phoenix-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From James Taylor <jamestay...@apache.org>
Subject Re: high cardinality aggregation query performance
Date Fri, 27 Feb 2015 01:55:16 GMT
Gary,
I'm not able to repro the issue - I filed PHOENIX-1690 to track it and
attached my test case there. It looks related to the particular state
the table is in wrt its region boundaries and current statistics, so
I'll need the following additional information to try to help me repro
this:

1) What are the current region boundaries of your table? You can get
this programmatically through code like this:

        Connection conn = DriverManager.getConnection(getUrl(), props);
        List<HRegionLocation> splits =
conn.unwrap(PhoenixConnection.class).getQueryServices().getAllTableRegions(Bytes.toBytes("PERF.BIG_OLAP_DOC"));
        for (HRegionLocation split : splits) {
            System.out.println(Bytes.toStringBinary(split.getRegionInfo().getEndKey()));
        }

2) What are the current stats for the table. You can get this by
programmatically through code like this:

        PTable table =
conn.unwrap(PhoenixConnection.class).getMetaDataCache().getTable(new
PTableKey(null, "PERF.BIG_OLAP_DOC"));
        for (GuidePostsInfo info :
table.getTableStats().getGuidePosts().values()) {
            for (byte[] gp : info.getGuidePosts()) {
                System.out.println(Bytes.toStringBinary(gp));
            }
        }

3) If you can try after removing all rows from the SYSTEM.STATS table
and let me know if the problem still occurs, that'd be helpful too.
You can just do the following from sqlline: DELETE FROM SYSTEM.STATS
and then exit sqlline, start it again, and rerun the original query.

Thanks,
James

On Thu, Feb 26, 2015 at 10:52 AM, James Taylor <jamestaylor@apache.org> wrote:
> Gary,
> One possible workaround. Can you try adding the SKIP_SCAN hint to your
> query (instead of the AND device_type in
> ('MOBILE','DESKTOP','OTHER','TABLET')), like this?
>
> SELECT /*+ SKIP_SCAN */ count(1) cnt,
> ...
>
> Thanks,
> James
>
> On Wed, Feb 25, 2015 at 10:16 AM, James Taylor <jamestaylor@apache.org> wrote:
>> Sounds like a bug. I'll try to repro on my end. Thanks for the details, Gary.
>>
>>     James
>>
>> On Tue, Feb 24, 2015 at 1:49 PM, Gary Schulte
>> <gschulte@marinsoftware.com> wrote:
>>> On Tue, Feb 24, 2015 at 12:29 AM, James Taylor <jamestaylor@apache.org>
>>> wrote:
>>>>
>>>> Based on your query plan, the skip scan is being done solely based on your
>>>> salt bucket while the rest of the filtering is being done by a filter, which
>>>> means that you're not filtering based on the leading part of your primary
>>>> key. We'll know more once you post your schema, but if NETWORK, KEYWORD_ID
>>>> and CUSTOMER_ID formed your primary key constraint, then the skip scan would
>>>> work well.
>>>>
>>>
>>> Thanks for your response James.  Sorry for the slow reply - I had difficulty
>>> finding the exact set of test queries I was using for timings.
>>>
>>> The relevant portion of the olap doc schema is:
>>>
>>> create table PERF.BIG_OLAP_DOC (
>>> client_id                           integer not null
>>> ,customer_id                        integer
>>> ,time_id                            integer not null
>>> ,conversion_type_id                 integer not null
>>> ,device_type                        varchar(16)
>>> ,keyword_id                         bigint not null
>>> ,creative_id                        bigint not null
>>> ,placement_id                       bigint not null
>>> ,product_target_id                  bigint not null
>>> ,network                            varchar(7)
>>> ,impressions                        decimal(18, 4)
>>> ,publisher_clicks                   decimal(18, 4)
>>> ,publisher_cost                     decimal(18, 4)
>>> ,conversions                        decimal(18, 4)
>>> ,revenue                            decimal(18, 4)
>>>
>>> [ ...additional metric and dimensional colums ... ]
>>>
>>>     constraint perf_fact_pk primary key (client_id, time_id,
>>> conversion_type_id, device_type, keyword_id, creative_id, placement_id,
>>> product_target_id))SALT_BUCKETS=10;
>>>
>>>
>>> I am evaluating a 'stitch' case where results from an external system are
>>> injected either via table or (as in this case) an in-list.  An example of
>>> one of these test agg queries I am using is:
>>>
>>> SELECT count(1) cnt,
>>>        coalesce(SUM(impressions), 0.0) AS "impressions",
>>>        coalesce(SUM(publisher_clicks), 0.0) AS "pub_clicks",
>>>        coalesce(SUM(publisher_cost), 0.0) AS "pub_cost",
>>>        coalesce(SUM(conversions), 0.0) AS "conversions",
>>>        coalesce(SUM(revenue), 0.0) AS "revenue"
>>>   FROM perf.big_olap_doc
>>>  WHERE time_id between 3000 and 3700
>>>    AND network in ('SEARCH')
>>>    AND conversion_type_id = 1
>>>    AND client_id = 10724
>>> --   AND device_type in ('MOBILE','DESKTOP','OTHER','TABLET')
>>>    AND keyword_id in (
>>> 613214369, 613217307, 613247509, 613248897, 613250382, 613250387, 613252322,
>>> 613260252, 613261753, 613261754, 613261759,
>>> 613261770, 613261873, 613261884, 613261885, 613261888, 613261889, 613261892,
>>> 613261897, 613261913, 613261919, 613261927,
>>> 614496021, 843606367, 843606967, 843607021, 843607033, 843607089,
>>> 1038731600, 1038731672, 1038731673, 1038731675,
>>> 1038731684, 1038731693, 1046990487, 1046990488, 1046990499, 1046990505,
>>> 1046990506, 1049724722, 1051109548, 1051311275,
>>> 1051311904, 1060574377, 1060574395, 1060574506, 1060574562, 1115915938,
>>> 1115915939, 1115915941, 1116310571, 1367495544,
>>> 1367495545, 1367497297, 1367497298, 1367497299, 1367497300, 1367497303,
>>> 1367497313, 1367497813, 1367497816, 1367497818,
>>> 1367497821, 1367497822, 1367497823, 1624976423, 1624976451, 1624976457,
>>> 3275636061, 3275640505, 3275645765, 3275645807,
>>> 3275649138, 3275651456, 3275651460, 3275651478, 3275651479, 3275654566,
>>> 3275654568, 3275654570, 3275654575, 3275659612,
>>> 3275659616, 3275659620, 3275668880, 3275669693, 3275675627, 3275675634,
>>> 3275677479, 3275677504, 3275678855, 3275679524,
>>> 3275679532, 3275680014, 3275682307, 3275682308, 3275682309, 3275682310,
>>> 3275682420, 3275682423, 3275682436, 3275682448,
>>> 3275682460, 3275682462, 3275682474, 3275684831, 3275688903, 3275694023,
>>> 3275694025, 3275694027, 3275695054, 3275695056,
>>> 3275695062, 3275699512, 3275699514, 3275699518, 3275701682, 3275701683,
>>> 3275701685, 3275701688, 3275703633, 3275703634,
>>> 3275703635, 3275703636, 3275703638, 3275703639, 3275704860, 3275704861,
>>> 3275764577, 3275797149, 3275798566, 3275798567,
>>> 3275798568, 3275798592, 3275931147, 3275942728, 3275945337, 3275945338,
>>> 3275945339, 3275945340, 3275945342, 3275945344,
>>> 3275946319, 3275946322, 3275946324, 3275946643, 3275949495, 3275949498,
>>> 3275949500, 3275950250, 3275955128, 3275955129,
>>> 3275955130, 3427017435, 3427017450, 3438304254, 3438304257, 3447068169,
>>> 3505227849, 3505227890, 3505556908, 3506351285,
>>> 3506351389, 3506351398, 3506351468, 3510037138, 3510038610, 3545590644,
>>> 3545594378, 3545595073, 3545595318, 3545595506,
>>> 3545597841, 3545598818, 3545599658, 3545599663, 3545601215, 3556080898,
>>> 3556080980, 3556080999, 3556081323, 3565122663,
>>> 3565122679, 3565122801, 3565122858, 3565122908, 3565122929, 3565122952,
>>> 3565122984, 3565123028, 3565123047, 3565123048,
>>> 3565123203, 3565123230, 3949988054, 3949988056, 3949988070, 3972992248,
>>> 3972992252, 3972992254, 3972992257, 3972992263,
>>> 3972992267, 3972992268, 3972992269, 3972992270, 3972992274, 3972992275,
>>> 3972992277, 3972992281, 3972992293, 3972992298,
>>> 3972992299, 3972992305, 3972992307, 3972992313, 3972992316, 3972992322,
>>> 3972992338, 3978471261, 3978471272, 4266318185,
>>> 4298107404, 4308853119, 4308853123, 4308853500, 4451174646, 4451174656,
>>> 4451174701, 4569827278, 4569827284, 4569827287,
>>> 4569827379, 4569827523, 4569827524, 4896589676, 4979049725, 5054587609,
>>> 5136433884, 5362640372, 5393109964, 5393405364,
>>> 5393405365, 5393405620, 5393405625, 5393405675, 5393405677, 5393405858,
>>> 5393405970)
>>>
>>>
>>> Reading your interpretation of the skip scan, I see that the plan is
>>> indicating it is only using the salt and the first three columns of the
>>> index, client_id, and time_id and conversion_type.  I hadn't considered the
>>> salt - that bit of detail in the plan makes more sense to me now.  It looks
>>> now like the lackluster performance for higher cardinality aggregations is
>>> related to scanning a much larger portion of the key space.  For
>>> aggregations where I am not relying on filtering, I am seeing much better
>>> performance.
>>>
>>> So to tune this particular stitch case / skip scan, it looks like I need to
>>> get the 4th index column into the criteria.  There are only four distinct
>>> values in the fourth index column (these can/should probably be something
>>> other than varchar, but this is what I have loaded currently).  In order to
>>> use the keyword_id portion of the index I tried explicitly specifying all
>>> device_types via in-list (the commented portion of the query above), but I
>>> get a peculiar error:
>>>
>>> java.lang.IndexOutOfBoundsException: end index (1) must not be less than
>>> start index (2)
>>> at
>>> com.google.common.base.Preconditions.checkPositionIndexes(Preconditions.java:388)
>>> at com.google.common.collect.ImmutableList.subList(ImmutableList.java:362)
>>> at com.google.common.collect.ImmutableList.subList(ImmutableList.java:62)
>>> at
>>> org.apache.phoenix.filter.SkipScanFilter.intersect(SkipScanFilter.java:291)
>>> at
>>> org.apache.phoenix.filter.SkipScanFilter.intersect(SkipScanFilter.java:177)
>>> at org.apache.phoenix.compile.ScanRanges.intersectScan(ScanRanges.java:316)
>>> at
>>> org.apache.phoenix.iterate.BaseResultIterators.getParallelScans(BaseResultIterators.java:464)
>>> at
>>> org.apache.phoenix.iterate.BaseResultIterators.getParallelScans(BaseResultIterators.java:394)
>>> at
>>> org.apache.phoenix.iterate.BaseResultIterators.<init>(BaseResultIterators.java:184)
>>> at
>>> org.apache.phoenix.iterate.ParallelIterators.<init>(ParallelIterators.java:54)
>>> at
>>> org.apache.phoenix.execute.AggregatePlan.newIterator(AggregatePlan.java:173)
>>> at org.apache.phoenix.execute.BaseQueryPlan.iterator(BaseQueryPlan.java:227)
>>> at org.apache.phoenix.execute.BaseQueryPlan.iterator(BaseQueryPlan.java:154)
>>> at
>>> org.apache.phoenix.jdbc.PhoenixStatement$1.call(PhoenixStatement.java:226)
>>> at
>>> org.apache.phoenix.jdbc.PhoenixStatement$1.call(PhoenixStatement.java:217)
>>> at org.apache.phoenix.call.CallRunner.run(CallRunner.java:53)
>>> at
>>> org.apache.phoenix.jdbc.PhoenixStatement.executeQuery(PhoenixStatement.java:216)
>>> at
>>> org.apache.phoenix.jdbc.PhoenixStatement.execute(PhoenixStatement.java:1057)
>>> at sqlline.SqlLine$Commands.execute(SqlLine.java:3673)
>>> at sqlline.SqlLine$Commands.sql(SqlLine.java:3584)
>>> at sqlline.SqlLine.dispatch(SqlLine.java:821)
>>> at sqlline.SqlLine.begin(SqlLine.java:699)
>>> at sqlline.SqlLine.mainWithInputRedirection(SqlLine.java:441)
>>> at sqlline.SqlLine.main(SqlLine.java:424)
>>>
>>>
>>> I thought perhaps I was hitting an upper limit on the number of elements in
>>> an in-list for a skip scan, and so tried removing the 250 element keyword
>>> in-list entirely and leaving only the device_type in-list, but I still get
>>> the same error.  It happens immediately, even for an explain, so I presume
>>> this is a query parsing problem.  Is there a bug or limitation of skip scans
>>> and/or sub lists involving varchar?
>>>
>>> Thx
>>>
>>>
>>>

Mime
View raw message