Indexesindex
Indexes are a common way to enhance database performance. An index
allows the database server to find and retrieve specific rows much
faster than it could do without an index. But indexes also add
overhead to the database system as a whole, so they should be used
sensibly.
Introduction
Suppose we have a table similar to this:
CREATE TABLE test1 (
id integer,
content varchar
);
and the application issues many queries of the form:
SELECT content FROM test1 WHERE id = constant;
With no advance preparation, the system would have to scan the entire
test1 table, row by row, to find all
matching entries. If there are many rows in
test1 and only a few rows (perhaps zero
or one) that would be returned by such a query, this is clearly an
inefficient method. But if the system has been instructed to maintain an
index on the id column, it can use a more
efficient method for locating matching rows. For instance, it
might only have to walk a few levels deep into a search tree.
A similar approach is used in most non-fiction books: terms and
concepts that are frequently looked up by readers are collected in
an alphabetic index at the end of the book. The interested reader
can scan the index relatively quickly and flip to the appropriate
page(s), rather than having to read the entire book to find the
material of interest. Just as it is the task of the author to
anticipate the items that readers are likely to look up,
it is the task of the database programmer to foresee which indexes
will be useful.
The following command can be used to create an index on the
id column, as discussed:
CREATE INDEX test1_id_index ON test1 (id);
The name test1_id_index can be chosen
freely, but you should pick something that enables you to remember
later what the index was for.
To remove an index, use the DROP INDEX command.
Indexes can be added to and removed from tables at any time.
Once an index is created, no further intervention is required: the
system will update the index when the table is modified, and it will
use the index in queries when it thinks doing so would be more efficient
than a sequential table scan. But you might have to run the
ANALYZE command regularly to update
statistics to allow the query planner to make educated decisions.
See for information about
how to find out whether an index is used and when and why the
planner might choose not to use an index.
Indexes can also benefit UPDATE and
DELETE commands with search conditions.
Indexes can moreover be used in join searches. Thus,
an index defined on a column that is part of a join condition can
also significantly speed up queries with joins.
In general, PostgreSQL indexes can be used
to optimize queries that contain one or more WHERE
or JOIN clauses of the form
indexed-columnindexable-operatorcomparison-value
Here, the indexed-column is whatever
column or expression the index has been defined on.
The indexable-operator is an operator that
is a member of the index's operator class for
the indexed column. (More details about that appear below.)
And the comparison-value can be any
expression that is not volatile and does not reference the index's
table.
In some cases the query planner can extract an indexable clause of
this form from another SQL construct. A simple example is that if
the original clause was
comparison-valueoperatorindexed-column
then it can be flipped around into indexable form if the
original operator has a commutator
operator that is a member of the index's operator class.
Creating an index on a large table can take a long time. By default,
PostgreSQL allows reads (SELECT statements) to occur
on the table in parallel with index creation, but writes (INSERT,
UPDATE, DELETE) are blocked until the index build is finished.
In production environments this is often unacceptable.
It is possible to allow writes to occur in parallel with index
creation, but there are several caveats to be aware of —
for more information see .
After an index is created, the system has to keep it synchronized with the
table. This adds overhead to data manipulation operations. Indexes can
also prevent the creation of heap-only
tuples.
Therefore indexes that are seldom or never used in queries
should be removed.
Index TypesPostgreSQL provides several index types:
B-tree, Hash, GiST, SP-GiST, GIN, BRIN, and the extension bloom.
Each index type uses a different
algorithm that is best suited to different types of indexable clauses.
By default, the CREATE
INDEX command creates
B-tree indexes, which fit the most common situations.
The other index types are selected by writing the keyword
USING followed by the index type name.
For example, to create a Hash index:
CREATE INDEX name ON table USING HASH (column);
B-TreeindexB-TreeB-Treeindex
B-trees can handle equality and range queries on data that can be sorted
into some ordering.
In particular, the PostgreSQL query planner
will consider using a B-tree index whenever an indexed column is
involved in a comparison using one of these operators:
< <= = >= >
Constructs equivalent to combinations of these operators, such as
BETWEEN and IN, can also be implemented with
a B-tree index search. Also, an IS NULL or IS NOT
NULL condition on an index column can be used with a B-tree index.
The optimizer can also use a B-tree index for queries involving the
pattern matching operators LIKE and ~if the pattern is a constant and is anchored to
the beginning of the string — for example, col LIKE
'foo%' or col ~ '^foo', but not
col LIKE '%bar'. However, if your database does not
use the C locale you will need to create the index with a special
operator class to support indexing of pattern-matching queries; see
below. It is also possible to use
B-tree indexes for ILIKE and
~*, but only if the pattern starts with
non-alphabetic characters, i.e., characters that are not affected by
upper/lower case conversion.
B-tree indexes can also be used to retrieve data in sorted order.
This is not always faster than a simple scan and sort, but it is
often helpful.
Hashindexhashhashindex
Hash indexes store a 32-bit hash code derived from the
value of the indexed column. Hence,
such indexes can only handle simple equality comparisons.
The query planner will consider using a hash index whenever an
indexed column is involved in a comparison using the
equal operator:
=
GiSTindexGiSTGiSTindex
GiST indexes are not a single kind of index, but rather an infrastructure
within which many different indexing strategies can be implemented.
Accordingly, the particular operators with which a GiST index can be
used vary depending on the indexing strategy (the operator
class). As an example, the standard distribution of
PostgreSQL includes GiST operator classes
for several two-dimensional geometric data types, which support indexed
queries using these operators:
<< &< &> >> <<| &<| |&> |>> @> <@ ~= &&
(See for the meaning of
these operators.)
The GiST operator classes included in the standard distribution are
documented in .
Many other GiST operator
classes are available in the contrib collection or as separate
projects. For more information see .
GiST indexes are also capable of optimizing nearest-neighbor
searches, such as
point '(101,456)' LIMIT 10;
]]>
which finds the ten places closest to a given target point. The ability
to do this is again dependent on the particular operator class being used.
In , operators that can be
used in this way are listed in the column Ordering Operators.
SP-GiSTindexSP-GiSTSP-GiSTindex
SP-GiST indexes, like GiST indexes, offer an infrastructure that supports
various kinds of searches. SP-GiST permits implementation of a wide range
of different non-balanced disk-based data structures, such as quadtrees,
k-d trees, and radix trees (tries). As an example, the standard distribution of
PostgreSQL includes SP-GiST operator classes
for two-dimensional points, which support indexed
queries using these operators:
<< >> ~= <@ <<| |>>
(See for the meaning of
these operators.)
The SP-GiST operator classes included in the standard distribution are
documented in .
For more information see .
Like GiST, SP-GiST supports nearest-neighbor searches.
For SP-GiST operator classes that support distance ordering, the
corresponding operator is listed in the Ordering Operators
column in .
GINindexGINGINindex
GIN indexes are inverted indexes which are appropriate for
data values that contain multiple component values, such as arrays. An
inverted index contains a separate entry for each component value, and
can efficiently handle queries that test for the presence of specific
component values.
Like GiST and SP-GiST, GIN can support
many different user-defined indexing strategies, and the particular
operators with which a GIN index can be used vary depending on the
indexing strategy.
As an example, the standard distribution of
PostgreSQL includes a GIN operator class
for arrays, which supports indexed queries using these operators:
<@ @> = &&
(See for the meaning of
these operators.)
The GIN operator classes included in the standard distribution are
documented in .
Many other GIN operator
classes are available in the contrib collection or as separate
projects. For more information see .
BRINindexBRINBRINindex
BRIN indexes (a shorthand for Block Range INdexes) store summaries about
the values stored in consecutive physical block ranges of a table.
Thus, they are most effective for columns whose values are well-correlated
with the physical order of the table rows.
Like GiST, SP-GiST and GIN,
BRIN can support many different indexing strategies,
and the particular operators with which a BRIN index can be used
vary depending on the indexing strategy.
For data types that have a linear sort order, the indexed data
corresponds to the minimum and maximum values of the
values in the column for each block range. This supports indexed queries
using these operators:
< <= = >= >
The BRIN operator classes included in the standard distribution are
documented in .
For more information see .
Multicolumn Indexesindexmulticolumn
An index can be defined on more than one column of a table. For example, if
you have a table of this form:
CREATE TABLE test2 (
major int,
minor int,
name varchar
);
(say, you keep your /dev
directory in a database...) and you frequently issue queries like:
SELECT name FROM test2 WHERE major = constant AND minor = constant;
then it might be appropriate to define an index on the columns
major and
minor together, e.g.:
CREATE INDEX test2_mm_idx ON test2 (major, minor);
Currently, only the B-tree, GiST, GIN, and BRIN index types support
multiple-key-column indexes. Whether there can be multiple key
columns is independent of whether INCLUDE columns
can be added to the index. Indexes can have up to 32 columns,
including INCLUDE columns. (This limit can be
altered when building PostgreSQL; see the
file pg_config_manual.h.)
A multicolumn B-tree index can be used with query conditions that
involve any subset of the index's columns, but the index is most
efficient when there are constraints on the leading (leftmost) columns.
The exact rule is that equality constraints on leading columns, plus
any inequality constraints on the first column that does not have an
equality constraint, will be used to limit the portion of the index
that is scanned. Constraints on columns to the right of these columns
are checked in the index, so they save visits to the table proper, but
they do not reduce the portion of the index that has to be scanned.
For example, given an index on (a, b, c) and a
query condition WHERE a = 5 AND b >= 42 AND c < 77,
the index would have to be scanned from the first entry with
a = 5 and b = 42 up through the last entry with
a = 5. Index entries with c >= 77 would be
skipped, but they'd still have to be scanned through.
This index could in principle be used for queries that have constraints
on b and/or c with no constraint on a
— but the entire index would have to be scanned, so in most cases
the planner would prefer a sequential table scan over using the index.
A multicolumn GiST index can be used with query conditions that
involve any subset of the index's columns. Conditions on additional
columns restrict the entries returned by the index, but the condition on
the first column is the most important one for determining how much of
the index needs to be scanned. A GiST index will be relatively
ineffective if its first column has only a few distinct values, even if
there are many distinct values in additional columns.
A multicolumn GIN index can be used with query conditions that
involve any subset of the index's columns. Unlike B-tree or GiST,
index search effectiveness is the same regardless of which index column(s)
the query conditions use.
A multicolumn BRIN index can be used with query conditions that
involve any subset of the index's columns. Like GIN and unlike B-tree or
GiST, index search effectiveness is the same regardless of which index
column(s) the query conditions use. The only reason to have multiple BRIN
indexes instead of one multicolumn BRIN index on a single table is to have
a different pages_per_range storage parameter.
Of course, each column must be used with operators appropriate to the index
type; clauses that involve other operators will not be considered.
Multicolumn indexes should be used sparingly. In most situations,
an index on a single column is sufficient and saves space and time.
Indexes with more than three columns are unlikely to be helpful
unless the usage of the table is extremely stylized. See also
and
for some discussion of the
merits of different index configurations.
Indexes and ORDER BYindexand ORDER BY
In addition to simply finding the rows to be returned by a query,
an index may be able to deliver them in a specific sorted order.
This allows a query's ORDER BY specification to be honored
without a separate sorting step. Of the index types currently
supported by PostgreSQL, only B-tree
can produce sorted output — the other index types return
matching rows in an unspecified, implementation-dependent order.
The planner will consider satisfying an ORDER BY specification
either by scanning an available index that matches the specification,
or by scanning the table in physical order and doing an explicit
sort. For a query that requires scanning a large fraction of the
table, an explicit sort is likely to be faster than using an index
because it requires
less disk I/O due to following a sequential access pattern. Indexes are
more useful when only a few rows need be fetched. An important
special case is ORDER BY in combination with
LIMITn: an explicit sort will have to process
all the data to identify the first n rows, but if there is
an index matching the ORDER BY, the first n
rows can be retrieved directly, without scanning the remainder at all.
By default, B-tree indexes store their entries in ascending order
with nulls last (table TID is treated as a tiebreaker column among
otherwise equal entries). This means that a forward scan of an
index on column x produces output satisfying ORDER BY x
(or more verbosely, ORDER BY x ASC NULLS LAST). The
index can also be scanned backward, producing output satisfying
ORDER BY x DESC
(or more verbosely, ORDER BY x DESC NULLS FIRST, since
NULLS FIRST is the default for ORDER BY DESC).
You can adjust the ordering of a B-tree index by including the
options ASC, DESC, NULLS FIRST,
and/or NULLS LAST when creating the index; for example:
CREATE INDEX test2_info_nulls_low ON test2 (info NULLS FIRST);
CREATE INDEX test3_desc_index ON test3 (id DESC NULLS LAST);
An index stored in ascending order with nulls first can satisfy
either ORDER BY x ASC NULLS FIRST or
ORDER BY x DESC NULLS LAST depending on which direction
it is scanned in.
You might wonder why bother providing all four options, when two
options together with the possibility of backward scan would cover
all the variants of ORDER BY. In single-column indexes
the options are indeed redundant, but in multicolumn indexes they can be
useful. Consider a two-column index on (x, y): this can
satisfy ORDER BY x, y if we scan forward, or
ORDER BY x DESC, y DESC if we scan backward.
But it might be that the application frequently needs to use
ORDER BY x ASC, y DESC. There is no way to get that
ordering from a plain index, but it is possible if the index is defined
as (x ASC, y DESC) or (x DESC, y ASC).
Obviously, indexes with non-default sort orderings are a fairly
specialized feature, but sometimes they can produce tremendous
speedups for certain queries. Whether it's worth maintaining such an
index depends on how often you use queries that require a special
sort ordering.
Combining Multiple Indexesindexcombining multiple indexesbitmap scan
A single index scan can only use query clauses that use the index's
columns with operators of its operator class and are joined with
AND. For example, given an index on (a, b)
a query condition like WHERE a = 5 AND b = 6 could
use the index, but a query like WHERE a = 5 OR b = 6 could not
directly use the index.
Fortunately,
PostgreSQL has the ability to combine multiple indexes
(including multiple uses of the same index) to handle cases that cannot
be implemented by single index scans. The system can form AND
and OR conditions across several index scans. For example,
a query like WHERE x = 42 OR x = 47 OR x = 53 OR x = 99
could be broken down into four separate scans of an index on x,
each scan using one of the query clauses. The results of these scans are
then ORed together to produce the result. Another example is that if we
have separate indexes on x and y, one possible
implementation of a query like WHERE x = 5 AND y = 6 is to
use each index with the appropriate query clause and then AND together
the index results to identify the result rows.
To combine multiple indexes, the system scans each needed index and
prepares a bitmap in memory giving the locations of
table rows that are reported as matching that index's conditions.
The bitmaps are then ANDed and ORed together as needed by the query.
Finally, the actual table rows are visited and returned. The table rows
are visited in physical order, because that is how the bitmap is laid
out; this means that any ordering of the original indexes is lost, and
so a separate sort step will be needed if the query has an ORDER
BY clause. For this reason, and because each additional index scan
adds extra time, the planner will sometimes choose to use a simple index
scan even though additional indexes are available that could have been
used as well.
In all but the simplest applications, there are various combinations of
indexes that might be useful, and the database developer must make
trade-offs to decide which indexes to provide. Sometimes multicolumn
indexes are best, but sometimes it's better to create separate indexes
and rely on the index-combination feature. For example, if your
workload includes a mix of queries that sometimes involve only column
x, sometimes only column y, and sometimes both
columns, you might choose to create two separate indexes on
x and y, relying on index combination to
process the queries that use both columns. You could also create a
multicolumn index on (x, y). This index would typically be
more efficient than index combination for queries involving both
columns, but as discussed in , it
would be almost useless for queries involving only y, so it
should not be the only index. A combination of the multicolumn index
and a separate index on y would serve reasonably well. For
queries involving only x, the multicolumn index could be
used, though it would be larger and hence slower than an index on
x alone. The last alternative is to create all three
indexes, but this is probably only reasonable if the table is searched
much more often than it is updated and all three types of query are
common. If one of the types of query is much less common than the
others, you'd probably settle for creating just the two indexes that
best match the common types.
Unique Indexesindexunique
Indexes can also be used to enforce uniqueness of a column's value,
or the uniqueness of the combined values of more than one column.
CREATE UNIQUE INDEX name ON table (column, ...) NULLS NOT DISTINCT ;
Currently, only B-tree indexes can be declared unique.
When an index is declared unique, multiple table rows with equal
indexed values are not allowed. By default, null values in a unique column
are not considered equal, allowing multiple nulls in the column. The
NULLS NOT DISTINCT option modifies this and causes the
index to treat nulls as equal. A multicolumn unique index will only reject
cases where all indexed columns are equal in multiple rows.
PostgreSQL automatically creates a unique
index when a unique constraint or primary key is defined for a table.
The index covers the columns that make up the primary key or unique
constraint (a multicolumn index, if appropriate), and is the mechanism
that enforces the constraint.
There's no need to manually
create indexes on unique columns; doing so would just duplicate
the automatically-created index.
Indexes on Expressionsindexon expressions
An index column need not be just a column of the underlying table,
but can be a function or scalar expression computed from one or
more columns of the table. This feature is useful to obtain fast
access to tables based on the results of computations.
For example, a common way to do case-insensitive comparisons is to
use the lower function:
SELECT * FROM test1 WHERE lower(col1) = 'value';
This query can use an index if one has been
defined on the result of the lower(col1)
function:
CREATE INDEX test1_lower_col1_idx ON test1 (lower(col1));
If we were to declare this index UNIQUE, it would prevent
creation of rows whose col1 values differ only in case,
as well as rows whose col1 values are actually identical.
Thus, indexes on expressions can be used to enforce constraints that
are not definable as simple unique constraints.
As another example, if one often does queries like:
SELECT * FROM people WHERE (first_name || ' ' || last_name) = 'John Smith';
then it might be worth creating an index like this:
CREATE INDEX people_names ON people ((first_name || ' ' || last_name));
The syntax of the CREATE INDEX command normally requires
writing parentheses around index expressions, as shown in the second
example. The parentheses can be omitted when the expression is just
a function call, as in the first example.
Index expressions are relatively expensive to maintain, because the
derived expression(s) must be computed for each row insertion
and non-HOT update. However, the index expressions are
not recomputed during an indexed search, since they are
already stored in the index. In both examples above, the system
sees the query as just WHERE indexedcolumn = 'constant'
and so the speed of the search is equivalent to any other simple index
query. Thus, indexes on expressions are useful when retrieval speed
is more important than insertion and update speed.
Partial Indexesindexpartial
A partial index is an index built over a
subset of a table; the subset is defined by a conditional
expression (called the predicate of the
partial index). The index contains entries only for those table
rows that satisfy the predicate. Partial indexes are a specialized
feature, but there are several situations in which they are useful.
One major reason for using a partial index is to avoid indexing common
values. Since a query searching for a common value (one that
accounts for more than a few percent of all the table rows) will not
use the index anyway, there is no point in keeping those rows in the
index at all. This reduces the size of the index, which will speed
up those queries that do use the index. It will also speed up many table
update operations because the index does not need to be
updated in all cases. shows a
possible application of this idea.
Setting up a Partial Index to Exclude Common Values
Suppose you are storing web server access logs in a database.
Most accesses originate from the IP address range of your organization but
some are from elsewhere (say, employees on dial-up connections).
If your searches by IP are primarily for outside accesses,
you probably do not need to index the IP range that corresponds to your
organization's subnet.
Assume a table like this:
CREATE TABLE access_log (
url varchar,
client_ip inet,
...
);
To create a partial index that suits our example, use a command
such as this:
CREATE INDEX access_log_client_ip_ix ON access_log (client_ip)
WHERE NOT (client_ip > inet '192.168.100.0' AND
client_ip < inet '192.168.100.255');
A typical query that can use this index would be:
SELECT *
FROM access_log
WHERE url = '/index.html' AND client_ip = inet '212.78.10.32';
Here the query's IP address is covered by the partial index. The
following query cannot use the partial index, as it uses an IP address
that is excluded from the index:
SELECT *
FROM access_log
WHERE url = '/index.html' AND client_ip = inet '192.168.100.23';
Observe that this kind of partial index requires that the common
values be predetermined, so such partial indexes are best used for
data distributions that do not change. Such indexes can be recreated
occasionally to adjust for new data distributions, but this adds
maintenance effort.
Another possible use for a partial index is to exclude values from the
index that the
typical query workload is not interested in; this is shown in . This results in the same
advantages as listed above, but it prevents the
uninteresting values from being accessed via that
index, even if an index scan might be profitable in that
case. Obviously, setting up partial indexes for this kind of
scenario will require a lot of care and experimentation.
Setting up a Partial Index to Exclude Uninteresting Values
If you have a table that contains both billed and unbilled orders,
where the unbilled orders take up a small fraction of the total
table and yet those are the most-accessed rows, you can improve
performance by creating an index on just the unbilled rows. The
command to create the index would look like this:
CREATE INDEX orders_unbilled_index ON orders (order_nr)
WHERE billed is not true;
A possible query to use this index would be:
SELECT * FROM orders WHERE billed is not true AND order_nr < 10000;
However, the index can also be used in queries that do not involve
order_nr at all, e.g.:
SELECT * FROM orders WHERE billed is not true AND amount > 5000.00;
This is not as efficient as a partial index on the
amount column would be, since the system has to
scan the entire index. Yet, if there are relatively few unbilled
orders, using this partial index just to find the unbilled orders
could be a win.
Note that this query cannot use this index:
SELECT * FROM orders WHERE order_nr = 3501;
The order 3501 might be among the billed or unbilled
orders.
also illustrates that the
indexed column and the column used in the predicate do not need to
match. PostgreSQL supports partial
indexes with arbitrary predicates, so long as only columns of the
table being indexed are involved. However, keep in mind that the
predicate must match the conditions used in the queries that
are supposed to benefit from the index. To be precise, a partial
index can be used in a query only if the system can recognize that
the WHERE condition of the query mathematically implies
the predicate of the index.
PostgreSQL does not have a sophisticated
theorem prover that can recognize mathematically equivalent
expressions that are written in different forms. (Not
only is such a general theorem prover extremely difficult to
create, it would probably be too slow to be of any real use.)
The system can recognize simple inequality implications, for example
x < 1 implies x < 2; otherwise
the predicate condition must exactly match part of the query's
WHERE condition
or the index will not be recognized as usable. Matching takes
place at query planning time, not at run time. As a result,
parameterized query clauses do not work with a partial index. For
example a prepared query with a parameter might specify
x < ? which will never imply
x < 2 for all possible values of the parameter.
A third possible use for partial indexes does not require the
index to be used in queries at all. The idea here is to create
a unique index over a subset of a table, as in . This enforces uniqueness
among the rows that satisfy the index predicate, without constraining
those that do not.
Setting up a Partial Unique Index
Suppose that we have a table describing test outcomes. We wish
to ensure that there is only one successful entry for
a given subject and target combination, but there might be any number of
unsuccessful entries. Here is one way to do it:
CREATE TABLE tests (
subject text,
target text,
success boolean,
...
);
CREATE UNIQUE INDEX tests_success_constraint ON tests (subject, target)
WHERE success;
This is a particularly efficient approach when there are few
successful tests and many unsuccessful ones. It is also possible to
allow only one null in a column by creating a unique partial index
with an IS NULL restriction.
Finally, a partial index can also be used to override the system's
query plan choices. Also, data sets with peculiar
distributions might cause the system to use an index when it really
should not. In that case the index can be set up so that it is not
available for the offending query. Normally,
PostgreSQL makes reasonable choices about index
usage (e.g., it avoids them when retrieving common values, so the
earlier example really only saves index size, it is not required to
avoid index usage), and grossly incorrect plan choices are cause
for a bug report.
Keep in mind that setting up a partial index indicates that you
know at least as much as the query planner knows, in particular you
know when an index might be profitable. Forming this knowledge
requires experience and understanding of how indexes in
PostgreSQL work. In most cases, the
advantage of a partial index over a regular index will be minimal.
There are cases where they are quite counterproductive, as in .
Do Not Use Partial Indexes as a Substitute for Partitioning
You might be tempted to create a large set of non-overlapping partial
indexes, for example
CREATE INDEX mytable_cat_1 ON mytable (data) WHERE category = 1;
CREATE INDEX mytable_cat_2 ON mytable (data) WHERE category = 2;
CREATE INDEX mytable_cat_3 ON mytable (data) WHERE category = 3;
...
CREATE INDEX mytable_cat_N ON mytable (data) WHERE category = N;
This is a bad idea! Almost certainly, you'll be better off with a
single non-partial index, declared like
CREATE INDEX mytable_cat_data ON mytable (category, data);
(Put the category column first, for the reasons described in
.) While a search in this larger
index might have to descend through a couple more tree levels than a
search in a smaller index, that's almost certainly going to be cheaper
than the planner effort needed to select the appropriate one of the
partial indexes. The core of the problem is that the system does not
understand the relationship among the partial indexes, and will
laboriously test each one to see if it's applicable to the current
query.
If your table is large enough that a single index really is a bad idea,
you should look into using partitioning instead (see
). With that mechanism, the system
does understand that the tables and indexes are non-overlapping, so
far better performance is possible.
More information about partial indexes can be found in , , and .
Index-Only Scans and Covering Indexesindexindex-only scansindex-only scanindexcoveringcovering index
All indexes in PostgreSQL
are secondary indexes, meaning that each index is
stored separately from the table's main data area (which is called the
table's heap
in PostgreSQL terminology). This means that
in an ordinary index scan, each row retrieval requires fetching data from
both the index and the heap. Furthermore, while the index entries that
match a given indexable WHERE condition are usually
close together in the index, the table rows they reference might be
anywhere in the heap. The heap-access portion of an index scan thus
involves a lot of random access into the heap, which can be slow,
particularly on traditional rotating media. (As described in
, bitmap scans try to alleviate
this cost by doing the heap accesses in sorted order, but that only goes
so far.)
To solve this performance problem, PostgreSQL
supports index-only scans, which can answer
queries from an index alone without any heap access. The basic idea is
to return values directly out of each index entry instead of consulting
the associated heap entry. There are two fundamental restrictions on
when this method can be used:
The index type must support index-only scans. B-tree indexes always
do. GiST and SP-GiST indexes support index-only scans for some
operator classes but not others. Other index types have no support.
The underlying requirement is that the index must physically store, or
else be able to reconstruct, the original data value for each index
entry. As a counterexample, GIN indexes cannot support index-only
scans because each index entry typically holds only part of the
original data value.
The query must reference only columns stored in the index. For
example, given an index on columns x
and y of a table that also has a
column z, these queries could use index-only scans:
SELECT x, y FROM tab WHERE x = 'key';
SELECT x FROM tab WHERE x = 'key' AND y < 42;
but these queries could not:
SELECT x, z FROM tab WHERE x = 'key';
SELECT x FROM tab WHERE x = 'key' AND z < 42;
(Expression indexes and partial indexes complicate this rule,
as discussed below.)
If these two fundamental requirements are met, then all the data values
required by the query are available from the index, so an index-only scan
is physically possible. But there is an additional requirement for any
table scan in PostgreSQL: it must verify that
each retrieved row be visible to the query's MVCC
snapshot, as discussed in . Visibility information
is not stored in index entries, only in heap entries; so at first glance
it would seem that every row retrieval would require a heap access
anyway. And this is indeed the case, if the table row has been modified
recently. However, for seldom-changing data there is a way around this
problem. PostgreSQL tracks, for each page in
a table's heap, whether all rows stored in that page are old enough to be
visible to all current and future transactions. This information is
stored in a bit in the table's visibility map. An
index-only scan, after finding a candidate index entry, checks the
visibility map bit for the corresponding heap page. If it's set, the row
is known visible and so the data can be returned with no further work.
If it's not set, the heap entry must be visited to find out whether it's
visible, so no performance advantage is gained over a standard index
scan. Even in the successful case, this approach trades visibility map
accesses for heap accesses; but since the visibility map is four orders
of magnitude smaller than the heap it describes, far less physical I/O is
needed to access it. In most situations the visibility map remains
cached in memory all the time.
In short, while an index-only scan is possible given the two fundamental
requirements, it will be a win only if a significant fraction of the
table's heap pages have their all-visible map bits set. But tables in
which a large fraction of the rows are unchanging are common enough to
make this type of scan very useful in practice.
INCLUDEin index definitions
To make effective use of the index-only scan feature, you might choose to
create a covering index, which is an index
specifically designed to include the columns needed by a particular
type of query that you run frequently. Since queries typically need to
retrieve more columns than just the ones they search
on, PostgreSQL allows you to create an index
in which some columns are just payload and are not part
of the search key. This is done by adding an INCLUDE
clause listing the extra columns. For example, if you commonly run
queries like
SELECT y FROM tab WHERE x = 'key';
the traditional approach to speeding up such queries would be to create
an index on x only. However, an index defined as
CREATE INDEX tab_x_y ON tab(x) INCLUDE (y);
could handle these queries as index-only scans,
because y can be obtained from the index without
visiting the heap.
Because column y is not part of the index's search
key, it does not have to be of a data type that the index can handle;
it's merely stored in the index and is not interpreted by the index
machinery. Also, if the index is a unique index, that is
CREATE UNIQUE INDEX tab_x_y ON tab(x) INCLUDE (y);
the uniqueness condition applies to just column x,
not to the combination of x and y.
(An INCLUDE clause can also be written
in UNIQUE and PRIMARY KEY
constraints, providing alternative syntax for setting up an index like
this.)
It's wise to be conservative about adding non-key payload columns to an
index, especially wide columns. If an index tuple exceeds the
maximum size allowed for the index type, data insertion will fail.
In any case, non-key columns duplicate data from the index's table
and bloat the size of the index, thus potentially slowing searches.
And remember that there is little point in including payload columns in an
index unless the table changes slowly enough that an index-only scan is
likely to not need to access the heap. If the heap tuple must be visited
anyway, it costs nothing more to get the column's value from there.
Other restrictions are that expressions are not currently supported as
included columns, and that only B-tree, GiST and SP-GiST indexes currently
support included columns.
Before PostgreSQL had
the INCLUDE feature, people sometimes made covering
indexes by writing the payload columns as ordinary index columns,
that is writing
CREATE INDEX tab_x_y ON tab(x, y);
even though they had no intention of ever using y as
part of a WHERE clause. This works fine as long as
the extra columns are trailing columns; making them be leading columns is
unwise for the reasons explained in .
However, this method doesn't support the case where you want the index to
enforce uniqueness on the key column(s).
Suffix truncation always removes non-key
columns from upper B-Tree levels. As payload columns, they are
never used to guide index scans. The truncation process also
removes one or more trailing key column(s) when the remaining
prefix of key column(s) happens to be sufficient to describe tuples
on the lowest B-Tree level. In practice, covering indexes without
an INCLUDE clause often avoid storing columns
that are effectively payload in the upper levels. However,
explicitly defining payload columns as non-key columns
reliably keeps the tuples in upper levels
small.
In principle, index-only scans can be used with expression indexes.
For example, given an index on f(x)
where x is a table column, it should be possible to
execute
SELECT f(x) FROM tab WHERE f(x) < 1;
as an index-only scan; and this is very attractive
if f() is an expensive-to-compute function.
However, PostgreSQL's planner is currently not
very smart about such cases. It considers a query to be potentially
executable by index-only scan only when all columns
needed by the query are available from the index. In this
example, x is not needed except in the
context f(x), but the planner does not notice that and
concludes that an index-only scan is not possible. If an index-only scan
seems sufficiently worthwhile, this can be worked around by
adding x as an included column, for example
CREATE INDEX tab_f_x ON tab (f(x)) INCLUDE (x);
An additional caveat, if the goal is to avoid
recalculating f(x), is that the planner won't
necessarily match uses of f(x) that aren't in
indexable WHERE clauses to the index column. It will
usually get this right in simple queries such as shown above, but not in
queries that involve joins. These deficiencies may be remedied in future
versions of PostgreSQL.
Partial indexes also have interesting interactions with index-only scans.
Consider the partial index shown in :
CREATE UNIQUE INDEX tests_success_constraint ON tests (subject, target)
WHERE success;
In principle, we could do an index-only scan on this index to satisfy a
query like
SELECT target FROM tests WHERE subject = 'some-subject' AND success;
But there's a problem: the WHERE clause refers
to success which is not available as a result column
of the index. Nonetheless, an index-only scan is possible because the
plan does not need to recheck that part of the WHERE
clause at run time: all entries found in the index necessarily
have success = true so this need not be explicitly
checked in the plan. PostgreSQL versions 9.6
and later will recognize such cases and allow index-only scans to be
generated, but older versions will not.
Operator Classes and Operator Familiesoperator classoperator family
An index definition can specify an operator
class for each column of an index.
CREATE INDEX name ON table (columnopclass [ ( opclass_options ) ] sort options, ...);
The operator class identifies the operators to be used by the index
for that column. For example, a B-tree index on the type int4
would use the int4_ops class; this operator
class includes comparison functions for values of type int4.
In practice the default operator class for the column's data type is
usually sufficient. The main reason for having operator classes is
that for some data types, there could be more than one meaningful
index behavior. For example, we might want to sort a complex-number data
type either by absolute value or by real part. We could do this by
defining two operator classes for the data type and then selecting
the proper class when making an index. The operator class determines
the basic sort ordering (which can then be modified by adding sort options
COLLATE,
ASC/DESC and/or
NULLS FIRST/NULLS LAST).
There are also some built-in operator classes besides the default ones:
The operator classes text_pattern_ops,
varchar_pattern_ops, and
bpchar_pattern_ops support B-tree indexes on
the types text, varchar, and
char respectively. The
difference from the default operator classes is that the values
are compared strictly character by character rather than
according to the locale-specific collation rules. This makes
these operator classes suitable for use by queries involving
pattern matching expressions (LIKE or POSIX
regular expressions) when the database does not use the standard
C locale. As an example, you might index a
varchar column like this:
CREATE INDEX test_index ON test_table (col varchar_pattern_ops);
Note that you should also create an index with the default operator
class if you want queries involving ordinary <,
<=, >, or >= comparisons
to use an index. Such queries cannot use the
xxx_pattern_ops
operator classes. (Ordinary equality comparisons can use these
operator classes, however.) It is possible to create multiple
indexes on the same column with different operator classes.
If you do use the C locale, you do not need the
xxx_pattern_ops
operator classes, because an index with the default operator class
is usable for pattern-matching queries in the C locale.
The following query shows all defined operator classes:
SELECT am.amname AS index_method,
opc.opcname AS opclass_name,
opc.opcintype::regtype AS indexed_type,
opc.opcdefault AS is_default
FROM pg_am am, pg_opclass opc
WHERE opc.opcmethod = am.oid
ORDER BY index_method, opclass_name;
An operator class is actually just a subset of a larger structure called an
operator family. In cases where several data types have
similar behaviors, it is frequently useful to define cross-data-type
operators and allow these to work with indexes. To do this, the operator
classes for each of the types must be grouped into the same operator
family. The cross-type operators are members of the family, but are not
associated with any single class within the family.
This expanded version of the previous query shows the operator family
each operator class belongs to:
SELECT am.amname AS index_method,
opc.opcname AS opclass_name,
opf.opfname AS opfamily_name,
opc.opcintype::regtype AS indexed_type,
opc.opcdefault AS is_default
FROM pg_am am, pg_opclass opc, pg_opfamily opf
WHERE opc.opcmethod = am.oid AND
opc.opcfamily = opf.oid
ORDER BY index_method, opclass_name;
This query shows all defined operator families and all
the operators included in each family:
SELECT am.amname AS index_method,
opf.opfname AS opfamily_name,
amop.amopopr::regoperator AS opfamily_operator
FROM pg_am am, pg_opfamily opf, pg_amop amop
WHERE opf.opfmethod = am.oid AND
amop.amopfamily = opf.oid
ORDER BY index_method, opfamily_name, opfamily_operator;
has
commands \dAc, \dAf,
and \dAo, which provide slightly more sophisticated
versions of these queries.
Indexes and Collations
An index can support only one collation per index column.
If multiple collations are of interest, multiple indexes may be needed.
Consider these statements:
CREATE TABLE test1c (
id integer,
content varchar COLLATE "x"
);
CREATE INDEX test1c_content_index ON test1c (content);
The index automatically uses the collation of the
underlying column. So a query of the form
SELECT * FROM test1c WHERE content > constant;
could use the index, because the comparison will by default use the
collation of the column. However, this index cannot accelerate queries
that involve some other collation. So if queries of the form, say,
SELECT * FROM test1c WHERE content > constant COLLATE "y";
are also of interest, an additional index could be created that supports
the "y" collation, like this:
CREATE INDEX test1c_content_y_index ON test1c (content COLLATE "y");
Examining Index Usageindexexamining usage
Although indexes in PostgreSQL do not need
maintenance or tuning, it is still important to check
which indexes are actually used by the real-life query workload.
Examining index usage for an individual query is done with the
command; its application for this purpose is
illustrated in .
It is also possible to gather overall statistics about index usage
in a running server, as described in .
It is difficult to formulate a general procedure for determining
which indexes to create. There are a number of typical cases that
have been shown in the examples throughout the previous sections.
A good deal of experimentation is often necessary.
The rest of this section gives some tips for that:
Always run
first. This command
collects statistics about the distribution of the values in the
table. This information is required to estimate the number of rows
returned by a query, which is needed by the planner to assign
realistic costs to each possible query plan. In absence of any
real statistics, some default values are assumed, which are
almost certain to be inaccurate. Examining an application's
index usage without having run ANALYZE is
therefore a lost cause.
See
and for more information.
Use real data for experimentation. Using test data for setting
up indexes will tell you what indexes you need for the test data,
but that is all.
It is especially fatal to use very small test data sets.
While selecting 1000 out of 100000 rows could be a candidate for
an index, selecting 1 out of 100 rows will hardly be, because the
100 rows probably fit within a single disk page, and there
is no plan that can beat sequentially fetching 1 disk page.
Also be careful when making up test data, which is often
unavoidable when the application is not yet in production.
Values that are very similar, completely random, or inserted in
sorted order will skew the statistics away from the distribution
that real data would have.
When indexes are not used, it can be useful for testing to force
their use. There are run-time parameters that can turn off
various plan types (see ).
For instance, turning off sequential scans
(enable_seqscan) and nested-loop joins
(enable_nestloop), which are the most basic plans,
will force the system to use a different plan. If the system
still chooses a sequential scan or nested-loop join then there is
probably a more fundamental reason why the index is not being
used; for example, the query condition does not match the index.
(What kind of query can use what kind of index is explained in
the previous sections.)
If forcing index usage does use the index, then there are two
possibilities: Either the system is right and using the index is
indeed not appropriate, or the cost estimates of the query plans
are not reflecting reality. So you should time your query with
and without indexes. The EXPLAIN ANALYZE
command can be useful here.
If it turns out that the cost estimates are wrong, there are,
again, two possibilities. The total cost is computed from the
per-row costs of each plan node times the selectivity estimate of
the plan node. The costs estimated for the plan nodes can be adjusted
via run-time parameters (described in ).
An inaccurate selectivity estimate is due to
insufficient statistics. It might be possible to improve this by
tuning the statistics-gathering parameters (see
).
If you do not succeed in adjusting the costs to be more
appropriate, then you might have to resort to forcing index usage
explicitly. You might also want to contact the
PostgreSQL developers to examine the issue.