number of
  98   rows indexed grows. The hash key-to-bucket-number mapping is chosen so that
  99   the index can be incrementally expanded. When a new bucket is to be added to
 100   the index, exactly one existing bucket will need to be "split", with some of
 101   its tuples being transferred to the new bucket according to the updated
 102   key-to-bucket-number mapping.
 103  </para>
 104
 105  <para>
 106   The expansion occurs in the foreground, which could increase execution
 107   time for user inserts. Thus, hash indexes may not be suitable for tables
 108   with rapidly increasing number of rows.
 109  </para>
 110
 111 </sect2>
 112
 113 <sect2 id="hash-implementation">
 114  <title>Implementation</title>
 115
 116  <para>
 117   There are four kinds of pages in a hash index: the meta page (page zero),
 118   which contains statically allocated control information; primary bucket
 119   pages; overflow pages; and bitmap pages, which keep track of overflow
 120   pages that have been freed and are available for re-use. For addressing
 121   purposes, bitmap pages are regarded as a subset of the overflow pages.
 122  </para>
 123
 124  <para>
 125   Both scanning the index and inserting tuples require locating the bucket
 126   where a given tuple ought to be located. To do this, we need the bucket
 127   count, highmask, and lowmask from the metapage; however, it's undesirable
 128   for performance reasons to have to have to lock and pin the metapage for
 129   every such operation. Instead, we retain a cached copy of the metapage
 130   in each backend's relcache entry. This will produce the correct bucket
 131   mapping as long as the target bucket hasn't been split since the last
 132   cache refresh.
 133  </para>
 134
 135  <para>
 136   Primary bucket pages and overflow pages are allocated independently since
 137   any given index might need more or fewer overflow pages relative to its
 138   number of buckets. The hash code uses an interesting set of addressing
 139   rules to support a variable number of overflow pages while not having to
 140   move primary bucket pages around after they are created.
 141  </para>
 142
 143  <para>
 144   Each row in the table indexed is represented by a single index tuple in
 145   the hash index. Hash index tuples are stored in bucket pages, and if
 146   they exist, overflow pages. We speed up searches by keeping the index entries
 147   in any one index page sorted by hash code, thus allowing binary search to be
 148   used within an index page. Note however that there is *no* assumption about
 149   the relative ordering of hash codes across different index pages of a bucket.
 150  </para>
 151
 152  <para>
 153   The bucket splitting algorithms to expand the hash index are too complex to
 154   be worthy of mention here, though are described in more detail in
 155   <filename>src/backend/access/hash/README</filename>.
 156   The split algorithm is crash safe and can be restarted if not completed
 157   successfully.
 158  </para>
 159
 160 </sect2>
 161
 162 </sect1>