[jira] [Updated] (SPARK-4502) Spark SQL unnecessarily reads the entire nested column from Parquet

["Liwen Sun (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4502?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Liwen Sun updated SPARK-4502:
-----------------------------
    Component/s: SQL

> Spark SQL unnecessarily reads the entire nested column from Parquet
> -------------------------------------------------------------------
>
>                 Key: SPARK-4502
>                 URL: https://issues.apache.org/jira/browse/SPARK-4502
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.1.0
>            Reporter: Liwen Sun
>
> When reading a field of a nested column from Parquet, SparkSQL reads and assemble all the fields of that nested column. This is unnecessary, as Parquet supports fine-grained field reads out of a nested column. This may degrades the performance significantly when a nested column has many fields. 
> For example, I loaded json tweets data into SparkSQL and ran the following query:
> {{SELECT User.contributors_enabled from Tweets;}}
> User is a nested structure that has 38 primitive fields (for Tweets schema, see: https://dev.twitter.com/overview/api/tweets), here is the log message:
> {{14/11/19 16:36:49 INFO InternalParquetRecordReader: Assembled and processed 385779 records from 38 columns in 3976 ms: 97.02691 rec/ms, 3687.0227 cell/ms}}
> For comparison, I also ran:
> {{SELECT User FROM Tweets;}}
> And here is the log message:
> {{14/11/19 16:45:40 INFO InternalParquetRecordReader: Assembled and processed 385779 records from 38 columns in 9461 ms: 40.77571 rec/ms, 1549.477 cell/ms}}
> So both queries load 38 columns from Parquet, while the first query only need 1 column. 



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