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https://issues.apache.org/jira/browse/SPARK-35817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17365762#comment-17365762
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Erik Krogen edited comment on SPARK-35817 at 6/18/21, 10:09 PM:
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Thanks for catching this [~bersprockets]! I will be happy to contribute as
well, given I'm the original author of SPARK-34133. Let me know if there's
anything I can do or provide input on. I guess we should create a map of one
sides' fields (with lowercased names for case-insensitivity) and do lookups, to
reduce the O(N^2) complexity to O(N) (N for map creation and N constant-time
lookups during resolution).
was (Author: xkrogen):
Thanks for catching this [~bersprockets]! I will be happy to contribute as
well, given I'm the original author of SPARK-34133. Let me know if there's
anything I can do or provide input on.
> Queries against wide Avro tables can be slow
> --------------------------------------------
>
> Key: SPARK-35817
> URL: https://issues.apache.org/jira/browse/SPARK-35817
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.2.0
> Reporter: Bruce Robbins
> Priority: Major
>
> A query against an Avro table can be quite slow when all are true:
> - There are many columns in the Avro file
> - The query contains a wide projection
> - There are many splits in the input
> - Some of the splits are read serially (e.g., less executors than there are
> tasks)
> A write to an Avro table can be quite slow when all are true:
> - There are many columns in the new rows
> - The operation is creating many files
> For example, a single-threaded query against a 6000 column Avro data set with
> 50K rows and 20 files takes less than a minute with Spark 3.0.1 but over 7
> minutes with Spark 3.2.0-SNAPSHOT.
> The culprit appears to be this line of code:
> https://github.com/apache/spark/blob/3fb044e043a2feab01d79b30c25b93d4fd166b12/external/avro/src/main/scala/org/apache/spark/sql/avro/AvroUtils.scala#L226
> For each split, AvroDeserializer will call this function once for each column
> in the projection, resulting in a potential n^2 lookup per split.
> For each file, AvroSerializer will call this function once for each column,
> resulting in an n^2 lookup per file.
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