postgres/src/backend/access/gist

src/backend/access/gist/README

GiST Indexing
=============

This directory contains an implementation of GiST indexing for Postgres.

GiST stands for Generalized Search Tree. It was introduced in the seminal paper
"Generalized Search Trees for Database Systems", 1995, Joseph M. Hellerstein,
Jeffrey F. Naughton, Avi Pfeffer:

    http://www.sai.msu.su/~megera/postgres/gist/papers/gist.ps

and implemented by J. Hellerstein and P. Aoki in an early version of
PostgreSQL (more details are available from The GiST Indexing Project
at Berkeley at http://gist.cs.berkeley.edu/). As a "university"
project it had a limited number of features and was in rare use.

The current implementation of GiST supports:

  * Variable length keys
  * Composite keys (multi-key)
  * Ordered search (nearest-neighbor search)
  * provides NULL-safe interface to GiST core
  * Concurrency
  * Recovery support via WAL logging

The support for concurrency implemented in PostgreSQL was developed based on
the paper "Access Methods for Next-Generation Database Systems" by
Marcel Kornaker:

    http://www.sai.msu.su/~megera/postgres/gist/papers/concurrency/access-methods-for-next-generation.pdf.gz

The original algorithms were modified in several ways:

* They had to be adapted to PostgreSQL conventions. For example, the SEARCH
  algorithm was considerably changed, because in PostgreSQL the search function
  should return one tuple (next), not all tuples at once. Also, it should
  release page locks between calls.
* Since we added support for variable length keys, it's not possible to
  guarantee enough free space for all keys on pages after splitting. User
  defined function picksplit doesn't have information about size of tuples
  (each tuple may contain several keys as in multicolumn index while picksplit
  could work with only one key) and pages.
* We modified original INSERT algorithm for performance reasons. In particular,
  it is now a single-pass algorithm.
* Since the papers were theoretical, some details were omitted and we
  had to find out ourself how to solve some specific problems.

Because of the above reasons, we have revised the interaction of GiST
core and PostgreSQL WAL system. Moreover, we encountered (and solved)
a problem of uncompleted insertions when recovering after crash, which
was not touched in the paper.

Search Algorithm
----------------

The search code maintains a queue of unvisited items, where an "item" is
either a heap tuple known to satisfy the search conditions, or an index
page that is consistent with the search conditions according to inspection
of its parent page's downlink item.  Initially the root page is searched
to find unvisited items in it.  Then we pull items from the queue.  A
heap tuple pointer is just returned immediately; an index page entry
causes that page to be searched, generating more queue entries.

The queue is kept ordered with heap tuple items at the front, then
index page entries, with any newly-added index page entry inserted
before existing index page entries.  This ensures depth-first traversal
of the index, and in particular causes the first few heap tuples to be
returned as soon as possible.  That is helpful in case there is a LIMIT
that requires only a few tuples to be produced.

To implement nearest-neighbor search, the queue entries are augmented
with distance data: heap tuple entries are labeled with exact distance
from the search argument, while index-page entries must be labeled with
the minimum distance that any of their children could have.  Then,
queue entries are retrieved in smallest-distance-first order, with
entries having identical distances managed as stated in the previous
paragraph.

The search algorithm keeps an index page locked only long enough to scan
its entries and queue those that satisfy the search conditions.  Since
insertions can occur concurrently with searches, it is possible for an
index child page to be split between the time we make a queue entry for it
(while visiting its parent page) and the time we actually reach and scan
the child page.  To avoid missing the entries that were moved to the right
sibling, we detect whether a split has occurred by comparing the child
page's NSN to the LSN that the parent had when visited.  If it did, the
sibling page is immediately added to the front of the queue, ensuring that
its items will be scanned in the same order as if they were still on the
original child page.

As is usual in Postgres, the search algorithm only guarantees to find index
entries that existed before the scan started; index entries added during
the scan might or might not be visited.  This is okay as long as all
searches use MVCC snapshot rules to reject heap tuples newer than the time
of scan start.  In particular, this means that we need not worry about
cases where a parent page's downlink key is "enlarged" after we look at it.
Any such enlargement would be to add child items that we aren't interested
in returning anyway.


Insert Algorithm
----------------

INSERT guarantees that the GiST tree remains balanced. User defined key method
Penalty is used for choosing a subtree to insert; method PickSplit is used for
the node splitting algorithm; method Union is used for propagating changes
upward to maintain the tree properties.

NOTICE: We modified original INSERT algorithm for performance reason. In
particularly, it is now a single-pass algorithm.

Function findLeaf is used to identify subtree for insertion. Page, in which
insertion is proceeded, is locked as well as its parent page. Functions
findParent and findPath are used to find parent pages, which could be changed
because of concurrent access. Function pageSplit is recurrent and could split
page by more than 2 pages, which could be necessary if keys have different
lengths or more than one key are inserted (in such situation, user defined
function pickSplit cannot guarantee free space on page).

findLeaf(new-key)
	push(stack, [root, 0]) //page, LSN
	while(true)
		ptr = top of stack
		latch( ptr->page, S-mode )
		ptr->lsn = ptr->page->lsn
		if ( exists ptr->parent AND ptr->parent->lsn < ptr->page->nsn )
			unlatch( ptr->page )
			pop stack
		else if ( ptr->page is not leaf )
			push( stack, [get_best_child(ptr->page, new-key), 0] )
			unlatch( ptr->page )
		else
			unlatch( ptr->page )
			latch( ptr->page, X-mode )
			if ( ptr->page is not leaf )
				//the only root page can become a non-leaf
				unlatch( ptr->page )
			else if ( ptr->parent->lsn < ptr->page->nsn )
				unlatch( ptr->page )
				pop stack
			else
				return stack
			end
		end
	end

findPath( stack item )
	push stack, [root, 0, 0] // page, LSN, parent
	while( stack )
		ptr = top of stack
		latch( ptr->page, S-mode )
		if ( ptr->parent->page->lsn < ptr->page->nsn )
			push stack, [ ptr->page->rightlink, 0, ptr->parent ]
		end
		for( each tuple on page )
			if ( tuple->pagepointer == item->page )
				return stack
			else
				add to stack at the end [tuple->pagepointer,0, ptr]
			end
		end
		unlatch( ptr->page )
		pop stack
	end

findParent( stack item )
	parent = item->parent
	latch( parent->page, X-mode )
	if ( parent->page->lsn != parent->lsn )
		while(true)
			search parent tuple on parent->page, if found the return
			rightlink = parent->page->rightlink
			unlatch( parent->page )
			if ( rightlink is incorrect )
				break loop
			end
			parent->page = rightlink
			latch( parent->page, X-mode )
		end
		newstack = findPath( item->parent )
		replace part of stack to new one
		return findParent( item )
	end

pageSplit(page, allkeys)
	(lkeys, rkeys) = pickSplit( allkeys )
	if ( page is root )
		lpage = new page
	else
		lpage = page
	rpage = new page
	if ( no space left on rpage )
		newkeys = pageSplit( rpage, rkeys )
	else
		push newkeys, union(rkeys)
	end
	if ( no space left on lpage )
		push newkeys, pageSplit( lpage, lkeys )
	else
		push newkeys, union(lkeys)
	end
	return newkeys


placetopage(page, keysarray)
	if ( no space left on page )
		keysarray = pageSplit(page, [ extract_keys(page), keysarray])
		last page in chain gets old NSN,
		original and others - new NSN equals to LSN
		if ( page is root )
			make new root with keysarray
		end
	else
		put keysarray on page
		if ( length of keysarray > 1 )
			keysarray = [ union(keysarray) ]
		end
	end

insert(new-key)
	stack = findLeaf(new-key)
	keysarray = [new-key]
	ptr = top of stack
	while(true)
		findParent( ptr ) //findParent latches parent page
		keysarray = placetopage(ptr->page, keysarray)
		unlatch( ptr->page )
		pop stack;
		ptr = top of stack
		if (length of keysarray == 1)
			newboundingkey = union(oldboundingkey, keysarray)
			if (newboundingkey == oldboundingkey)
				unlatch ptr->page
				break loop
			end
		end
	end

Authors:
	Teodor Sigaev	<teodor@sigaev.ru>
	Oleg Bartunov	<oleg@sai.msu.su>