Traditionally, implementing a new index access method meant a lot of difficult work. It was necessary to understand the inner workings of the database, such as the lock manager and Write-Ahead Log. The GiST interface has a high level of abstraction, requiring the access method implementer only to implement the semantics of the data type being accessed. The GiST layer itself takes care of concurrency, logging and searching the tree structure.
This extensibility should not be confused with the extensibility of the other standard search trees in terms of the data they can handle. For example, PostgreSQL supports extensible B-trees and hash indexes. That means that you can use PostgreSQL to build a B-tree or hash over any data type you want. But B-trees only support range predicates (<
, =
, >
), and hash indexes only support equality queries.
So if you index, say, an image collection with a PostgreSQL B-tree, you can only issue queries such as "is imagex equal to imagey", "is imagex less than imagey" and "is imagex greater than imagey". Depending on how you define "equals", "less than" and "greater than" in this context, this could be useful. However, by using a GiST based index, you could create ways to ask domain-specific questions, perhaps "find all images of horses" or "find all over-exposed images".
All it takes to get a GiST access method up and running is to implement several user-defined methods, which define the behavior of keys in the tree. Of course these methods have to be pretty fancy to support fancy queries, but for all the standard queries (B-trees, R-trees, etc.) they're relatively straightforward. In short, GiST combines extensibility along with generality, code reuse, and a clean interface.
There are seven methods that an index operator class for GiST must provide, and two that are optional. Correctness of the index is ensured by proper implementation of the same
, consistent
and union
methods, while efficiency (size and speed) of the index will depend on the penalty
and picksplit
methods. The remaining two basic methods are compress
and decompress
, which allow an index to have internal tree data of a different type than the data it indexes. The leaves are to be of the indexed data type, while the other tree nodes can be of any C struct (but you still have to follow PostgreSQL data type rules here, see about varlena
for variable sized data). If the tree's internal data type exists at the SQL level, the STORAGE
option of the CREATE OPERATOR CLASS
command can be used. The optional eighth method is distance
, which is needed if the operator class wishes to support ordered scans (nearest-neighbor searches). The optional ninth method fetch
is needed if the operator class wishes to support index-only scans.
consistent
-
Given an index entry
p
and a query valueq
, this function determines whether the index entry is "consistent" with the query; that is, could the predicate "indexed_column
indexable_operator
q
" be true for any row represented by the index entry? For a leaf index entry this is equivalent to testing the indexable condition, while for an internal tree node this determines whether it is necessary to scan the subtree of the index represented by the tree node. When the result istrue
, arecheck
flag must also be returned. This indicates whether the predicate is certainly true or only possibly true. Ifrecheck
=false
then the index has tested the predicate condition exactly, whereas ifrecheck
=true
the row is only a candidate match. In that case the system will automatically evaluate theindexable_operator
against the actual row value to see if it is really a match. This convention allows GiST to support both lossless and lossy index structures.The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_consistent(internal, data_type, smallint, oid, internal) RETURNS bool AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_consistent); Datum my_consistent(PG_FUNCTION_ARGS) { GISTENTRY *entry = (GISTENTRY *) PG_GETARG_POINTER(0); data_type *query = PG_GETARG_DATA_TYPE_P(1); StrategyNumber strategy = (StrategyNumber) PG_GETARG_UINT16(2); /* Oid subtype = PG_GETARG_OID(3); */ bool *recheck = (bool *) PG_GETARG_POINTER(4); data_type *key = DatumGetDataType(entry->key); bool retval; /* * determine return value as a function of strategy, key and query. * * Use GIST_LEAF(entry) to know where you're called in the index tree, * which comes handy when supporting the = operator for example (you could * check for non empty union() in non-leaf nodes and equality in leaf * nodes). */ *recheck = true; /* or false if check is exact */ PG_RETURN_BOOL(retval); }
Here,
key
is an element in the index andquery
the value being looked up in the index. TheStrategyNumber
parameter indicates which operator of your operator class is being applied — it matches one of the operator numbers in theCREATE OPERATOR CLASS
command.Depending on which operators you have included in the class, the data type of
query
could vary with the operator, since it will be whatever type is on the righthand side of the operator, which might be different from the indexed data type appearing on the lefthand side. (The above code skeleton assumes that only one type is possible; if not, fetching thequery
argument value would have to depend on the operator.) It is recommended that the SQL declaration of theconsistent
function use the opclass's indexed data type for thequery
argument, even though the actual type might be something else depending on the operator. union
-
This method consolidates information in the tree. Given a set of entries, this function generates a new index entry that represents all the given entries.
The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_union(internal, internal) RETURNS storage_type AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_union); Datum my_union(PG_FUNCTION_ARGS) { GistEntryVector *entryvec = (GistEntryVector *) PG_GETARG_POINTER(0); GISTENTRY *ent = entryvec->vector; data_type *out, *tmp, *old; int numranges, i = 0; numranges = entryvec->n; tmp = DatumGetDataType(ent[0].key); out = tmp; if (numranges == 1) { out = data_type_deep_copy(tmp); PG_RETURN_DATA_TYPE_P(out); } for (i = 1; i < numranges; i++) { old = out; tmp = DatumGetDataType(ent[i].key); out = my_union_implementation(out, tmp); } PG_RETURN_DATA_TYPE_P(out); }
As you can see, in this skeleton we're dealing with a data type where
union(X, Y, Z) = union(union(X, Y), Z)
. It's easy enough to support data types where this is not the case, by implementing the proper union algorithm in this GiST support method.The result of the
union
function must be a value of the index's storage type, whatever that is (it might or might not be different from the indexed column's type). Theunion
function should return a pointer to newlypalloc()
ed memory. You can't just return the input value as-is, even if there is no type change.As shown above, the
union
function's firstinternal
argument is actually aGistEntryVector
pointer. The second argument is a pointer to an integer variable, which can be ignored. (It used to be required that theunion
function store the size of its result value into that variable, but this is no longer necessary.) compress
-
Converts the data item into a format suitable for physical storage in an index page.
The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_compress(internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_compress); Datum my_compress(PG_FUNCTION_ARGS) { GISTENTRY *entry = (GISTENTRY *) PG_GETARG_POINTER(0); GISTENTRY *retval; if (entry->leafkey) { /* replace entry->key with a compressed version */ compressed_data_type *compressed_data = palloc(sizeof(compressed_data_type)); /* fill *compressed_data from entry->key ... */ retval = palloc(sizeof(GISTENTRY)); gistentryinit(*retval, PointerGetDatum(compressed_data), entry->rel, entry->page, entry->offset, FALSE); } else { /* typically we needn't do anything with non-leaf entries */ retval = entry; } PG_RETURN_POINTER(retval); }
You have to adapt
compressed_data_type
to the specific type you're converting to in order to compress your leaf nodes, of course. decompress
-
The reverse of the
compress
method. Converts the index representation of the data item into a format that can be manipulated by the other GiST methods in the operator class.The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_decompress(internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_decompress); Datum my_decompress(PG_FUNCTION_ARGS) { PG_RETURN_POINTER(PG_GETARG_POINTER(0)); }
The above skeleton is suitable for the case where no decompression is needed.
penalty
-
Returns a value indicating the "cost" of inserting the new entry into a particular branch of the tree. Items will be inserted down the path of least
penalty
in the tree. Values returned bypenalty
should be non-negative. If a negative value is returned, it will be treated as zero.The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_penalty(internal, internal, internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT; -- in some cases penalty functions need not be strict
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_penalty); Datum my_penalty(PG_FUNCTION_ARGS) { GISTENTRY *origentry = (GISTENTRY *) PG_GETARG_POINTER(0); GISTENTRY *newentry = (GISTENTRY *) PG_GETARG_POINTER(1); float *penalty = (float *) PG_GETARG_POINTER(2); data_type *orig = DatumGetDataType(origentry->key); data_type *new = DatumGetDataType(newentry->key); *penalty = my_penalty_implementation(orig, new); PG_RETURN_POINTER(penalty); }
For historical reasons, the
penalty
function doesn't just return afloat
result; instead it has to store the value at the location indicated by the third argument. The return value per se is ignored, though it's conventional to pass back the address of that argument.The
penalty
function is crucial to good performance of the index. It'll get used at insertion time to determine which branch to follow when choosing where to add the new entry in the tree. At query time, the more balanced the index, the quicker the lookup. picksplit
-
When an index page split is necessary, this function decides which entries on the page are to stay on the old page, and which are to move to the new page.
The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_picksplit(internal, internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_picksplit); Datum my_picksplit(PG_FUNCTION_ARGS) { GistEntryVector *entryvec = (GistEntryVector *) PG_GETARG_POINTER(0); GIST_SPLITVEC *v = (GIST_SPLITVEC *) PG_GETARG_POINTER(1); OffsetNumber maxoff = entryvec->n - 1; GISTENTRY *ent = entryvec->vector; int i, nbytes; OffsetNumber *left, *right; data_type *tmp_union; data_type *unionL; data_type *unionR; GISTENTRY **raw_entryvec; maxoff = entryvec->n - 1; nbytes = (maxoff + 1) * sizeof(OffsetNumber); v->spl_left = (OffsetNumber *) palloc(nbytes); left = v->spl_left; v->spl_nleft = 0; v->spl_right = (OffsetNumber *) palloc(nbytes); right = v->spl_right; v->spl_nright = 0; unionL = NULL; unionR = NULL; /* Initialize the raw entry vector. */ raw_entryvec = (GISTENTRY **) malloc(entryvec->n * sizeof(void *)); for (i = FirstOffsetNumber; i <= maxoff; i = OffsetNumberNext(i)) raw_entryvec[i] = &(entryvec->vector[i]); for (i = FirstOffsetNumber; i <= maxoff; i = OffsetNumberNext(i)) { int real_index = raw_entryvec[i] - entryvec->vector; tmp_union = DatumGetDataType(entryvec->vector[real_index].key); Assert(tmp_union != NULL); /* * Choose where to put the index entries and update unionL and unionR * accordingly. Append the entries to either v_spl_left or * v_spl_right, and care about the counters. */ if (my_choice_is_left(unionL, curl, unionR, curr)) { if (unionL == NULL) unionL = tmp_union; else unionL = my_union_implementation(unionL, tmp_union); *left = real_index; ++left; ++(v->spl_nleft); } else { /* * Same on the right */ } } v->spl_ldatum = DataTypeGetDatum(unionL); v->spl_rdatum = DataTypeGetDatum(unionR); PG_RETURN_POINTER(v); }
Notice that the
picksplit
function's result is delivered by modifying the passed-inv
structure. The return value per se is ignored, though it's conventional to pass back the address ofv
.Like
penalty
, thepicksplit
function is crucial to good performance of the index. Designing suitablepenalty
andpicksplit
implementations is where the challenge of implementing well-performing GiST indexes lies. same
-
Returns true if two index entries are identical, false otherwise. (An "index entry" is a value of the index's storage type, not necessarily the original indexed column's type.)
The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_same(storage_type, storage_type, internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_same); Datum my_same(PG_FUNCTION_ARGS) { prefix_range *v1 = PG_GETARG_PREFIX_RANGE_P(0); prefix_range *v2 = PG_GETARG_PREFIX_RANGE_P(1); bool *result = (bool *) PG_GETARG_POINTER(2); *result = my_eq(v1, v2); PG_RETURN_POINTER(result); }
For historical reasons, the
same
function doesn't just return a Boolean result; instead it has to store the flag at the location indicated by the third argument. The return value per se is ignored, though it's conventional to pass back the address of that argument. distance
-
Given an index entry
p
and a query valueq
, this function determines the index entry's "distance" from the query value. This function must be supplied if the operator class contains any ordering operators. A query using the ordering operator will be implemented by returning index entries with the smallest "distance" values first, so the results must be consistent with the operator's semantics. For a leaf index entry the result just represents the distance to the index entry; for an internal tree node, the result must be the smallest distance that any child entry could have.The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_distance(internal, data_type, smallint, oid, internal) RETURNS float8 AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
And the matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_distance); Datum my_distance(PG_FUNCTION_ARGS) { GISTENTRY *entry = (GISTENTRY *) PG_GETARG_POINTER(0); data_type *query = PG_GETARG_DATA_TYPE_P(1); StrategyNumber strategy = (StrategyNumber) PG_GETARG_UINT16(2); /* Oid subtype = PG_GETARG_OID(3); */ /* bool *recheck = (bool *) PG_GETARG_POINTER(4); */ data_type *key = DatumGetDataType(entry->key); double retval; /* * determine return value as a function of strategy, key and query. */ PG_RETURN_FLOAT8(retval); }
The arguments to the
distance
function are identical to the arguments of theconsistent
function.Some approximation is allowed when determining the distance, so long as the result is never greater than the entry's actual distance. Thus, for example, distance to a bounding box is usually sufficient in geometric applications. For an internal tree node, the distance returned must not be greater than the distance to any of the child nodes. If the returned distance is not exact, the function must set
*recheck
to true. (This is not necessary for internal tree nodes; for them, the calculation is always assumed to be inexact.) In this case the executor will calculate the accurate distance after fetching the tuple from the heap, and reorder the tuples if necessary.If the distance function returns
*recheck = true
for any leaf node, the original ordering operator's return type must befloat8
orfloat4
, and the distance function's result values must be comparable to those of the original ordering operator, since the executor will sort using both distance function results and recalculated ordering-operator results. Otherwise, the distance function's result values can be any finitefloat8
values, so long as the relative order of the result values matches the order returned by the ordering operator. (Infinity and minus infinity are used internally to handle cases such as nulls, so it is not recommended thatdistance
functions return these values.) fetch
-
Converts the compressed index representation of a data item into the original data type, for index-only scans. The returned data must be an exact, non-lossy copy of the originally indexed value.
The SQL declaration of the function must look like this:
CREATE OR REPLACE FUNCTION my_fetch(internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT;
The argument is a pointer to a
GISTENTRY
struct. On entry, itskey
field contains a non-NULL leaf datum in compressed form. The return value is anotherGISTENTRY
struct, whosekey
field contains the same datum in its original, uncompressed form. If the opclass's compress function does nothing for leaf entries, thefetch
method can return the argument as-is.The matching code in the C module could then follow this skeleton:
PG_FUNCTION_INFO_V1(my_fetch); Datum my_fetch(PG_FUNCTION_ARGS) { GISTENTRY *entry = (GISTENTRY *) PG_GETARG_POINTER(0); input_data_type *in = DatumGetP(entry->key); fetched_data_type *fetched_data; GISTENTRY *retval; retval = palloc(sizeof(GISTENTRY)); fetched_data = palloc(sizeof(fetched_data_type)); /* * Convert 'fetched_data' into the a Datum of the original datatype. */ /* fill *retval from fetch_data. */ gistentryinit(*retval, PointerGetDatum(converted_datum), entry->rel, entry->page, entry->offset, FALSE); PG_RETURN_POINTER(retval); }
If the compress method is lossy for leaf entries, the operator class cannot support index-only scans, and must not define a
fetch
function.
All the GiST support methods are normally called in short-lived memory contexts; that is, CurrentMemoryContext
will get reset after each tuple is processed. It is therefore not very important to worry about pfree'ing everything you palloc. However, in some cases it's useful for a support method to cache data across repeated calls. To do that, allocate the longer-lived data in fcinfo->flinfo->fn_mcxt
, and keep a pointer to it in fcinfo->flinfo->fn_extra
. Such data will survive for the life of the index operation (e.g., a single GiST index scan, index build, or index tuple insertion). Be careful to pfree the previous value when replacing a fn_extra
value, or the leak will accumulate for the duration of the operation.
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