[jira] [Resolved] (SPARK-5446) Parquet column pruning should work for Map and Struct

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-5446?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-5446.
------------------------------
    Resolution: Duplicate

> Parquet column pruning should work for Map and Struct
> -----------------------------------------------------
>
>                 Key: SPARK-5446
>                 URL: https://issues.apache.org/jira/browse/SPARK-5446
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.2.0, 1.3.0
>            Reporter: Jianshi Huang
>
> Consider the following query:
> {code:sql}
> select stddev_pop(variables.var1) stddev
> from model
> group by model_name
> {code}
> Where variables is a Struct containing many fields, similarly it can be a Map with many key-value pairs.
> During execution, SparkSQL will shuffle the whole map or struct column instead of extracting the value first. The performance is very poor.
> The optimized version could use a subquery:
> {code:sql}
> select stddev_pop(var) stddev
> from (select variables.var1 as var, model_name from model) m
> group by model_name
> {code}
> Where we extract the field/key-value only in the mapper side, so data being shuffled is small.
> A benchmark for a table with 600 variables shows drastic improvment in runtime:
> || || Parquet (using Map) || Parquet (using Struct) ||
> | Stddev (unoptimized) |	12890s |	583s |
> | Stddev (optimized)	  | 84s |	61s |
> Parquet already supports reading a single field/key-value in the storage level, but SparkSQL currently doesn’t have optimization for it. This will be very useful optimization for tables having Map or Struct with many columns. 
> Jianshi



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org