48.6. Index Cost Estimation Functions

The amcostestimate function is given a list of WHERE clauses that have been determined to be usable with the index. It must return estimates of the cost of accessing the index and the selectivity of the WHERE clauses (that is, the fraction of parent-table rows that will be retrieved during the index scan). For simple cases, nearly all the work of the cost estimator can be done by calling standard routines in the optimizer; the point of having an amcostestimate function is to allow index access methods to provide index-type-specific knowledge, in case it is possible to improve on the standard estimates.

Each amcostestimate function must have the signature:

void
amcostestimate (PlannerInfo *root,
                IndexOptInfo *index,
                List *indexQuals,
                Cost *indexStartupCost,
                Cost *indexTotalCost,
                Selectivity *indexSelectivity,
                double *indexCorrelation);

The first four parameters are inputs:

The last four parameters are pass-by-reference outputs:

Note that cost estimate functions must be written in C, not in SQL or any available procedural language, because they must access internal data structures of the planner/optimizer.

The index access costs should be computed in the units used by src/backend/optimizer/path/costsize.c: a sequential disk block fetch has cost 1.0, a nonsequential fetch has cost random_page_cost, and the cost of processing one index row should usually be taken as cpu_index_tuple_cost. In addition, an appropriate multiple of cpu_operator_cost should be charged for any comparison operators invoked during index processing (especially evaluation of the indexQuals themselves).

The access costs should include all disk and CPU costs associated with scanning the index itself, but not the costs of retrieving or processing the parent-table rows that are identified by the index.

The "start-up costndex TERAL" >(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.

Beginning in release 8.1, 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 may 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 Section 11.3, it would be almost useless for queries involving only y, so it could 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 updat