index GiST GiST stands for Generalized Search Tree. It is a balanced, tree-structured access method, that acts as a base template in which to implement arbitrary indexing schemes. B-trees, R-trees and many other indexing schemes can be implemented in GiST. One advantage of GiST 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. Some of the information here is derived from the University of California at Berkeley's GiST Indexing Project web site and Marcel Kornacker's thesis, Access Methods for Next-Generation Database Systems. The GiST implementation in PostgreSQL is primarily maintained by Teodor Sigaev and Oleg Bartunov, and there is more information on their web site. 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 seven 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. 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 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. consistent Given an index entry p and a query value q, 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 is true, a recheck flag must also be returned. This indicates whether the predicate is certainly true or only possibly true. If recheck = false then the index has tested the predicate condition exactly, whereas if recheck = true the row is only a candidate match. In that case the system will automatically evaluate the indexable_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: Datum my_consistent(PG_FUNCTION_ARGS); 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 and query the value being looked up in the index. The StrategyNumber parameter indicates which operator of your operator class is being applied — it matches one of the operator numbers in the CREATE OPERATOR CLASS command. Depending on what operators you have included in the class, the data type of query could vary with the operator, but the above skeleton assumes it doesn't. 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 internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT; And the matching code in the C module could then follow this skeleton: Datum my_union(PG_FUNCTION_ARGS); 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 union implementation function should return a pointer to newly palloc()ed memory. You can't just return whatever the input is. 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: Datum my_compress(PG_FUNCTION_ARGS); 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. Depending on your needs, you could also need to care about compressing NULL values in there, storing for example (Datum) 0 like gist_circle_compress does. decompress The reverse of the compress method. Converts the index representation of the data item into a format that can be manipulated by the database. 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: Datum my_decompress(PG_FUNCTION_ARGS); 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. 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: Datum my_penalty(PG_FUNCTION_ARGS); 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); } 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: Datum my_picksplit(PG_FUNCTION_ARGS); PG_FUNCTION_INFO_V1(my_picksplit); Datum my_picksplit(PG_FUNCTION_ARGS) { GistEntryVector *entryvec = (GistEntryVector *) PG_GETARG_POINTER(0); OffsetNumber maxoff = entryvec->n - 1; GISTENTRY *ent = entryvec->vector; GIST_SPLITVEC *v = (GIST_SPLITVEC *) PG_GETARG_POINTER(1); 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); } Like penalty, the picksplit function is crucial to good performance of the index. Designing suitable penalty and picksplit implementations is where the challenge of implementing well-performing GiST indexes lies. same Returns true if two index entries are identical, false otherwise. The SQL declaration of the function must look like this: CREATE OR REPLACE FUNCTION my_same(internal, internal, internal) RETURNS internal AS 'MODULE_PATHNAME' LANGUAGE C STRICT; And the matching code in the C module could then follow this skeleton: Datum my_same(PG_FUNCTION_ARGS); 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 PostgreSQL source distribution includes several examples of index methods implemented using GiST. The core system currently provides text search support (indexing for tsvector and tsquery) as well as R-Tree equivalent functionality for some of the built-in geometric data types (see src/backend/access/gist/gistproc.c). The following contrib modules also contain GiST operator classes: btree_gist B-tree equivalent functionality for several data types cube Indexing for multidimensional cubes hstore Module for storing (key, value) pairs intarray RD-Tree for one-dimensional array of int4 values ltree Indexing for tree-like structures pg_trgm Text similarity using trigram matching seg Indexing for float ranges Usually, replay of the WAL log is sufficient to restore the integrity of a GiST index following a database crash. However, there are some corner cases in which the index state is not fully rebuilt. The index will still be functionally correct, but there might be some performance degradation. When this occurs, the index can be repaired by VACUUMing its table, or by rebuilding the index using REINDEX. In some cases a plain VACUUM is not sufficient, and either VACUUM FULL or REINDEX is needed. The need for one of these procedures is indicated by occurrence of this log message during crash recovery: LOG: index NNN/NNN/NNN needs VACUUM or REINDEX to finish crash recovery or this log message during routine index insertions: LOG: index "FOO" needs VACUUM or REINDEX to finish crash recovery If a plain VACUUM finds itself unable to complete recovery fully, it will return a notice: NOTICE: index "FOO" needs VACUUM FULL or REINDEX to finish crash recovery