Github user thunterdb commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18798#discussion_r131971123
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala ---
    @@ -0,0 +1,587 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.ml.stat
    +
    +import java.io._
    +
    +import org.apache.spark.annotation.Since
    +import org.apache.spark.internal.Logging
    +import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
    +import org.apache.spark.sql.Column
    +import org.apache.spark.sql.catalyst.InternalRow
    +import org.apache.spark.sql.catalyst.expressions.{Expression, 
UnsafeArrayData}
    +import 
org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, 
Complete, TypedImperativeAggregate}
    +import org.apache.spark.sql.catalyst.util.ArrayData
    +import org.apache.spark.sql.functions.lit
    +import org.apache.spark.sql.types._
    +
    +/**
    + * A builder object that provides summary statistics about a given column.
    + *
    + * Users should not directly create such builders, but instead use one of 
the methods in
    + * [[Summarizer]].
    + */
    +@Since("2.3.0")
    +abstract class SummaryBuilder {
    +  /**
    +   * Returns an aggregate object that contains the summary of the column 
with the requested metrics.
    +   * @param featuresCol a column that contains features Vector object.
    +   * @param weightCol a column that contains weight value.
    +   * @return an aggregate column that contains the statistics. The exact 
content of this
    +   *         structure is determined during the creation of the builder.
    +   */
    +  @Since("2.3.0")
    +  def summary(featuresCol: Column, weightCol: Column): Column
    +
    +  @Since("2.3.0")
    +  def summary(featuresCol: Column): Column = summary(featuresCol, lit(1.0))
    +}
    +
    +/**
    + * Tools for vectorized statistics on MLlib Vectors.
    + *
    + * The methods in this package provide various statistics for Vectors 
contained inside DataFrames.
    + *
    + * This class lets users pick the statistics they would like to extract 
for a given column. Here is
    + * an example in Scala:
    + * {{{
    + *   val dataframe = ... // Some dataframe containing a feature column
    + *   val allStats = dataframe.select(Summarizer.metrics("min", 
"max").summary($"features"))
    + *   val Row(min_, max_) = allStats.first()
    + * }}}
    + *
    + * If one wants to get a single metric, shortcuts are also available:
    + * {{{
    + *   val meanDF = dataframe.select(Summarizer.mean($"features"))
    + *   val Row(mean_) = meanDF.first()
    + * }}}
    + */
    --- End diff --
    
    Put a comment about performance here.


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