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Posted to issues@hive.apache.org by "Pengcheng Xiong (JIRA)" <ji...@apache.org> on 2017/03/25 20:16:41 UTC
[jira] [Updated] (HIVE-15635) Hive/Druid integration: timeseries
query shows all days, even if no data
[ https://issues.apache.org/jira/browse/HIVE-15635?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Pengcheng Xiong updated HIVE-15635:
-----------------------------------
Target Version/s: 3.0.0 (was: 2.2.0)
> Hive/Druid integration: timeseries query shows all days, even if no data
> ------------------------------------------------------------------------
>
> Key: HIVE-15635
> URL: https://issues.apache.org/jira/browse/HIVE-15635
> Project: Hive
> Issue Type: Bug
> Components: Druid integration
> Affects Versions: 2.2.0
> Reporter: Jesus Camacho Rodriguez
> Assignee: Jesus Camacho Rodriguez
> Priority: Critical
>
> We should have consistent results on Druid vs Hive. However, following query is transformed into timeseries Druid query which yields different results in Druid, since it will show all values for the given time granularity, even if there is no data for the given _i\_brand\_id_.
> In Druid:
> {code:sql}
> SELECT floor_day(`__time`) as `granularity`, max(ss_quantity), sum(ss_wholesale_cost)
> FROM store_sales_sold_time_subset
> WHERE i_brand_id = 10001009
> GROUP BY floor_day(`__time`)
> ORDER BY `granularity`;
> OK
> 1999-11-01 00:00:00 45 37.47
> 1999-11-02 00:00:00 -9223372036854775808 0.0
> 1999-11-03 00:00:00 -9223372036854775808 0.0
> 1999-11-04 00:00:00 39 61.52
> 1999-11-05 00:00:00 74 145.84
> 1999-11-06 00:00:00 62 14.5
> 1999-11-07 00:00:00 -9223372036854775808 0.0
> 1999-11-08 00:00:00 5 34.08
> 1999-11-09 00:00:00 -9223372036854775808 0.0
> 1999-11-10 00:00:00 -9223372036854775808 0.0
> 1999-11-11 00:00:00 -9223372036854775808 0.0
> 1999-11-12 00:00:00 66 67.22
> 1999-11-13 00:00:00 -9223372036854775808 0.0
> 1999-11-14 00:00:00 -9223372036854775808 0.0
> 1999-11-15 00:00:00 -9223372036854775808 0.0
> 1999-11-16 00:00:00 60 96.37
> 1999-11-17 00:00:00 50 79.11
> 1999-11-18 00:00:00 -9223372036854775808 0.0
> 1999-11-19 00:00:00 -9223372036854775808 0.0
> 1999-11-20 00:00:00 -9223372036854775808 0.0
> 1999-11-21 00:00:00 -9223372036854775808 0.0
> 1999-11-22 00:00:00 -9223372036854775808 0.0
> 1999-11-23 00:00:00 57 17.69
> 1999-11-24 00:00:00 -9223372036854775808 0.0
> 1999-11-25 00:00:00 -9223372036854775808 0.0
> 1999-11-26 00:00:00 -9223372036854775808 0.0
> 1999-11-27 00:00:00 86 91.59
> 1999-11-28 00:00:00 -9223372036854775808 0.0
> 1999-11-29 00:00:00 93 136.48
> 1999-11-30 00:00:00 -9223372036854775808 0.0
> {code}
> In Hive:
> {code:sql}
> SELECT floor_day(`__time`) as `granularity`, max(ss_quantity), sum(ss_wholesale_cost)
> FROM store_sales_sold_time_subset_hive
> WHERE i_brand_id = 10001009
> GROUP BY floor_day(`__time`)
> ORDER BY `granularity`;
> OK
> 1999-11-01 00:00:00 45 37.47
> 1999-11-04 00:00:00 39 61.52
> 1999-11-05 00:00:00 74 145.84
> 1999-11-06 00:00:00 62 14.5
> 1999-11-08 00:00:00 5 34.08
> 1999-11-12 00:00:00 66 67.22
> 1999-11-16 00:00:00 60 96.36999999999999
> 1999-11-17 00:00:00 50 79.11
> 1999-11-23 00:00:00 57 17.689999999999998
> 1999-11-27 00:00:00 86 91.59
> 1999-11-29 00:00:00 93 136.48
> {code}
> Probably we should handle this in the _timeseries_ record reader.
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