[SPARK-15881] Update microbenchmark results for WideSchemaBenchmark

## What changes were proposed in this pull request?

These were not updated after performance improvements. To make updating them easier, I also moved the results from inline comments out into a file, which is auto-generated when the benchmark is re-run.

Author: Eric Liang <ekl@databricks.com>

Closes #13607 from ericl/sc-3538.

4 files changed, 123 insertions, 234 deletions

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 304288a32c..2f7da31e55 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -833,7 +833,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dspark.ui.showConsoleProgress=false",
     javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
-    javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
+    javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=false",
     javaOptions in Test += "-Dderby.system.durability=test",
     javaOptions in Test ++= System.getProperties.asScala.filter(_._1.startsWith("spark"))
       .map { case (k,v) => s"-D$k=$v" }.toSeq,
diff --git a/sql/core/benchmarks/WideSchemaBenchmark-results.txt b/sql/core/benchmarks/WideSchemaBenchmark-results.txt
new file mode 100644
index 0000000000..ea6a6616c2
--- /dev/null
+++ b/sql/core/benchmarks/WideSchemaBenchmark-results.txt
@@ -0,0 +1,93 @@
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+parsing large select:                    Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 select expressions                             3 /    5          0.0     2967064.0       1.0X
+100 select expressions                          11 /   12          0.0    11369518.0       0.3X
+2500 select expressions                        243 /  250          0.0   242561004.0       0.0X
+
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+many column field r/w:                   Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 cols x 100000 rows (read in-mem)              28 /   40          3.6         278.8       1.0X
+1 cols x 100000 rows (exec in-mem)              28 /   42          3.5         284.0       1.0X
+1 cols x 100000 rows (read parquet)             23 /   35          4.4         228.8       1.2X
+1 cols x 100000 rows (write parquet)           163 /  182          0.6        1633.0       0.2X
+100 cols x 1000 rows (read in-mem)              27 /   39          3.7         266.9       1.0X
+100 cols x 1000 rows (exec in-mem)              48 /   79          2.1         481.7       0.6X
+100 cols x 1000 rows (read parquet)             25 /   36          3.9         254.3       1.1X
+100 cols x 1000 rows (write parquet)           182 /  196          0.5        1819.5       0.2X
+2500 cols x 40 rows (read in-mem)              280 /  315          0.4        2797.1       0.1X
+2500 cols x 40 rows (exec in-mem)              606 /  638          0.2        6064.3       0.0X
+2500 cols x 40 rows (read parquet)             836 /  843          0.1        8356.4       0.0X
+2500 cols x 40 rows (write parquet)            490 /  522          0.2        4900.6       0.1X
+
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+wide shallowly nested struct field r/w:  Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 wide x 100000 rows (read in-mem)              22 /   35          4.6         216.0       1.0X
+1 wide x 100000 rows (exec in-mem)              40 /   63          2.5         400.6       0.5X
+1 wide x 100000 rows (read parquet)             93 /  134          1.1         933.9       0.2X
+1 wide x 100000 rows (write parquet)           133 /  174          0.7        1334.3       0.2X
+100 wide x 1000 rows (read in-mem)              22 /   44          4.5         223.3       1.0X
+100 wide x 1000 rows (exec in-mem)              88 /  138          1.1         878.6       0.2X
+100 wide x 1000 rows (read parquet)            117 /  186          0.9        1172.0       0.2X
+100 wide x 1000 rows (write parquet)           144 /  174          0.7        1441.6       0.1X
+2500 wide x 40 rows (read in-mem)               36 /   57          2.8         358.9       0.6X
+2500 wide x 40 rows (exec in-mem)             1466 / 1507          0.1       14656.6       0.0X
+2500 wide x 40 rows (read parquet)             690 /  802          0.1        6898.2       0.0X
+2500 wide x 40 rows (write parquet)            197 /  207          0.5        1970.9       0.1X
+
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+deeply nested struct field r/w:          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 deep x 100000 rows (read in-mem)              22 /   35          4.5         223.9       1.0X
+1 deep x 100000 rows (exec in-mem)              28 /   52          3.6         280.6       0.8X
+1 deep x 100000 rows (read parquet)             41 /   65          2.4         410.5       0.5X
+1 deep x 100000 rows (write parquet)           163 /  173          0.6        1634.5       0.1X
+100 deep x 1000 rows (read in-mem)              43 /   63          2.3         425.9       0.5X
+100 deep x 1000 rows (exec in-mem)             232 /  280          0.4        2321.7       0.1X
+100 deep x 1000 rows (read parquet)           1989 / 2281          0.1       19886.6       0.0X
+100 deep x 1000 rows (write parquet)           144 /  184          0.7        1442.6       0.2X
+250 deep x 400 rows (read in-mem)               68 /   95          1.5         680.9       0.3X
+250 deep x 400 rows (exec in-mem)             1310 / 1403          0.1       13096.4       0.0X
+250 deep x 400 rows (read parquet)          41477 / 41847          0.0      414766.8       0.0X
+250 deep x 400 rows (write parquet)            243 /  272          0.4        2433.1       0.1X
+
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+bushy struct field r/w:                  Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 x 1 deep x 100000 rows (read in-mem)          23 /   36          4.4         229.8       1.0X
+1 x 1 deep x 100000 rows (exec in-mem)          27 /   48          3.7         269.6       0.9X
+1 x 1 deep x 100000 rows (read parquet)         25 /   33          4.0         247.5       0.9X
+1 x 1 deep x 100000 rows (write parquet)        82 /  134          1.2         821.1       0.3X
+128 x 8 deep x 1000 rows (read in-mem)          19 /   29          5.3         189.5       1.2X
+128 x 8 deep x 1000 rows (exec in-mem)         144 /  165          0.7        1440.4       0.2X
+128 x 8 deep x 1000 rows (read parquet)        117 /  159          0.9        1174.4       0.2X
+128 x 8 deep x 1000 rows (write parquet)       135 /  162          0.7        1349.0       0.2X
+1024 x 11 deep x 100 rows (read in-mem)         30 /   49          3.3         304.4       0.8X
+1024 x 11 deep x 100 rows (exec in-mem)       1146 / 1183          0.1       11457.6       0.0X
+1024 x 11 deep x 100 rows (read parquet)       712 /  758          0.1        7119.5       0.0X
+1024 x 11 deep x 100 rows (write parquet)       104 /  143          1.0        1037.3       0.2X
+
+OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
+Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
+wide array field r/w:                    Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+1 wide x 100000 rows (read in-mem)              18 /   31          5.6         179.3       1.0X
+1 wide x 100000 rows (exec in-mem)              31 /   47          3.2         310.2       0.6X
+1 wide x 100000 rows (read parquet)             45 /   73          2.2         445.1       0.4X
+1 wide x 100000 rows (write parquet)           109 /  140          0.9        1085.9       0.2X
+100 wide x 1000 rows (read in-mem)              17 /   25          5.8         172.7       1.0X
+100 wide x 1000 rows (exec in-mem)              18 /   22          5.4         184.6       1.0X
+100 wide x 1000 rows (read parquet)             26 /   42          3.8         261.8       0.7X
+100 wide x 1000 rows (write parquet)           150 /  164          0.7        1499.4       0.1X
+2500 wide x 40 rows (read in-mem)               19 /   31          5.1         194.7       0.9X
+2500 wide x 40 rows (exec in-mem)               19 /   24          5.3         188.5       1.0X
+2500 wide x 40 rows (read parquet)              33 /   47          3.0         334.4       0.5X
+2500 wide x 40 rows (write parquet)            153 /  164          0.7        1528.2       0.1X
+
diff --git a/sql/core/src/test/resources/log4j.properties b/sql/core/src/test/resources/log4j.properties
index e53cb1f4e6..33b9ecf1e2 100644
--- a/sql/core/src/test/resources/log4j.properties
+++ b/sql/core/src/test/resources/log4j.properties
@@ -16,7 +16,7 @@
 #
 
 # Set everything to be logged to the file core/target/unit-tests.log
-log4j.rootLogger=DEBUG, CA, FA
+log4j.rootLogger=INFO, CA, FA
 
 #Console Appender
 log4j.appender.CA=org.apache.log4j.ConsoleAppender
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/WideSchemaBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/WideSchemaBenchmark.scala
index 06466e629b..d2704b3d3f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/WideSchemaBenchmark.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/WideSchemaBenchmark.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import java.io.File
+import java.io.{File, FileOutputStream, OutputStream}
 
 import org.scalatest.BeforeAndAfterEach
 
@@ -29,11 +29,13 @@ import org.apache.spark.util.{Benchmark, Utils}
  * Benchmark for performance with very wide and nested DataFrames.
  * To run this:
  *  build/sbt "sql/test-only *WideSchemaBenchmark"
+ *
+ * Results will be written to "sql/core/benchmarks/WideSchemaBenchmark-results.txt".
  */
 class WideSchemaBenchmark extends SparkFunSuite with BeforeAndAfterEach {
   private val scaleFactor = 100000
-  private val widthsToTest = Seq(1, 10, 100, 1000, 2500)
-  private val depthsToTest = Seq(1, 10, 100, 250)
+  private val widthsToTest = Seq(1, 100, 2500)
+  private val depthsToTest = Seq(1, 100, 250)
   assert(scaleFactor > widthsToTest.max)
 
   private lazy val sparkSession = SparkSession.builder
@@ -44,15 +46,22 @@ class WideSchemaBenchmark extends SparkFunSuite with BeforeAndAfterEach {
   import sparkSession.implicits._
 
   private var tmpFiles: List[File] = Nil
+  private var out: OutputStream = null
+
+  override def beforeAll() {
+    super.beforeAll()
+    out = new FileOutputStream(new File("benchmarks/WideSchemaBenchmark-results.txt"))
+  }
+
+  override def afterAll() {
+    super.afterAll()
+    out.close()
+  }
 
   override def afterEach() {
-    try {
-      for (tmpFile <- tmpFiles) {
-        Utils.deleteRecursively(tmpFile)
-      }
-    } finally {
-      tmpFiles = Nil
-      super.afterEach()
+    super.afterEach()
+    for (tmpFile <- tmpFiles) {
+      Utils.deleteRecursively(tmpFile)
     }
   }
 
@@ -80,7 +89,7 @@ class WideSchemaBenchmark extends SparkFunSuite with BeforeAndAfterEach {
     benchmark.addCase(desc + " (read in-mem)") { iter =>
       df.selectExpr(s"sum($selector)").collect()
     }
-    benchmark.addCase(desc + " (write in-mem)") { iter =>
+    benchmark.addCase(desc + " (exec in-mem)") { iter =>
       df.selectExpr("*", s"hash($selector) as f").selectExpr(s"sum($selector)", "sum(f)").collect()
     }
     val parquet = saveAsParquet(df)
@@ -93,7 +102,7 @@ class WideSchemaBenchmark extends SparkFunSuite with BeforeAndAfterEach {
   }
 
   ignore("parsing large select expressions") {
-    val benchmark = new Benchmark("parsing large select", 1)
+    val benchmark = new Benchmark("parsing large select", 1, output = Some(out))
     for (width <- widthsToTest) {
       val selectExpr = (1 to width).map(i => s"id as a_$i")
       benchmark.addCase(s"$width select expressions") { iter =>
@@ -101,22 +110,10 @@ class WideSchemaBenchmark extends SparkFunSuite with BeforeAndAfterEach {
       }
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-parsing large select:                    Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 select expressions                            22 /   25          0.0    22053737.0       1.0X
-10 select expressions                            8 /   13          0.0     8288520.0       2.7X
-100 select expressions                          29 /   32          0.0    29481040.0       0.7X
-1000 select expressions                        268 /  276          0.0   268183159.0       0.1X
-2500 select expressions                        683 /  691          0.0   683422241.0       0.0X
-*/
   }
 
   ignore("many column field read and write") {
-    val benchmark = new Benchmark("many column field r/w", scaleFactor)
+    val benchmark = new Benchmark("many column field r/w", scaleFactor, output = Some(out))
     for (width <- widthsToTest) {
       // normalize by width to keep constant data size
       val numRows = scaleFactor / width
@@ -126,38 +123,11 @@ parsing large select:                    Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$width cols x $numRows rows", "a_1")
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-many column field r/w:                   Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 cols x 100000 rows (read in-mem)              26 /   33          3.8         262.9       1.0X
-1 cols x 100000 rows (write in-mem)             40 /   51          2.5         401.6       0.7X
-1 cols x 100000 rows (read parquet)             37 /   57          2.7         374.3       0.7X
-1 cols x 100000 rows (write parquet)           105 /  157          0.9        1054.9       0.2X
-10 cols x 10000 rows (read in-mem)              26 /   39          3.8         260.5       1.0X
-10 cols x 10000 rows (write in-mem)             37 /   44          2.7         367.4       0.7X
-10 cols x 10000 rows (read parquet)             31 /   39          3.3         305.1       0.9X
-10 cols x 10000 rows (write parquet)            86 /  137          1.2         860.2       0.3X
-100 cols x 1000 rows (read in-mem)              40 /   64          2.5         401.2       0.7X
-100 cols x 1000 rows (write in-mem)            112 /  139          0.9        1118.3       0.2X
-100 cols x 1000 rows (read parquet)             35 /   52          2.9         349.8       0.8X
-100 cols x 1000 rows (write parquet)           150 /  182          0.7        1497.1       0.2X
-1000 cols x 100 rows (read in-mem)             304 /  362          0.3        3043.6       0.1X
-1000 cols x 100 rows (write in-mem)            647 /  729          0.2        6467.8       0.0X
-1000 cols x 100 rows (read parquet)            194 /  235          0.5        1937.7       0.1X
-1000 cols x 100 rows (write parquet)           511 /  521          0.2        5105.0       0.1X
-2500 cols x 40 rows (read in-mem)              915 /  924          0.1        9148.2       0.0X
-2500 cols x 40 rows (write in-mem)            1856 / 1933          0.1       18558.1       0.0X
-2500 cols x 40 rows (read parquet)             802 /  881          0.1        8019.3       0.0X
-2500 cols x 40 rows (write parquet)           1268 / 1291          0.1       12681.6       0.0X
-*/
   }
 
   ignore("wide shallowly nested struct field read and write") {
     val benchmark = new Benchmark(
-      "wide shallowly nested struct field r/w", scaleFactor)
+      "wide shallowly nested struct field r/w", scaleFactor, output = Some(out))
     for (width <- widthsToTest) {
       val numRows = scaleFactor / width
       var datum: String = "{"
@@ -175,84 +145,10 @@ many column field r/w:                   Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$width wide x $numRows rows", "a.b.c.value_1")
     }
     benchmark.run()
-
-/*
-Java HotSpot(TM) 64-Bit Server VM 1.7.0_80-b15 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-wide shallowly nested struct field r/w:  Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 wide x 100000 rows (read in-mem)             100 /  125          1.0         997.7       1.0X
-1 wide x 100000 rows (write in-mem)            130 /  147          0.8        1302.9       0.8X
-1 wide x 100000 rows (read parquet)            195 /  228          0.5        1951.4       0.5X
-1 wide x 100000 rows (write parquet)           248 /  259          0.4        2479.7       0.4X
-10 wide x 10000 rows (read in-mem)              76 /   89          1.3         757.2       1.3X
-10 wide x 10000 rows (write in-mem)             90 /  116          1.1         900.0       1.1X
-10 wide x 10000 rows (read parquet)             90 /  135          1.1         903.9       1.1X
-10 wide x 10000 rows (write parquet)           222 /  240          0.4        2222.8       0.4X
-100 wide x 1000 rows (read in-mem)              71 /   91          1.4         710.8       1.4X
-100 wide x 1000 rows (write in-mem)            252 /  324          0.4        2522.4       0.4X
-100 wide x 1000 rows (read parquet)            310 /  329          0.3        3095.9       0.3X
-100 wide x 1000 rows (write parquet)           253 /  267          0.4        2525.7       0.4X
-1000 wide x 100 rows (read in-mem)             144 /  160          0.7        1439.5       0.7X
-1000 wide x 100 rows (write in-mem)           2055 / 2326          0.0       20553.9       0.0X
-1000 wide x 100 rows (read parquet)            750 /  925          0.1        7496.8       0.1X
-1000 wide x 100 rows (write parquet)           235 /  317          0.4        2347.5       0.4X
-2500 wide x 40 rows (read in-mem)              219 /  227          0.5        2190.9       0.5X
-2500 wide x 40 rows (write in-mem)            5177 / 5423          0.0       51773.2       0.0X
-2500 wide x 40 rows (read parquet)            1642 / 1714          0.1       16417.7       0.1X
-2500 wide x 40 rows (write parquet)            357 /  381          0.3        3568.2       0.3X
-*/
-  }
-
-  ignore("wide struct field read and write") {
-    val benchmark = new Benchmark("wide struct field r/w", scaleFactor)
-    for (width <- widthsToTest) {
-      val numRows = scaleFactor / width
-      var datum: String = "{"
-      for (i <- 1 to width) {
-        if (i == 1) {
-          datum += s""""value_$i": 1"""
-        } else {
-          datum += s""", "value_$i": 1"""
-        }
-      }
-      datum += "}"
-      val df = sparkSession.read.json(sparkSession.range(numRows).map(_ => datum).rdd).cache()
-      df.count()  // force caching
-      addCases(benchmark, df, s"$width wide x $numRows rows", "value_1")
-    }
-    benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-wide struct field r/w:                   Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 wide x 100000 rows (read in-mem)              22 /   37          4.6         216.8       1.0X
-1 wide x 100000 rows (write in-mem)             37 /   54          2.7         365.6       0.6X
-1 wide x 100000 rows (read parquet)             27 /   44          3.6         274.7       0.8X
-1 wide x 100000 rows (write parquet)           155 /  183          0.6        1546.3       0.1X
-10 wide x 10000 rows (read in-mem)              27 /   40          3.7         272.1       0.8X
-10 wide x 10000 rows (write in-mem)             32 /   44          3.2         315.7       0.7X
-10 wide x 10000 rows (read parquet)             31 /   44          3.2         309.8       0.7X
-10 wide x 10000 rows (write parquet)           151 /  169          0.7        1509.3       0.1X
-100 wide x 1000 rows (read in-mem)              37 /   62          2.7         374.4       0.6X
-100 wide x 1000 rows (write in-mem)             81 /   96          1.2         805.6       0.3X
-100 wide x 1000 rows (read parquet)             31 /   44          3.3         307.3       0.7X
-100 wide x 1000 rows (write parquet)           174 /  209          0.6        1745.0       0.1X
-1000 wide x 100 rows (read in-mem)             308 /  339          0.3        3082.4       0.1X
-1000 wide x 100 rows (write in-mem)            672 /  696          0.1        6717.7       0.0X
-1000 wide x 100 rows (read parquet)            182 /  228          0.5        1821.2       0.1X
-1000 wide x 100 rows (write parquet)           484 /  497          0.2        4841.2       0.0X
-2500 wide x 40 rows (read in-mem)              727 /  786          0.1        7268.4       0.0X
-2500 wide x 40 rows (write in-mem)            1734 / 1782          0.1       17341.5       0.0X
-2500 wide x 40 rows (read parquet)             882 /  935          0.1        8816.8       0.0X
-2500 wide x 40 rows (write parquet)            935 /  982          0.1        9351.9       0.0X
-*/
   }
 
   ignore("deeply nested struct field read and write") {
-    val benchmark = new Benchmark("deeply nested struct field r/w", scaleFactor)
+    val benchmark = new Benchmark("deeply nested struct field r/w", scaleFactor, output = Some(out))
     for (depth <- depthsToTest) {
       val numRows = scaleFactor / depth
       var datum: String = "{\"value\": 1}"
@@ -266,34 +162,11 @@ wide struct field r/w:                   Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$depth deep x $numRows rows", selector)
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-deeply nested struct field r/w:          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 deep x 100000 rows (read in-mem)              24 /   39          4.2         239.0       1.0X
-1 deep x 100000 rows (write in-mem)             34 /   47          3.0         335.1       0.7X
-1 deep x 100000 rows (read parquet)             45 /   51          2.2         446.1       0.5X
-1 deep x 100000 rows (write parquet)            86 /  108          1.2         859.4       0.3X
-10 deep x 10000 rows (read in-mem)              28 /   38          3.6         275.1       0.9X
-10 deep x 10000 rows (write in-mem)             43 /   64          2.3         427.1       0.6X
-10 deep x 10000 rows (read parquet)             44 /   59          2.3         438.1       0.5X
-10 deep x 10000 rows (write parquet)            85 /  110          1.2         853.6       0.3X
-100 deep x 1000 rows (read in-mem)              79 /  100          1.3         785.5       0.3X
-100 deep x 1000 rows (write in-mem)            776 /  800          0.1        7760.3       0.0X
-100 deep x 1000 rows (read parquet)           3302 / 3394          0.0       33021.2       0.0X
-100 deep x 1000 rows (write parquet)           226 /  243          0.4        2259.0       0.1X
-250 deep x 400 rows (read in-mem)              610 /  639          0.2        6104.0       0.0X
-250 deep x 400 rows (write in-mem)            8526 / 8531          0.0       85256.9       0.0X
-250 deep x 400 rows (read parquet)          54968 / 55069          0.0      549681.4       0.0X
-250 deep x 400 rows (write parquet)            714 /  718          0.1        7143.0       0.0X
-*/
   }
 
   ignore("bushy struct field read and write") {
-    val benchmark = new Benchmark("bushy struct field r/w", scaleFactor)
-    for (width <- Seq(1, 10, 100, 500)) {
+    val benchmark = new Benchmark("bushy struct field r/w", scaleFactor, output = Some(out))
+    for (width <- Seq(1, 100, 1000)) {
       val numRows = scaleFactor / width
       var numNodes = 1
       var datum: String = "{\"value\": 1}"
@@ -312,33 +185,10 @@ deeply nested struct field r/w:          Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$numNodes x $depth deep x $numRows rows", selector)
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-bushy struct field r/w:                  Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 x 1 deep x 100000 rows (read in-mem)         21 /   27          4.7         212.6       1.0X
-1 x 1 deep x 100000 rows (write in-mem)        27 /   38          3.8         265.8       0.8X
-1 x 1 deep x 100000 rows (read parquet)        26 /   32          3.9         259.1       0.8X
-1 x 1 deep x 100000 rows (write parquet)      150 /  169          0.7        1499.5       0.1X
-16 x 5 deep x 10000 rows (read in-mem)         26 /   45          3.9         258.7       0.8X
-16 x 5 deep x 10000 rows (write in-mem)        54 /   58          1.9         535.1       0.4X
-16 x 5 deep x 10000 rows (read parquet)        60 /   84          1.7         595.8       0.4X
-16 x 5 deep x 10000 rows (write parquet)      179 /  184          0.6        1787.5       0.1X
-128 x 8 deep x 1000 rows (read in-mem)         26 /   40          3.8         261.4       0.8X
-128 x 8 deep x 1000 rows (write in-mem)       592 /  592          0.2        5915.3       0.0X
-128 x 8 deep x 1000 rows (read parquet)       203 /  251          0.5        2031.8       0.1X
-128 x 8 deep x 1000 rows (write parquet)      105 /  131          1.0        1045.2       0.2X
-512 x 10 deep x 200 rows (read in-mem)        101 /  125          1.0        1007.4       0.2X
-512 x 10 deep x 200 rows (write in-mem)      6778 / 6943          0.0       67781.1       0.0X
-512 x 10 deep x 200 rows (read parquet)       958 / 1071          0.1        9584.9       0.0X
-512 x 10 deep x 200 rows (write parquet)      173 /  207          0.6        1726.1       0.1X
-*/
   }
 
   ignore("wide array field read and write") {
-    val benchmark = new Benchmark("wide array field r/w", scaleFactor)
+    val benchmark = new Benchmark("wide array field r/w", scaleFactor, output = Some(out))
     for (width <- widthsToTest) {
       val numRows = scaleFactor / width
       var datum: String = "{\"value\": ["
@@ -355,37 +205,10 @@ bushy struct field r/w:                  Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$width wide x $numRows rows", "value[0]")
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-wide array field r/w:                    Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 wide x 100000 rows (read in-mem)              27 /   45          3.7         268.0       1.0X
-1 wide x 100000 rows (write in-mem)             37 /   52          2.7         368.3       0.7X
-1 wide x 100000 rows (read parquet)             52 /   65          1.9         524.9       0.5X
-1 wide x 100000 rows (write parquet)           102 /  139          1.0        1016.7       0.3X
-10 wide x 10000 rows (read in-mem)              20 /   26          5.0         201.7       1.3X
-10 wide x 10000 rows (write in-mem)             26 /   35          3.8         259.8       1.0X
-10 wide x 10000 rows (read parquet)             39 /   59          2.5         393.8       0.7X
-10 wide x 10000 rows (write parquet)           120 /  143          0.8        1201.4       0.2X
-100 wide x 1000 rows (read in-mem)              24 /   31          4.2         240.1       1.1X
-100 wide x 1000 rows (write in-mem)             26 /   35          3.8         264.1       1.0X
-100 wide x 1000 rows (read parquet)             30 /   47          3.4         296.8       0.9X
-100 wide x 1000 rows (write parquet)           109 /  147          0.9        1094.8       0.2X
-1000 wide x 100 rows (read in-mem)              20 /   38          5.0         200.6       1.3X
-1000 wide x 100 rows (write in-mem)             24 /   32          4.1         242.3       1.1X
-1000 wide x 100 rows (read parquet)             47 /   55          2.1         470.1       0.6X
-1000 wide x 100 rows (write parquet)           146 /  164          0.7        1465.0       0.2X
-2500 wide x 40 rows (read in-mem)               20 /   28          5.1         196.1       1.4X
-2500 wide x 40 rows (write in-mem)              25 /   27          4.0         249.3       1.1X
-2500 wide x 40 rows (read parquet)              33 /   48          3.0         332.0       0.8X
-2500 wide x 40 rows (write parquet)            149 /  176          0.7        1489.3       0.2X
-*/
   }
 
   ignore("wide map field read and write") {
-    val benchmark = new Benchmark("wide map field r/w", scaleFactor)
+    val benchmark = new Benchmark("wide map field r/w", scaleFactor, output = Some(out))
     for (width <- widthsToTest) {
       val numRows = scaleFactor / width
       val datum = Tuple1((1 to width).map(i => ("value_" + i -> 1)).toMap)
@@ -394,32 +217,5 @@ wide array field r/w:                    Best/Avg Time(ms)    Rate(M/s)   Per Ro
       addCases(benchmark, df, s"$width wide x $numRows rows", "_1[\"value_1\"]")
     }
     benchmark.run()
-
-/*
-OpenJDK 64-Bit Server VM 1.8.0_66-internal-b17 on Linux 4.2.0-36-generic
-Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
-wide map field r/w:                      Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------------
-1 wide x 100000 rows (read in-mem)              27 /   42          3.7         270.9       1.0X
-1 wide x 100000 rows (write in-mem)             40 /   63          2.5         403.4       0.7X
-1 wide x 100000 rows (read parquet)             71 /  114          1.4         705.8       0.4X
-1 wide x 100000 rows (write parquet)           169 /  184          0.6        1689.7       0.2X
-10 wide x 10000 rows (read in-mem)              22 /   35          4.6         216.6       1.3X
-10 wide x 10000 rows (write in-mem)             29 /   34          3.5         285.6       0.9X
-10 wide x 10000 rows (read parquet)             61 /   81          1.6         610.3       0.4X
-10 wide x 10000 rows (write parquet)           150 /  172          0.7        1504.7       0.2X
-100 wide x 1000 rows (read in-mem)              21 /   29          4.8         207.9       1.3X
-100 wide x 1000 rows (write in-mem)             30 /   57          3.3         304.9       0.9X
-100 wide x 1000 rows (read parquet)             36 /   61          2.8         356.7       0.8X
-100 wide x 1000 rows (write parquet)           108 /  136          0.9        1075.7       0.3X
-1000 wide x 100 rows (read in-mem)              22 /   31          4.5         223.0       1.2X
-1000 wide x 100 rows (write in-mem)             33 /   41          3.0         332.0       0.8X
-1000 wide x 100 rows (read parquet)             49 /   66          2.0         493.6       0.5X
-1000 wide x 100 rows (write parquet)           127 /  139          0.8        1265.9       0.2X
-2500 wide x 40 rows (read in-mem)               23 /   34          4.4         226.0       1.2X
-2500 wide x 40 rows (write in-mem)              33 /   42          3.1         326.6       0.8X
-2500 wide x 40 rows (read parquet)              36 /   48          2.8         359.2       0.8X
-2500 wide x 40 rows (write parquet)            155 /  168          0.6        1549.2       0.2X
-*/
   }
 }