index 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. 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. 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. Therefore indexes that are seldom or never used in queries should be removed. PostgreSQL provides several index types: B-tree, Hash, GiST, SP-GiST, GIN and BRIN. Each index type uses a different algorithm that is best suited to different types of queries. 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); index B-Tree B-Tree index 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. index hash hash index 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: = index GiST GiST index 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. index SP-GiST SP-GiST index 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 . index GIN GIN index 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 . index BRIN BRIN index 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 . index multicolumn 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. index and 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 LIMIT n: 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. index combining multiple indexes bitmap 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. index unique 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 , ...); 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. Null values are not considered 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. index on 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. index partial 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. 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. 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. 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 . 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 index-only scans index-only scan index covering covering 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. INCLUDE in 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 class operator family An index definition can specify an operator class for each column of an index. CREATE INDEX name ON table (column opclass [ ( 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. 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"); index examining 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.