Author: Written by Tom Lane.
A PostgreSQL operator definition can include several optional clauses that tell the system useful things about how the operator behaves. These clauses should be provided whenever appropriate, because they can make for considerable speedups in execution of queries that use the operator. But if you provide them, you must be sure that they are right! Incorrect use of an optimization clause can result in backend crashes, subtly wrong output, or other Bad Things. You can always leave out an optimization clause if you are not sure about it; the only consequence is that queries might run slower than they need to.
Additional optimization clauses might be added in future versions of PostgreSQL. The ones described here are all the ones that release 7.2.8 understands.
The COMMUTATOR clause, if provided, names an operator that is the commutator of the operator being defined. We say that operator A is the commutator of operator B if (x A y) equals (y B x) for all possible input values x, y. Notice that B is also the commutator of A. For example, operators < and > for a particular data type are usually each others' commutators, and operator + is usually commutative with itself. But operator - is usually not commutative with anything.
The left operand type of a commuted operator is the same as the right operand type of its commutator, and vice versa. So the name of the commutator operator is all that PostgreSQL needs to be given to look up the commutator, and that's all that needs to be provided in the COMMUTATOR clause.
When you are defining a self-commutative operator, you just do it. When you are defining a pair of commutative operators, things are a little trickier: how can the first one to be defined refer to the other one, which you haven't defined yet? There are two solutions to this problem:
One way is to omit the COMMUTATOR clause in the first operator that you define, and then provide one in the second operator's definition. Since PostgreSQL knows that commutative operators come in pairs, when it sees the second definition it will automatically go back and fill in the missing COMMUTATOR clause in the first definition.
The other, more straightforward way is just to include COMMUTATOR clauses in both definitions. When PostgreSQL processes the first definition and realizes that COMMUTATOR refers to a non-existent operator, the system will make a dummy entry for that operator in the system catalog. This dummy entry will have valid data only for the operator name, left and right operand types, and result type, since that's all that PostgreSQL can deduce at this point. The first operator's catalog entry will link to this dummy entry. Later, when you define the second operator, the system updates the dummy entry with the additional information from the second definition. If you try to use the dummy operator before it's been filled in, you'll just get an error message. (Note: This procedure did not work reliably in PostgreSQL versions before 6.5, but it is now the recommended way to do things.)
The NEGATOR clause, if provided, names an operator that is the negator of the operator being defined. We say that operator A is the negator of operator B if both return Boolean results and (x A y) equals NOT (x B y) for all possible inputs x, y. Notice that B is also the negator of A. For example, < and >= are a negator pair for most data types. An operator can never validly be its own negator.
Unlike commutators, a pair of unary operators could validly be marked as each others' negators; that would mean (A x) equals NOT (B x) for all x, or the equivalent for right unary operators.
An operator's negator must have the same left and/or right operand types as the operator itself, so just as with COMMUTATOR, only the operator name need be given in the NEGATOR clause.
Providing a negator is very helpful to the query optimizer since it allows expressions like NOT (x = y) to be simplified into x <> y. This comes up more often than you might think, because NOTs can be inserted as a consequence of other rearrangements.
Pairs of negator operators can be defined using the same methods explained above for commutator pairs.
The RESTRICT clause, if provided, names a restriction selectivity estimation function for the operator (note that this is a function name, not an operator name). RESTRICT clauses only make sense for binary operators that return boolean. The idea behind a restriction selectivity estimator is to guess what fraction of the rows in a table will satisfy a WHERE-clause condition of the form
column OP constant
for the current operator and a particular constant value. This assists the optimizer by giving it some idea of how many rows will be eliminated by WHERE clauses that have this form. (What happens if the constant is on the left, you may be wondering? Well, that's one of the things that COMMUTATOR is for...)
Writing new restriction selectivity estimation functions is far beyond the scope of this chapter, but fortunately you can usually just use one of the system's standard estimators for many of your own operators. These are the standard restriction estimators:
You can frequently get away with using either
for operators that have very high or very low selectivity, even
if they aren't really equality or inequality. For example, the
approximate-equality geometric operators use
eqsel on the assumption that they'll usually
only match a small fraction of the entries in a table.
You can use
scalargtsel for comparisons on
data types that have some sensible means of being converted
into numeric scalars for range comparisons. If possible, add
the data type to those understood by the routine
convert_to_scalar() in src/backend/utils/adt/selfuncs.c. (Eventually,
this routine should be replaced by per-data-type functions
identified through a column of the
pg_type system catalog; but that hasn't
happened yet.) If you do not do this, things will still work,
but the optimizer's estimates won't be as good as they could
There are additional selectivity functions designed for
geometric operators in src/backend/utils/adt/geo_selfuncs.c:
contsel. At this writing these are just
stubs, but you may want to use them (or even better, improve
The JOIN clause, if provided, names a join selectivity estimation function for the operator (note that this is a function name, not an operator name). JOIN clauses only make sense for binary operators that return boolean. The idea behind a join selectivity estimator is to guess what fraction of the rows in a pair of tables will satisfy a WHERE-clause condition of the form
table1.column1 OP table2.column2
for the current operator. As with the RESTRICT clause, this helps the optimizer very substantially by letting it figure out which of several possible join sequences is likely to take the least work.
As before, this chapter will make no attempt to explain how to write a join selectivity estimator function, but will just suggest that you use one of the standard estimators if one is applicable:
The HASHES clause, if present, tells the system that it is OK to use the hash join method for a join based on this operator. HASHES only makes sense for binary operators that return boolean, and in practice the operator had better be equality for some data type.
The assumption underlying hash join is that the join operator can only return true for pairs of left and right values that hash to the same hash code. If two values get put in different hash buckets, the join will never compare them at all, implicitly assuming that the result of the join operator must be false. So it never makes sense to specify HASHES for operators that do not represent equality.
In fact, logical equality is not good enough either; the operator had better represent pure bitwise equality, because the hash function will be computed on the memory representation of the values regardless of what the bits mean. For example, equality of time intervals is not bitwise equality; the interval equality operator considers two time intervals equal if they have the same duration, whether or not their endpoints are identical. What this means is that a join using = between interval fields would yield different results if implemented as a hash join than if implemented another way, because a large fraction of the pairs that should match will hash to different values and will never be compared by the hash join. But if the optimizer chose to use a different kind of join, all the pairs that the equality operator says are equal will be found. We don't want that kind of inconsistency, so we don't mark interval equality as hashable.
There are also machine-dependent ways in which a hash join might fail to do the right thing. For example, if your data type is a structure in which there may be uninteresting pad bits, it's unsafe to mark the equality operator HASHES. (Unless, perhaps, you write your other operators to ensure that the unused bits are always zero.) Another example is that the floating-point data types are unsafe for hash joins. On machines that meet the IEEE floating-point standard, minus zero and plus zero are different values (different bit patterns) but they are defined to compare equal. So, if the equality operator on floating-point data types were marked HASHES, a minus zero and a plus zero would probably not be matched up by a hash join, but they would be matched up by any other join process.
The bottom line is that you should probably only use
HASHES for equality operators that are
(or could be) implemented by
The SORT clauses, if present, tell the system that it is permissible to use the merge join method for a join based on the current operator. Both must be specified if either is. The current operator must be equality for some pair of data types, and the SORT1 and SORT2 clauses name the ordering operator ("<" operator) for the left and right-side data types respectively.
Merge join is based on the idea of sorting the left and righthand tables into order and then scanning them in parallel. So, both data types must be capable of being fully ordered, and the join operator must be one that can only succeed for pairs of values that fall at the "same place" in the sort order. In practice this means that the join operator must behave like equality. But unlike hash join, where the left and right data types had better be the same (or at least bitwise equivalent), it is possible to merge-join two distinct data types so long as they are logically compatible. For example, the int2-versus-int4 equality operator is merge-joinable. We only need sorting operators that will bring both data types into a logically compatible sequence.
When specifying merge-sort operators, the current operator and both referenced operators must return boolean; the SORT1 operator must have both input data types equal to the current operator's left operand type, and the SORT2 operator must have both input data types equal to the current operator's right operand type. (As with COMMUTATOR and NEGATOR, this means that the operator name is sufficient to specify the operator, and the system is able to make dummy operator entries if you happen to define the equality operator before the other ones.)
In practice you should only write SORT clauses for an = operator, and the two referenced operators should always be named <. Trying to use merge join with operators named anything else will result in hopeless confusion, for reasons we'll see in a moment.
There are additional restrictions on operators that you mark merge-joinable. These restrictions are not currently checked by CREATE OPERATOR, but a merge join may fail at run time if any are not true:
The merge-joinable equality operator must have a commutator (itself if the two data types are the same, or a related equality operator if they are different).
There must be < and > ordering operators having the same left and right operand data types as the merge-joinable operator itself. These operators must be named < and >; you do not have any choice in the matter, since there is no provision for specifying them explicitly. Note that if the left and right data types are different, neither of these operators is the same as either SORT operator. But they had better order the data values compatibly with the SORT operators, or the merge join will fail to work.