|From:||Jeff Davis <pgsql(at)j-davis(dot)com>|
|Subject:||Range Merge Join v1|
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Find different people using the same website at the same time:
create table session(sessionid text, username text, during tstzrange);
SELECT s1.username, s2.username, s1.during * s2.during
FROM session s1, session s2
WHERE s1.during && s2.during AND s1.username < s2.username
Brief summary of previous discussion:
- can indexes solve it already (parameterized paths, etc.)?
- planner complexity (track path keys, mergejoinable equality, etc.)
- spatial join algorithm choice
- compare range merge join against existing indexes to see if any
indexing approach is viable
No new syntax or other externals. Just improved execution strategy for
joining ranges using the overlaps operator (&&).
New syntax is possible, but I don't see a strong reason to support
special range join syntax at this time.
I took the path of least resistance, so if I am doing something wrong
please let me know. I hardwired it to look for the range overlaps
operator when checking if it could do a merge join, and then add
range_ops to restrictinfo->mergeopfamilies.
Then, I have to suppress adding it as an equivalence class, because
overlaps is not equality. It still adds single-member ECs to
restrictinfo->right_ec and left_ec, but (I think) that's OK.
Also, I have to prevent generating paths for right/full join, because
the range join algorithm can't (currently) handle those.
Needs more consideration. Seems to do reasonable things in the few
examples I tried.
See detailed comments in nodeMergejoin.c
Seems much better than index nest loop join when the join is not
selective. I will post detailed numbers soon.
If no index is available, range merge join is the only reasonable way
to execute a range join. For instance, if the inner is not a leaf in
It was suggested that I approach it as a general spatial-join
problem. That has merit, but I rejected it for now because the
algorithm that I think has the most promise is range-oriented
anyway. By that I mean we would need to extract all of the dimensions
into ranges first, and then perform the join. With that in mind, it
makes sense to implement range joins first; and then later provide the
APIs to get at the spatial dimensions so that we can implement other
spatial joins as range joins.
Another suggestion was to base it on hash join, but I never understood
that proposal and it didn't seem to map very well to a spatial join.
Yet another suggestion was to use some kind of temporary index. Some
brief experiments I did indicated that it would be fairly slow (though
more investigation might be useful here). Also, it doesn't provide any
alternative to the nestloop-with-inner-index we already offer at the
leaf level today.
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