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

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

Liwen Sun created SPARK-4502:
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             Summary: 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
    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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