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From Manjunath Shetty H <manjunathshe...@live.com>
Subject Optimising multiple hive table join and query in spark
Date Sun, 15 Mar 2020 05:03:55 GMT
Hi All,

We have 10 tables in data warehouse (hdfs/hive) written using ORC format. We are serving a
usecase on top of that by joining 4-5 tables using Hive as of now. But it is not fast as we
wanted it to be, so we are thinking of using spark for this use case.

Any suggestion on this ? Is it good idea to use the Spark for this use case ? Can we get better
performance by using spark ?

Any pointers would be helpful.

Notes:

  *   Data is partitioned by date (yyyyMMdd) as integer.
  *   Query will fetch data for last 7 days from some tables while joining with other tables.

Approach we thought of as now :

  *   Create dataframe for each table and partition by same column for all tables ( Lets say
Country as partition column )
  *   Register all tables as temporary tables
  *   Run the sql query with joins

But the problem we are seeing with this approach is , even though we already partitioned using
country it still does hashParittioning + shuffle during join. All the table join contain `Country`
column with some extra column based on the table.

Is there any way to avoid these shuffles ? and improve performance ?


Thanks and regards
Manjunath

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