Jesus Camacho Rodriguez created HIVE-15635: ----------------------------------------------
Summary: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332)