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From Marcell Ortutay <mortu...@23andme.com>
Subject Re: Direct HBase vs. Phoenix query performance
Date Tue, 13 Mar 2018 23:56:42 GMT
Hi James,

Thanks for the tips. Our row keys are (I think) reasonably optimized. I've
made a gist which is an anonymized version of the query, and it indicates
which conditions are / are not part of the PK. It is here:
https://gist.github.com/ortutay23andme/12f03767db13343ee797c328a4d78c9c

I don't (yet) have an anonymized version of the raw HBase Go script
available, but after comparing the performance of the two, I've figured out
the root cause. The query does a subquery to produce the LHS of one of the
hash joins. This can be seen on L5 of the gist above. This subquery is
quite long (~1sec) to execute and scans a few million rows. It is shared
across all queries so in the raw HBase script I cached / re-used it for all
queries. This has a (very large) performance benefit, in particular under
high load.

My understanding of Phoenix is that it is supposed to do the same thing. It
seems like the hash join code has some mechanic for caching data for hash
joining using the HBase coprocessor system. I would expect this cache to
kick in, and only execute the large subquery once. Looking at the
performance of the query (30sec timeouts after ~2qps), this doesn't seem to
be happening.

I'm wondering if my understanding of the Phoenix join cache is right. Is it
correct to expect that it would cache the results of a subquery used in a
join? If so, what are possible reasons why it would *not* do so? Any
guidance on metrics / optimizations to look at would be appreciated.

Thanks,
Marcell

On Thu, Mar 8, 2018 at 2:59 PM, James Taylor <jamestaylor@apache.org> wrote:

> Hi Marcell,
> It'd be helpful to see the table DDL and the query too along with an idea
> of how many regions might be involved in the query. If a query is a
> commonly run query, usually you'll design the row key around optimizing it.
> If you have other, simpler queries that have determined your row key, then
> another alternative is to add one or more secondary indexes. Another common
> technique is to denormalize your data in ways that precompute the join to
> avoid having to do it at run time.
>
> With joins, make sure to order your tables from post filtered largest (on
> LHS) to smallest (on RHS). Also, if you're joining on the PK of both
> tables, you should use the USE_SORT_MERGE_JOIN hint. Another common tuning
> exercise is around determining the best parallelization to use (i.e.
> guidepost width) or even disabling parallelization for more than an entire
> region's worth of data.
>
> It'd also be interesting to see the raw HBase code for a query of this
> complexity.
>
> Thanks,
> James
>
> On Thu, Mar 8, 2018 at 1:03 PM, Marcell Ortutay <mortutay@23andme.com>
> wrote:
>
>> Hi,
>>
>> I am using Phoenix at my company for a large query that is meant to be
>> run in real time as part of our application. The query involves several
>> aggregations, anti-joins, and an inner query. Here is the (anonymized)
>> query plan: https://gist.github.com/ortutay23andme/1da620472cc469e
>> d2d8a6fdd0cc7eb01
>>
>> The query performance on this is not great, it takes about 5sec to
>> execute the query, and moreover it performs badly under load. If we run
>> ~4qps of this query Phoenix starts to timeout and slow down a lot (queries
>> take >30sec).
>>
>> For comparison, I wrote a simple Go script that runs a similar query
>> talking directly to HBase. The performance on it is substantially better.
>> It executes in ~1.5sec, and can handle loads of ~50-100qps on the same
>> cluster.
>>
>> I'm wondering if anyone has ideas on what might be causing this
>> difference in performance? Are there configs / optimizations we can do in
>> Phoenix to bring the performance closer to direct HBase queries?
>>
>> I can provide context on the table sizes etc. if needed.
>>
>> Thanks,
>> Marcell
>>
>>
>

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