diff options
Diffstat (limited to 'sql/core')
| -rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala | 9 | ||||
| -rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala | 6 |
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 |