Sean Zhong created SPARK-16907:
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Summary: 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
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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