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  1. Spark
  2. SPARK-4502

Spark SQL reads unneccesary nested fields from Parquet

    Details

    • Type: Improvement
    • Status: Resolved
    • Priority: Critical
    • Resolution: Fixed
    • Affects Version/s: 1.1.0
    • Fix Version/s: 2.4.0
    • Component/s: SQL
    • Labels:
      None
    • Target Version/s:

      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.

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              • Assignee:
                michael Michael Allman
                Reporter:
                liwen Liwen Sun
              • Votes:
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                • Created:
                  Updated:
                  Resolved: