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https://issues.apache.org/jira/browse/SPARK-39584?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17558678#comment-17558678
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Kazuyuki Tanimura commented on SPARK-39584:
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Hi [~maropu] , pinging you since it seems you are the expert in this area. I am
wondering if you have preference between the solution 1 vs 2?
CC [~dongjoon]
I will be adding benchmark results from the master for the future comparison
purpose
> Fix TPCDSQueryBenchmark Measuring Performance of Wrong Query Results
> --------------------------------------------------------------------
>
> Key: SPARK-39584
> URL: https://issues.apache.org/jira/browse/SPARK-39584
> Project: Spark
> Issue Type: Test
> Components: Tests
> Affects Versions: 3.0.3, 3.1.2, 3.2.1, 3.3.0, 3.4.0
> Reporter: Kazuyuki Tanimura
> Priority: Minor
>
> GenTPCDSData uses the schema defined in `TPCDSSchema` that contains
> varchar(N)/char(N). When GenTPCDSData generates parquet, that pads spaces for
> strings whose lengths are < N.
> When TPCDSQueryBenchmark reads data from parquet generated by GenTPCDSData,
> it uses schema from the parquet file and keeps the paddings. Due to the extra
> spaces, string filter queries of TPC-DS fail to match. For example, q13 query
> results are all nulls and returns too fast because string filter does not
> meet any rows.
> Therefore, TPCDSQueryBenchmark is benchmarking with wrong query results and
> that is inflating some performance results.
> I am exploring two possible solutions now
> 1. Call `{{{}CREATE TABLE tableName schema USING parquet LOCATION path`
> {}}}before reading. This is what Spark unit tests are doing
> 2. Change varchar to string in the schema. This is what [databricks data
> generator| [https://github.com/databricks/spark-sql-perf]] is doing
> TPCDSQueryBenchmark was ported from databricks/spark-sql-perf in
> https://issues.apache.org/jira/browse/SPARK-35192
> History related varchar
> https://lists.apache.org/thread/rg7pgwyto3616hb15q78n0sykls9j7rn
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