Re: Direct HBase vs. Phoenix query performance

[Marcell Ortutay]

Thanks James! I've made a JIRA ticket here:
https://issues.apache.org/jira/projects/PHOENIX/issues/PHOENIX-4666

This is a priority for us at 23andMe as it substantially affects some of
our queries, so we'd be happy to provide a patch if Phoenix maintainers are
able to provide some guidance on the design. I've put a question in the
JIRA ticket as well regarding the approach to take.

On Thu, Mar 15, 2018 at 10:44 AM, James Taylor 
wrote:

> Hi Marcell,
> Yes, that's correct - the cache we build for the RHS is only kept around
> while the join query is being executed. It'd be interesting to explore
> keeping the cache around longer for cases like yours (and probably not too
> difficult). We'd need to keep a map that maps the RHS query to its hash
> join cache identifier and if found skip the running of the query. Would you
> mind filing a JIRA and we can explore further?
> Thanks,
> James
>
> On Wed, Mar 14, 2018 at 3:40 PM, Marcell Ortutay 
> wrote:
>
>> A quick update--I did some inspection of the Phoenix codebase, and it
>> looks like my understanding of the coprocessor cache was incorrect. I
>> thought it was meant to be used across queries, eg. that the RHS of the
>> join would be saved for subsequent queries. In fact this is not the case,
>> the coprocessor cache is meant to live only for the duration of the query.
>> This explains the performance difference--Phoenix is re-running a long
>> subquery for each join, whereas my direct to HBase script saves those
>> results across queries.
>>
>> On Tue, Mar 13, 2018 at 4:56 PM, Marcell Ortutay 
>> wrote:
>>
>>> 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 
>>> 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 
 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 

Re: phoenix + protobuf 3

[Josh Elser]

You'll like have to shade the use of Protobuf3 in your application.

this is not something "optional". HBase2 (and the sister release 
Phoenix5) will have done this shading internally which will make this 
easier for you downstream. However, these releases have not yet been made.


On 3/21/18 11:14 AM, Marcelo Valle wrote:
I am creating an application that uses phoenix jdbc driver (the fat 
one). I saw this driver has a dependency of google protobuffer 2 library.


I want to use grpc in my application, which uses proto buffer 3, which 
is incompatible with proto 2.


How much phoenix JDBC driver really depends on proto 2 library? Will it 
always be used or there is a setting where I can disable it?


--
Marcelo Valle
http://mvalle.com - @mvallebr

Re: tmp files

[Josh Elser]

I would assume that they would be cleaned up, but I don't have specifics 
to point you to off the top of my head.


On 3/21/18 4:32 AM, Flavio Pompermaier wrote:
Is there a way to cleanup them? Are they left there when client query 
are interrupted?


On Wed, Mar 21, 2018 at 3:00 AM, Josh Elser > wrote:


Are they ResultSpooler files?

If so, you want to set `phoenix.spool.directory` which defaults to
the java.io.tmpdir system property.


On 3/20/18 12:47 PM, Flavio Pompermaier wrote:

Hi to all,
I've just discovered that Phoenix continue to create .tmp files
in /tmp directory causing the disk to run out of space...is
there a way to change this directory and run a cleanup??

Best,
Flavio

Re: tmp files

[Flavio Pompermaier]

Is there a way to cleanup them? Are they left there when client query are
interrupted?

On Wed, Mar 21, 2018 at 3:00 AM, Josh Elser  wrote:

> Are they ResultSpooler files?
>
> If so, you want to set `phoenix.spool.directory` which defaults to the
> java.io.tmpdir system property.
>
>
> On 3/20/18 12:47 PM, Flavio Pompermaier wrote:
>
>> Hi to all,
>> I've just discovered that Phoenix continue to create .tmp files in /tmp
>> directory causing the disk to run out of space...is there a way to change
>> this directory and run a cleanup??
>>
>> Best,
>> Flavio
>>
>