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https://issues.apache.org/jira/browse/SPARK-24906?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jason Guo updated SPARK-24906:
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Attachment: image-2018-07-24-20-29-24-797.png
> Enlarge split size for columnar file to ensure the task read enough data
> ------------------------------------------------------------------------
>
> Key: SPARK-24906
> URL: https://issues.apache.org/jira/browse/SPARK-24906
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.1
> Reporter: Jason Guo
> Priority: Critical
> Attachments: image-2018-07-24-20-26-32-441.png,
> image-2018-07-24-20-28-06-269.png, image-2018-07-24-20-29-24-797.png
>
>
> For columnar file, such as, when spark sql read the table, each split will be
> 128 MB by default since spark.sql.files.maxPartitionBytes is default to
> 128MB. Even when user set it to a large value, such as 512MB, the task may
> read only few MB or even hundreds of KB. Because the table (Parquet) may
> consists of dozens of columns while the SQL only need few columns.
>
> In this case, spark DataSourceScanExec can enlarge maxPartitionBytes
> adaptively.
> For example, there is 40 columns , 20 are integer while another 20 are long.
> When use query on an integer type column and an long type column, the
> maxPartitionBytes should be 20 times larger. (20*4+20*8) / (4+8) = 20.
>
> With this optimization, the number of task will be smaller and the job will
> run faster. More importantly, for a very large cluster (more the 10 thousand
> nodes), it will relieve RM's schedule pressure.
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