Re: [Bigdata-developers] Performance impact of Named Subqueries

[Bryan T. <br...@bl...>]

Named subqueries are run first in a bottom up evaluation style. Normal
subqueries are evaluated as-bound using left to right evaluation. So the
plans are quite different. There are now non blocking subquery hash joins
that are used if a limit is specified. So they can be turned on using a
very large limit. We plan to have a query hint for that soon...
On Jun 17, 2016 5:40 PM, "Edgar Rodriguez-Diaz" <ed...@sy...> wrote:

Hi,

If I understand correctly from the wiki docs, named subqueries should help
by reusing results computed, but I’m noticing some very significant
performance difference between a query using named subqueries and the same
query but inlining the named subqueries, both queries return the same
results but the latter performs at least an order of magnitude better than
the former. I’ve seen this in several queries now:

              SPARQLBenchmark
        ---------------

                      name | result | rank | runs |  mean |     sd
        -------------------|--------|------|------|-------|--------
                    query1 |   PASS |    1 |    3 | 3.497 |  1.195
                    query2 |   PASS |    2 |    3 | 16.03 |  1.448
                    query3 |   PASS |    3 |    3 |  21.6 |  1.386
              named query2 |   PASS |    4 |    3 | 386.9 |  3.411
              named query3 |   PASS |    5 |    3 | 397.5 |   6.31
              named query1 |   PASS |    6 |    3 | 827.3 | 0.7966

        Each of the above 18 runs were run in random, non-consecutive
order. Mean times in seconds.

So I guess the questions are:
What’s known the performance impact and if what I’m experiencing is a known
behavior?
Is it that named subqueries are blocking and when inlining the subqueries
the results can be streamed?

Cheers,
Edgar


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