[SPARK-7243][SQL] Reduce size for Contingency Tables in DataFrames

Reduced take size from 1e8 to 1e6.

cc rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #5900 from brkyvz/df-cont-followup and squashes the following commits:

c11e762 [Burak Yavuz] fix grammar
b30ace2 [Burak Yavuz] address comments
a417ba5 [Burak Yavuz] [SPARK-7243][SQL] Reduce  size for Contingency Tables in DataFrames

2 files changed, 8 insertions, 7 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index fcf21ca741..cb88deab35 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -65,10 +65,11 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
 
   /**
    * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
-   * The number of distinct values for each column should be less than 1e4. The first
-   * column of each row will be the distinct values of `col1` and the column names will be the
-   * distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts will be
-   * returned as `Long`s. Pairs that have no occurrences will have `null` as their counts.
+   * The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero
+   * pair frequencies will be returned.
+   * The first column of each row will be the distinct values of `col1` and the column names will
+   * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts
+   * will be returned as `Long`s. Pairs that have no occurrences will have `null` as their counts.
    *
    * @param col1 The name of the first column. Distinct items will make the first item of
    *             each row.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index b50f606d9c..386ac969f1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -102,9 +102,9 @@ private[sql] object StatFunctions extends Logging {
   /** Generate a table of frequencies for the elements of two columns. */
   private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = {
     val tableName = s"${col1}_$col2"
-    val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e8.toInt)
-    if (counts.length == 1e8.toInt) {
-      logWarning("The maximum limit of 1e8 pairs have been collected, which may not be all of " +
+    val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt)
+    if (counts.length == 1e6.toInt) {
+      logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
         "the pairs. Please try reducing the amount of distinct items in your columns.")
     }
     // get the distinct values of column 2, so that we can make them the column names