On Fri, Sep 22, 2017 at 10:45 AM, Rafia Sabih
<rafia.sabih@enterprisedb.com> wrote:
>>
>> On completing the benchmark for all queries for the above mentioned
>> setup, following performance improvement can be seen,
>> Query | Patch | Head
>> 3 | 1455 | 1631
>> 4 | 499 | 4344
>> 5 | 1464 | 1606
>> 10 | 1475 | 1599
>> 12 | 1465 | 1790
>>
>> Note that all values of execution time are in seconds.
>
> I compared this experiment with non-partitioned database and following
> is the result,
> Query | Non-partitioned head
> 3 | 1752
> 4 | 315
> 5 | 2319
> 10 | 1535
> 12 | 1739
>
> In summary, the query that appears slowest in partitioned database is
> not so otherwise. It is good to see that in Q4 partition-wise join
> helps in achieving performance closer to it's non-partitioned case,
> otherwise partitioning alone causes it to suffer greatly. Apart from
> Q4 it does not looks like partitioning hurts anywhere else, though the
> maximum improvement is ~35% for Q5.
> Another point to note here is that the performance on partitioned and
> unpartitioned heads are quite close (except Q4) which is something
> atleast I wasn't expecting. It looks like we need not to partition the
> tables anyway, or atleast this set of queries doesn't benefit from
> partitioning. Please let me know if somebody has better ideas on how
> partitioning schemes should be applied to make it more beneficial for
> these queries.
Just partitioning is not expected to improve query performance (but we
still see some performance improvement). Partitioning + partition-wise
operations, pruning is expected to show performance gains. IIUC the
results you reported, Q3 takes 1752 seconds with non-partitioned head,
with partitioning it completes in 1631 seconds and with partition-wise
join it completes in 1455, so net improvement because of partitioning
is 300 seconds is almost 16% improvement, which is a lot for very
large data. So, except Q4, every query improves when the tables are
partitioned. Am I interpreting the results correctly?
There may be some other way of partitioning, which may give better
results, but I think what we have now shows the importance of
partitioning in case of very large data e.g. scale 300 TPCH.
--
Best Wishes,
Ashutosh Bapat
EnterpriseDB Corporation
The Postgres Database Company
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