GIN stands for Generalized Inverted Index. GIN is designed for handling cases where the items to be indexed are composite values, and the queries to be handled by the index need to search for element values that appear within the composite items. For example, the items could be documents, and the queries could be searches for documents containing specific words.
We use the word item to refer to a composite value that is to be indexed, and the word key to refer to an element value. GIN always stores and searches for keys, not item values per se.
A GIN index stores a set of (key, posting list) pairs, where a posting list is a set of row IDs in which the key occurs. The same row ID can appear in multiple posting lists, since an item can contain more than one key. Each key value is stored only once, so a GIN index is very compact for cases where the same key appears many times.
GIN is generalized in the sense that the GIN access method code does not need to know the specific operations that it accelerates. Instead, it uses custom strategies defined for particular data types. The strategy defines how keys are extracted from indexed items and query conditions, and how to determine whether a row that contains some of the key values in a query actually satisfies the query.
One advantage of GIN is that it allows the development of custom data types with the appropriate access methods, by an expert in the domain of the data type, rather than a database expert. This is much the same advantage as using GiST.
The GIN implementation in PostgreSQL is primarily maintained by Teodor Sigaev and Oleg Bartunov. There is more information about GIN on their website.
The core PostgreSQL distribution includes the GIN operator classes shown in Table 66.3. (Some of the optional modules described in Appendix F provide additional GIN operator classes.)
Table 66.3. Built-in GIN Operator Classes
| Name | Indexable Operators |
|---|---|
array_ops | && (anyarray,anyarray) |
@> (anyarray,anyarray) | |
<@ (anyarray,anyarray) | |
= (anyarray,anyarray) | |
jsonb_ops | @> (jsonb,jsonb) |
@? (jsonb,jsonpath) | |
@@ (jsonb,jsonpath) | |
? (jsonb,text) | |
?| (jsonb,text[]) | |
?& (jsonb,text[]) | |
jsonb_path_ops | @> (jsonb,jsonb) |
@? (jsonb,jsonpath) | |
@@ (jsonb,jsonpath) | |
tsvector_ops | @@ (tsvector,tsquery) |
Of the two operator classes for type jsonb, jsonb_ops
is the default. jsonb_path_ops supports fewer operators but
offers better performance for those operators.
See Section 8.14.4 for details.
The GIN interface has a high level of abstraction, requiring the access method implementer only to implement the semantics of the data type being accessed. The GIN layer itself takes care of concurrency, logging and searching the tree structure.
All it takes to get a GIN access method working is to implement a few user-defined methods, which define the behavior of keys in the tree and the relationships between keys, indexed items, and indexable queries. In short, GIN combines extensibility with generality, code reuse, and a clean interface.
There are two methods that an operator class for GIN must provide:
Datum *extractValue(Datum itemValue, int32 *nkeys,
bool **nullFlags)
Returns a palloc'd array of keys given an item to be indexed. The
number of returned keys must be stored into *nkeys.
If any of the keys can be null, also palloc an array of
*nkeys bool fields, store its address at
*nullFlags, and set these null flags as needed.
*nullFlags can be left NULL (its initial value)
if all keys are non-null.
The return value can be NULL if the item contains no keys.
Datum *extractQuery(Datum query, int32 *nkeys,
StrategyNumber n, bool **pmatch, Pointer **extra_data,
bool **nullFlags, int32 *searchMode)
Returns a palloc'd array of keys given a value to be queried; that is,
query is the value on the right-hand side of an
indexable operator whose left-hand side is the indexed column.
n is the strategy number of the operator within the
operator class (see Section 38.16.2).
Often, extractQuery will need
to consult n to determine the data type of
query and the method it should use to extract key values.
The number of returned keys must be stored into *nkeys.
If any of the keys can be null, also palloc an array of
*nkeys bool fields, store its address at
*nullFlags, and set these null flags as needed.
*nullFlags can be left NULL (its initial value)
if all keys are non-null.
The return value can be NULL if the query contains no keys.
searchMode is an output argument that allows
extractQuery to specify details about how the search
will be done.
If *searchMode is set to
GIN_SEARCH_MODE_DEFAULT (which is the value it is
initialized to before call), only items that match at least one of
the returned keys are considered candidate matches.
If *searchMode is set to
GIN_SEARCH_MODE_INCLUDE_EMPTY, then in addition to items
containing at least one matching key, items that contain no keys at
all are considered candidate matches. (This mode is useful for
implementing is-subset-of operators, for example.)
If *searchMode is set to GIN_SEARCH_MODE_ALL,
then all non-null items in the index are considered candidate
matches, whether they match any of the returned keys or not. (This
mode is much slower than the other two choices, since it requires
scanning essentially the entire index, but it may be necessary to
implement corner cases correctly. An operator that needs this mode
in most cases is probably not a good candidate for a GIN operator
class.)
The symbols to use for setting this mode are defined in
access/gin.h.
pmatch is an output argument for use when partial match
is supported. To use it, extractQuery must allocate
an array of *nkeys bools and store its address at
*pmatch. Each element of the array should be set to true
if the corresponding key requires partial match, false if not.
If *pmatch is set to NULL then GIN assumes partial match
is not required. The variable is initialized to NULL before call,
so this argument can simply be ignored by operator classes that do
not support partial match.
extra_data is an output argument that allows
extractQuery to pass additional data to the
consistent and comparePartial methods.
To use it, extractQuery must allocate
an array of *nkeys pointers and store its address at
*extra_data, then store whatever it wants to into the
individual pointers. The variable is initialized to NULL before
call, so this argument can simply be ignored by operator classes that
do not require extra data. If *extra_data is set, the
whole array is passed to the consistent method, and
the appropriate element to the comparePartial method.
An operator class must also provide a function to check if an indexed item
matches the query. It comes in two flavors, a Boolean consistent
function, and a ternary triConsistent function.
triConsistent covers the functionality of both, so providing
triConsistent alone is sufficient. However, if the Boolean
variant is significantly cheaper to calculate, it can be advantageous to
provide both. If only the Boolean variant is provided, some optimizations
that depend on refuting index items before fetching all the keys are
disabled.
bool consistent(bool check[], StrategyNumber n, Datum query,
int32 nkeys, Pointer extra_data[], bool *recheck,
Datum queryKeys[], bool nullFlags[])
Returns true if an indexed item satisfies the query operator with
strategy number n (or might satisfy it, if the recheck
indication is returned). This function does not have direct access
to the indexed item's value, since GIN does not
store items explicitly. Rather, what is available is knowledge
about which key values extracted from the query appear in a given
indexed item. The check array has length
nkeys, which is the same as the number of keys previously
returned by extractQuery for this query datum.
Each element of the
check array is true if the indexed item contains the
corresponding query key, i.e., if (check[i] == true) the i-th key of the
extractQuery result array is present in the indexed item.
The original query datum is
passed in case the consistent method needs to consult it,
and so are the queryKeys[] and nullFlags[]
arrays previously returned by extractQuery.
extra_data is the extra-data array returned by
extractQuery, or NULL if none.
When extractQuery returns a null key in
queryKeys[], the corresponding check[] element
is true if the indexed item contains a null key; that is, the
semantics of check[] are like IS NOT DISTINCT
FROM. The consistent function can examine the
corresponding nullFlags[] element if it needs to tell
the difference between a regular value match and a null match.
On success, *recheck should be set to true if the heap
tuple needs to be rechecked against the query operator, or false if
the index test is exact. That is, a false return value guarantees
that the heap tuple does not match the query; a true return value with
*recheck set to false guarantees that the heap tuple does
match the query; and a true return value with
*recheck set to true means that the heap tuple might match
the query, so it needs to be fetched and rechecked by evaluating the
query operator directly against the originally indexed item.
GinTernaryValue triConsistent(GinTernaryValue check[], StrategyNumber n, Datum query,
int32 nkeys, Pointer extra_data[],
Datum queryKeys[], bool nullFlags[])
triConsistent is similar to consistent,
but instead of Booleans in the check vector, there are
three possible values for each
key: GIN_TRUE, GIN_FALSE and
GIN_MAYBE. GIN_FALSE and GIN_TRUE
have the same meaning as regular Boolean values, while
GIN_MAYBE means that the presence of that key is not known.
When GIN_MAYBE values are present, the function should only
return GIN_TRUE if the item certainly matches whether or
not the index item contains the corresponding query keys. Likewise, the
function must return GIN_FALSE only if the item certainly
does not match, whether or not it contains the GIN_MAYBE
keys. If the result depends on the GIN_MAYBE entries, i.e.,
the match cannot be confirmed or refuted based on the known query keys,
the function must return GIN_MAYBE.
When there are no GIN_MAYBE values in the check
vector, a GIN_MAYBE return value is the equivalent of
setting the recheck flag in the
Boolean consistent function.
In addition, GIN must have a way to sort the key values stored in the index. The operator class can define the sort ordering by specifying a comparison method:
int compare(Datum a, Datum b)Compares two keys (not indexed items!) and returns an integer less than zero, zero, or greater than zero, indicating whether the first key is less than, equal to, or greater than the second. Null keys are never passed to this function.
Alternatively, if the operator class does not provide a compare
method, GIN will look up the default btree operator class for the index
key data type, and use its comparison function. It is recommended to
specify the comparison function in a GIN operator class that is meant for
just one data type, as looking up the btree operator class costs a few
cycles. However, polymorphic GIN operator classes (such
as array_ops) typically cannot specify a single comparison
function.
An operator class for GIN can optionally supply the following methods:
int comparePartial(Datum partial_key, Datum key, StrategyNumber n,
Pointer extra_data)
Compare a partial-match query key to an index key. Returns an integer
whose sign indicates the result: less than zero means the index key
does not match the query, but the index scan should continue; zero
means that the index key does match the query; greater than zero
indicates that the index scan should stop because no more matches
are possible. The strategy number n of the operator
that generated the partial match query is provided, in case its
semantics are needed to determine when to end the scan. Also,
extra_data is the corresponding element of the extra-data
array made by extractQuery, or NULL if none.
Null keys are never passed to this function.
void options(local_relopts *relopts)Defines a set of user-visible parameters that control operator class behavior.
The options function is passed a pointer to a
local_relopts struct, which needs to be
filled with a set of operator class specific options. The options
can be accessed from other support functions using the
PG_HAS_OPCLASS_OPTIONS() and
PG_GET_OPCLASS_OPTIONS() macros.
Since both key extraction of indexed values and representation of the key in GIN are flexible, they may depend on user-specified parameters.
To support “partial match” queries, an operator class must
provide the comparePartial method, and its
extractQuery method must set the pmatch
parameter when a partial-match query is encountered. See
Section 66.4.4.2 for details.
The actual data types of the various Datum values mentioned
above vary depending on the operator class. The item values passed to
extractValue are always of the operator class's input type, and
all key values must be of the class's STORAGE type. The type of
the query argument passed to extractQuery,
consistent and triConsistent is whatever is the
right-hand input type of the class member operator identified by the
strategy number. This need not be the same as the indexed type, so long as
key values of the correct type can be extracted from it. However, it is
recommended that the SQL declarations of these three support functions use
the opclass's indexed data type for the query argument, even
though the actual type might be something else depending on the operator.
Internally, a GIN index contains a B-tree index constructed over keys, where each key is an element of one or more indexed items (a member of an array, for example) and where each tuple in a leaf page contains either a pointer to a B-tree of heap pointers (a “posting tree”), or a simple list of heap pointers (a “posting list”) when the list is small enough to fit into a single index tuple along with the key value. Figure 66.1 illustrates these components of a GIN index.
As of PostgreSQL 9.1, null key values can be
included in the index. Also, placeholder nulls are included in the index
for indexed items that are null or contain no keys according to
extractValue. This allows searches that should find empty
items to do so.
Multicolumn GIN indexes are implemented by building a single B-tree over composite values (column number, key value). The key values for different columns can be of different types.
Figure 66.1. GIN Internals
Updating a GIN index tends to be slow because of the
intrinsic nature of inverted indexes: inserting or updating one heap row
can cause many inserts into the index (one for each key extracted
from the indexed item).
GIN is capable of postponing much of this work by inserting
new tuples into a temporary, unsorted list of pending entries.
When the table is vacuumed or autoanalyzed, or when
gin_clean_pending_list function is called, or if the
pending list becomes larger than
gin_pending_list_limit, the entries are moved to the
main GIN data structure using the same bulk insert
techniques used during initial index creation. This greatly improves
GIN index update speed, even counting the additional
vacuum overhead. Moreover the overhead work can be done by a background
process instead of in foreground query processing.
The main disadvantage of this approach is that searches must scan the list of pending entries in addition to searching the regular index, and so a large list of pending entries will slow searches significantly. Another disadvantage is that, while most updates are fast, an update that causes the pending list to become “too large” will incur an immediate cleanup cycle and thus be much slower than other updates. Proper use of autovacuum can minimize both of these problems.
If consistent response time is more important than update speed,
use of pending entries can be disabled by turning off the
fastupdate storage parameter for a
GIN index. See CREATE INDEX
for details.
GIN can support “partial match” queries, in which the query
does not determine an exact match for one or more keys, but the possible
matches fall within a reasonably narrow range of key values (within the
key sorting order determined by the compare support method).
The extractQuery method, instead of returning a key value
to be matched exactly, returns a key value that is the lower bound of
the range to be searched, and sets the pmatch flag true.
The key range is then scanned using the comparePartial
method. comparePartial must return zero for a matching
index key, less than zero for a non-match that is still within the range
to be searched, or greater than zero if the index key is past the range
that could match.
Insertion into a GIN index can be slow due to the likelihood of many keys being inserted for each item. So, for bulk insertions into a table it is advisable to drop the GIN index and recreate it after finishing bulk insertion.
When fastupdate is enabled for GIN
(see Section 66.4.4.1 for details), the penalty is
less than when it is not. But for very large updates it may still be
best to drop and recreate the index.
Build time for a GIN index is very sensitive to
the maintenance_work_mem setting; it doesn't pay to
skimp on work memory during index creation.
During a series of insertions into an existing GIN
index that has fastupdate enabled, the system will clean up
the pending-entry list whenever the list grows larger than
gin_pending_list_limit. To avoid fluctuations in observed
response time, it's desirable to have pending-list cleanup occur in the
background (i.e., via autovacuum). Foreground cleanup operations
can be avoided by increasing gin_pending_list_limit
or making autovacuum more aggressive.
However, enlarging the threshold of the cleanup operation means that
if a foreground cleanup does occur, it will take even longer.
gin_pending_list_limit can be overridden for individual
GIN indexes by changing storage parameters, which allows each
GIN index to have its own cleanup threshold.
For example, it's possible to increase the threshold only for the GIN
index which can be updated heavily, and decrease it otherwise.
The primary goal of developing GIN indexes was to create support for highly scalable full-text search in PostgreSQL, and there are often situations when a full-text search returns a very large set of results. Moreover, this often happens when the query contains very frequent words, so that the large result set is not even useful. Since reading many tuples from the disk and sorting them could take a lot of time, this is unacceptable for production. (Note that the index search itself is very fast.)
To facilitate controlled execution of such queries,
GIN has a configurable soft upper limit on the
number of rows returned: the
gin_fuzzy_search_limit configuration parameter.
It is set to 0 (meaning no limit) by default.
If a non-zero limit is set, then the returned set is a subset of
the whole result set, chosen at random.
“Soft” means that the actual number of returned results could differ somewhat from the specified limit, depending on the query and the quality of the system's random number generator.
From experience, values in the thousands (e.g., 5000 — 20000) work well.
GIN assumes that indexable operators are strict. This
means that extractValue will not be called at all on a null
item value (instead, a placeholder index entry is created automatically),
and extractQuery will not be called on a null query
value either (instead, the query is presumed to be unsatisfiable). Note
however that null key values contained within a non-null composite item
or query value are supported.
The core PostgreSQL distribution
includes the GIN operator classes previously shown in
Table 66.3.
The following contrib modules also contain
GIN operator classes:
btree_ginB-tree equivalent functionality for several data types
hstoreModule for storing (key, value) pairs
intarrayEnhanced support for int[]
pg_trgmText similarity using trigram matching