On 7/31/14, 12:28 PM, Firas Abuzaid wrote:
We're running various "triangle" join queries on Hive 0.9.0, and we're
wondering if we can get any better performance. Here's the query we're
running:
SELECT count(*)
FROM table r1 JOIN table r2 ON (r1.dst = r2.src) JOIN table r3 ON (r2.dst =
r3.src AND r3.dst = r1.src)
WHERE r1.src < r2.src AND r2.src < r3.src;
This is an interesting use-case for a JOIN clause.
The main overhead is that the JOIN criteria is materializing JOINs onto
HDFS.
Neither hive indexes, nor ORC indexes will help you here, unfortunately.
With the latest hive-13, I tried rewriting this to get a performance
boost with a CTE (which will be streamed through in tez‚ but MR will
still write it as SequenceFiles to HDFS).
Roughly, you need to remove the implicit filter of r1.src < r2.src &&
r2.src < r3.src into an independent sub query.
explain
with r2_tmp as (
select r2.src as r2_src, r3.dst as r3_dst from r2 join r3 on (r2.dst =
r3.src) where r2.src < r3.src
)
select count(1) from r1 JOIN r2_tmp on (r1.dst = r2_src and r1.src = r3_dst)
where (r1.src < r2_src)
Vectorization+ORC will massively reduce the GC overhead for this as well
- but for all these you need a modern hive version.
Data organization can give a bigger performance boost as well, because
of how ORC will do run-length packing of the same valued columns. I'd
just sort/bucket on src for all of them.
You can see a similar organization for easier big table JOINs here -
https://github.com/t3rmin4t0r/nyc-taxi-bigdata/blob/master/ddl/orc.sql#L26
HTH.
Cheers,
Gopal
