This section covers B-Tree index implementation details that may be
of use to advanced users. See
src/backend/access/nbtree/README
in the source
distribution for a much more detailed, internals-focused description
of the B-Tree implementation.
PostgreSQL B-Tree indexes are multi-level tree structures, where each level of the tree can be used as a doubly-linked list of pages. A single metapage is stored in a fixed position at the start of the first segment file of the index. All other pages are either leaf pages or internal pages. Leaf pages are the pages on the lowest level of the tree. All other levels consist of internal pages. Each leaf page contains tuples that point to table rows. Each internal page contains tuples that point to the next level down in the tree. Typically, over 99% of all pages are leaf pages. Both internal pages and leaf pages use the standard page format described in Section 70.6.
New leaf pages are added to a B-Tree index when an existing leaf page cannot fit an incoming tuple. A page split operation makes room for items that originally belonged on the overflowing page by moving a portion of the items to a new page. Page splits must also insert a new downlink to the new page in the parent page, which may cause the parent to split in turn. Page splits “cascade upwards” in a recursive fashion. When the root page finally cannot fit a new downlink, a root page split operation takes place. This adds a new level to the tree structure by creating a new root page that is one level above the original root page.
B-Tree indexes are not directly aware that under MVCC, there might
be multiple extant versions of the same logical table row; to an
index, each tuple is an independent object that needs its own index
entry. “Version churn” tuples may sometimes
accumulate and adversely affect query latency and throughput. This
typically occurs with UPDATE
-heavy workloads
where most individual updates cannot apply the
HOT optimization. Changing the value of only
one column covered by one index during an UPDATE
always necessitates a new set of index tuples
— one for each and every index on the
table. Note in particular that this includes indexes that were not
“logically modified” by the UPDATE
.
All indexes will need a successor physical index tuple that points
to the latest version in the table. Each new tuple within each
index will generally need to coexist with the original
“updated” tuple for a short period of time (typically
until shortly after the UPDATE
transaction
commits).
B-Tree indexes incrementally delete version churn index tuples by
performing bottom-up index deletion passes.
Each deletion pass is triggered in reaction to an anticipated
“version churn page split”. This only happens with
indexes that are not logically modified by
UPDATE
statements, where concentrated build up
of obsolete versions in particular pages would occur otherwise. A
page split will usually be avoided, though it's possible that
certain implementation-level heuristics will fail to identify and
delete even one garbage index tuple (in which case a page split or
deduplication pass resolves the issue of an incoming new tuple not
fitting on a leaf page). The worst-case number of versions that
any index scan must traverse (for any single logical row) is an
important contributor to overall system responsiveness and
throughput. A bottom-up index deletion pass targets suspected
garbage tuples in a single leaf page based on
qualitative distinctions involving logical
rows and versions. This contrasts with the “top-down”
index cleanup performed by autovacuum workers, which is triggered
when certain quantitative table-level
thresholds are exceeded (see Section 25.1.6).
Not all deletion operations that are performed within B-Tree
indexes are bottom-up deletion operations. There is a distinct
category of index tuple deletion: 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). Like bottom-up index deletion, simple index
deletion takes place at the point that a page split is anticipated
as a way of avoiding the split.
Simple deletion is opportunistic in the sense that it can only
take place when recent index scans set the
LP_DEAD
bits of affected items in passing.
Prior to PostgreSQL 14, the only
category of B-Tree deletion was simple deletion. The main
differences between it and bottom-up deletion are that only the
former is opportunistically driven by the activity of passing
index scans, while only the latter specifically targets version
churn from UPDATE
s that do not logically modify
indexed columns.
Bottom-up index deletion performs the vast majority of all garbage
index tuple cleanup for particular indexes with certain workloads.
This is expected with any B-Tree index that is subject to
significant version churn from UPDATE
s that
rarely or never logically modify the columns that the index covers.
The average and worst-case number of versions per logical row can
be kept low purely through targeted incremental deletion passes.
It's quite possible that the on-disk size of certain indexes will
never increase by even one single page/block despite
constant version churn from
UPDATE
s. Even then, an exhaustive “clean
sweep” by a VACUUM
operation (typically
run in an autovacuum worker process) will eventually be required as
a part of collective cleanup of the table and
each of its indexes.
Unlike VACUUM
, bottom-up index deletion does not
provide any strong guarantees about how old the oldest garbage
index tuple may be. No index can be permitted to retain
“floating garbage” index tuples that became dead prior
to a conservative cutoff point shared by the table and all of its
indexes collectively. This fundamental table-level invariant makes
it safe to recycle table TIDs. This is how it
is possible for distinct logical rows to reuse the same table
TID over time (though this can never happen with
two logical rows whose lifetimes span the same
VACUUM
cycle).
A duplicate is a leaf page tuple (a tuple that points to a table row) where all indexed key columns have values that match corresponding column values from at least one other leaf page tuple in the same index. Duplicate tuples are quite common in practice. B-Tree indexes can use a special, space-efficient representation for duplicates when an optional technique is enabled: deduplication.
Deduplication works by periodically merging groups of duplicate tuples together, forming a single posting list tuple for each group. The column key value(s) only appear once in this representation. This is followed by a sorted array of TIDs that point to rows in the table. This significantly reduces the storage size of indexes where each value (or each distinct combination of column values) appears several times on average. The latency of queries can be reduced significantly. Overall query throughput may increase significantly. The overhead of routine index vacuuming may also be reduced significantly.
B-Tree deduplication is just as effective with
“duplicates” that contain a NULL value, even though
NULL values are never equal to each other according to the
=
member of any B-Tree operator class. As far
as any part of the implementation that understands the on-disk
B-Tree structure is concerned, NULL is just another value from the
domain of indexed values.
The deduplication process occurs lazily, when a new item is inserted that cannot fit on an existing leaf page, though only when index tuple deletion could not free sufficient space for the new item (typically deletion is briefly considered and then skipped over). Unlike GIN posting list tuples, B-Tree posting list tuples do not need to expand every time a new duplicate is inserted; they are merely an alternative physical representation of the original logical contents of the leaf page. This design prioritizes consistent performance with mixed read-write workloads. Most client applications will at least see a moderate performance benefit from using deduplication. Deduplication is enabled by default.
CREATE INDEX
and REINDEX
apply deduplication to create posting list tuples, though the
strategy they use is slightly different. Each group of duplicate
ordinary tuples encountered in the sorted input taken from the
table is merged into a posting list tuple
before being added to the current pending leaf
page. Individual posting list tuples are packed with as many
TIDs as possible. Leaf pages are written out in
the usual way, without any separate deduplication pass. This
strategy is well-suited to CREATE INDEX
and
REINDEX
because they are once-off batch
operations.
Write-heavy workloads that don't benefit from deduplication due to
having few or no duplicate values in indexes will incur a small,
fixed performance penalty (unless deduplication is explicitly
disabled). The deduplicate_items
storage
parameter can be used to disable deduplication within individual
indexes. There is never any performance penalty with read-only
workloads, since reading posting list tuples is at least as
efficient as reading the standard tuple representation. Disabling
deduplication isn't usually helpful.
It is sometimes possible for unique indexes (as well as unique constraints) to use deduplication. This allows leaf pages to temporarily “absorb” extra version churn duplicates. Deduplication in unique indexes augments bottom-up index deletion, especially in cases where a long-running transaction holds a snapshot that blocks garbage collection. The goal is to buy time for the bottom-up index deletion strategy to become effective again. Delaying page splits until a single long-running transaction naturally goes away can allow a bottom-up deletion pass to succeed where an earlier deletion pass failed.
A special heuristic is applied to determine whether a
deduplication pass in a unique index should take place. It can
often skip straight to splitting a leaf page, avoiding a
performance penalty from wasting cycles on unhelpful deduplication
passes. If you're concerned about the overhead of deduplication,
consider setting deduplicate_items = off
selectively. Leaving deduplication enabled in unique indexes has
little downside.
Deduplication cannot be used in all cases due to
implementation-level restrictions. Deduplication safety is
determined when CREATE INDEX
or
REINDEX
is run.
Note that deduplication is deemed unsafe and cannot be used in the following cases involving semantically significant differences among equal datums:
text
, varchar
, and char
cannot use deduplication when a
nondeterministic collation is used. Case
and accent differences must be preserved among equal datums.
numeric
cannot use deduplication. Numeric display
scale must be preserved among equal datums.
jsonb
cannot use deduplication, since the
jsonb
B-Tree operator class uses
numeric
internally.
float4
and float8
cannot use
deduplication. These types have distinct representations for
-0
and 0
, which are
nevertheless considered equal. This difference must be
preserved.
There is one further implementation-level restriction that may be lifted in a future version of PostgreSQL:
Container types (such as composite types, arrays, or range types) cannot use deduplication.
There is one further implementation-level restriction that applies regardless of the operator class or collation used:
INCLUDE
indexes can never use deduplication.