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https://issues.apache.org/jira/browse/SPARK-4502?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Liwen Sun updated SPARK-4502:
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Description:
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 needs
1 column. I also measured the bytes read within Parquet. In these two cases,
the same number of bytes (99365194 bytes) were read.
was:
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. I also measured the bytes read within Parquet. In these two cases, the
same number of bytes (99365194 bytes) were read.
> Spark SQL reads unneccesary fields 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
> needs 1 column. I also measured the bytes read within Parquet. In these two
> cases, the same number of bytes (99365194 bytes) were read.
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