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https://issues.apache.org/jira/browse/SPARK-16907?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan resolved SPARK-16907.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
2.0.1
> Parquet table reading performance regression when vectorized record reader is
> not used
> --------------------------------------------------------------------------------------
>
> Key: SPARK-16907
> URL: https://issues.apache.org/jira/browse/SPARK-16907
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: Sean Zhong
> Assignee: Sean Zhong
> Fix For: 2.0.1, 2.1.0
>
>
> For this parquet reading benchmark, Spark 2.0 is 20%-30% slower than Spark
> 1.6.
> {code}
> // Test Env: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz, Intel SSD SC2KW24
> // Generates parquet table with nested columns
> spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")
> def time[R](block: => R): Long = {
> val t0 = System.nanoTime()
> val result = block // call-by-name
> val t1 = System.nanoTime()
> println("Elapsed time: " + (t1 - t0)/1000000 + "ms")
> (t1 - t0)/1000000
> }
> val x = ((0 until 20).toList.map(x =>
> time(spark.read.parquet("/tmp/data4").filter($"nc.id" <
> 100).collect()))).sum/20
> {code}
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