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Posted to reviews@spark.apache.org by "LuciferYang (via GitHub)" <gi...@apache.org> on 2023/03/02 16:13:05 UTC
[GitHub] [spark] LuciferYang opened a new pull request, #40255: [SPARK-42558][CONNECT] DataFrameStatFunctions WIP
LuciferYang opened a new pull request, #40255:
URL: https://github.com/apache/spark/pull/40255
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124699930
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
Review Comment:
I keep it as it is now
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124797844
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ sparkSession.newDataFrame { builder =>
Review Comment:
The results of local test and GA test are different
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[GitHub] [spark] hvanhovell commented on pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1453850392
@LuciferYang can you update the binary compatibility tests?
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125221876
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/client/CheckConnectJvmClientCompatibility.scala:
##########
@@ -133,6 +134,10 @@ object CheckConnectJvmClientCompatibility {
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameReader.jdbc"),
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameWriter.jdbc"),
+ // DataFrameStatFunctions
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.bloomFilter"),
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.this"),
Review Comment:
yes you are right.... :)
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124806858
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ sparkSession.newDataFrame { builder =>
Review Comment:
I changed a simple case to try again
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124451582
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
Review Comment:
`newDataset(PrimitiveDoubleEncoder)`?
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124686005
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
Review Comment:
There need `Array[Array[Double]]` Encoder, need make `newArrayEncoder` as private[sql] and write like
```
newArrayEncoder(newDoubleArrayEncoder.asInstanceOf[ArrayEncoder[Double]])
```
?
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124585799
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
Review Comment:
@zhengruifeng @amaliujia do you know why we implemented a separate proto messages for this? It seems all of this can be implemented using aggregates.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125473488
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
Review Comment:
it is only py4j? if so, then let's get rid of it.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124795511
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ sparkSession.newDataFrame { builder =>
Review Comment:
I don't see a problem. Is it not working?
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[GitHub] [spark] LuciferYang commented on pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1455324716
Thanks @hvanhovell @HyukjinKwon @zhengruifeng @amaliujia
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[GitHub] [spark] hvanhovell closed pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell closed pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
URL: https://github.com/apache/spark/pull/40255
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124696192
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
Review Comment:
done
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] DataFrameStatFunctions WIP
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1123365858
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
Review Comment:
Missing proto definition, will be implemented in followup
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, numBits, Double.NaN)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, numBits, Double.NaN)
+ }
+
+ private def buildBloomFilter(
+ col: Column,
+ expectedNumItems: Long,
+ numBits: Long,
+ fpp: Double): BloomFilter = {
+ throw new UnsupportedOperationException("buildBloomFilter is not implemented.")
Review Comment:
Missing proto definition, will be implemented in followup
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125217576
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/client/CheckConnectJvmClientCompatibility.scala:
##########
@@ -133,6 +134,10 @@ object CheckConnectJvmClientCompatibility {
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameReader.jdbc"),
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameWriter.jdbc"),
+ // DataFrameStatFunctions
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.bloomFilter"),
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.this"),
Review Comment:
Please also remove `ProblemFilters.exclude[Problem]("org.apache.spark.sql.Dataset.na")`
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[GitHub] [spark] LuciferYang commented on pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1454330973
Now all paased
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124736465
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
Review Comment:
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125222488
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/client/CheckConnectJvmClientCompatibility.scala:
##########
@@ -133,6 +134,10 @@ object CheckConnectJvmClientCompatibility {
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameReader.jdbc"),
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameWriter.jdbc"),
+ // DataFrameStatFunctions
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.bloomFilter"),
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.this"),
Review Comment:
done
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124462028
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, numBits, Double.NaN)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, numBits, Double.NaN)
+ }
+
+ private def buildBloomFilter(
+ col: Column,
+ expectedNumItems: Long,
+ numBits: Long,
+ fpp: Double): BloomFilter = {
+ throw new UnsupportedOperationException("buildBloomFilter is not implemented.")
Review Comment:
Please remove this for now. Can you file an follow-up ticket.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124461534
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
Review Comment:
You can implement this by using the aggregate function:
```scala
val agg = Column.fn("count_min_sketch", col.expr, lit(eps), lit(confidence), lit(seed))
val ds = sparkSession.newDataset(BinaryEncoder) { builder =>
builder.getProjectBuilder
.setInput(plan.getRoot)
.addExpressions(agg.expr)
}
CountMinSketch.readFrom(ds.head())
```
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125221046
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/client/CheckConnectJvmClientCompatibility.scala:
##########
@@ -133,6 +134,10 @@ object CheckConnectJvmClientCompatibility {
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameReader.jdbc"),
ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameWriter.jdbc"),
+ // DataFrameStatFunctions
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.bloomFilter"),
+ ProblemFilters.exclude[Problem]("org.apache.spark.sql.DataFrameStatFunctions.this"),
Review Comment:
Are you want to say `org.apache.spark.sql.Dataset.stat`? `na` is `DataFrameNaFunctions`, not `DataFrameStatFunctions`
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124580063
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, numBits, Double.NaN)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, numBits, Double.NaN)
+ }
+
+ private def buildBloomFilter(
+ col: Column,
+ expectedNumItems: Long,
+ numBits: Long,
+ fpp: Double): BloomFilter = {
+ throw new UnsupportedOperationException("buildBloomFilter is not implemented.")
Review Comment:
we can also add the aggregate function, but I would do that in a separate pr
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[GitHub] [spark] amaliujia commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124913199
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
Review Comment:
I don't remember the decision now. Will need @zhengruifeng to comment
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124788183
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataset(PrimitiveDoubleEncoder) { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ sparkSession.newDataFrame { builder =>
Review Comment:
@hvanhovell Is there any error in the construction of this dataframe?
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124775799
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
Review Comment:
This is fine. Can you put it in a separate val? that will make the code more readable. You can also put it in static since the encoder is immutable.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125375617
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
Review Comment:
do we need this function?
python client will not invoke this method in scala client via py4j.
https://github.com/apache/spark/blob/3d900b70c5593326ddc96f094d9abe796308b0e4/python/pyspark/sql/dataframe.py#L4433
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[GitHub] [spark] hvanhovell commented on pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1455323028
Merging.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124450308
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
Review Comment:
You can use `sparkSession.implicits.newDoubleArrayEncoder` here for the nested encoder.
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] DataFrameStatFunctions WIP
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1123361868
##########
connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/ClientE2ETestSuite.scala:
##########
@@ -586,6 +586,57 @@ class ClientE2ETestSuite extends RemoteSparkSession {
list.asScala.map(kv => Row(kv.key, kv.value)),
session.createDataFrame(list.asScala.toSeq))
}
+
+ test("approximate quantile") {
Review Comment:
will add more case later
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124451930
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
Review Comment:
newDataset(PrimitiveDoubleEncoder)?
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
Review Comment:
`newDataset(PrimitiveDoubleEncoder)`?
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124686005
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,670 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
Review Comment:
There need `Array[Array[Double]]` Encoder, need make `sparkSession.implicits.newArrayEncoder` as private[sql] and write like
```
newArrayEncoder(newDoubleArrayEncoder.asInstanceOf[ArrayEncoder[Double]])
```
?
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1124699079
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,665 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.Relation
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch}
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0,
+ s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(
+ ArrayEncoder(
+ ArrayEncoder(PrimitiveDoubleEncoder, containsNull = false),
+ containsNull = false)) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
+ cols: List[String],
+ probabilities: List[Double],
+ relativeError: Double): java.util.List[java.util.List[Double]] = {
+ approxQuantile(cols.toArray, probabilities.toArray, relativeError)
+ .map(_.toList.asJava)
+ .toList
+ .asJava
+ }
+
+ /**
+ * Calculate the sample covariance of two numerical columns of a DataFrame.
+ * @param col1
+ * the name of the first column
+ * @param col2
+ * the name of the second column
+ * @return
+ * the covariance of the two columns.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.cov("rand1", "rand2")
+ * res1: Double = 0.065...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def cov(col1: String, col2: String): Double = {
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+ * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in
+ * MLlib's Statistics.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String, method: String): Double = {
+ require(
+ method == "pearson",
+ "Currently only the calculation of the Pearson Correlation " +
+ "coefficient is supported.")
+ sparkSession
+ .newDataFrame { builder =>
+ builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ .head()
+ .getDouble(0)
+ }
+
+ /**
+ * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+ *
+ * @param col1
+ * the name of the column
+ * @param col2
+ * the name of the column to calculate the correlation against
+ * @return
+ * The Pearson Correlation Coefficient as a Double.
+ *
+ * {{{
+ * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+ * .withColumn("rand2", rand(seed=27))
+ * df.stat.corr("rand1", "rand2", "pearson")
+ * res1: Double = 0.613...
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def corr(col1: String, col2: String): Double = {
+ corr(col1, col2, "pearson")
+ }
+
+ /**
+ * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+ * 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 zero as their counts.
+ * Null elements will be replaced by "null", and back ticks will be dropped from elements if
+ * they exist.
+ *
+ * @param col1
+ * The name of the first column. Distinct items will make the first item of each row.
+ * @param col2
+ * The name of the second column. Distinct items will make the column names of the DataFrame.
+ * @return
+ * A DataFrame containing for the contingency table.
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)))
+ * .toDF("key", "value")
+ * val ct = df.stat.crosstab("key", "value")
+ * ct.show()
+ * +---------+---+---+---+
+ * |key_value| 1| 2| 3|
+ * +---------+---+---+---+
+ * | 2| 2| 0| 1|
+ * | 1| 1| 1| 0|
+ * | 3| 0| 1| 1|
+ * +---------+---+---+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def crosstab(col1: String, col2: String): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @param support
+ * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String], support: Double): DataFrame = {
+ sparkSession.newDataFrame { builder =>
+ val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support)
+ cols.foreach(freqItemsBuilder.addCols)
+ }
+ }
+
+ /**
+ * Finding frequent items for columns, possibly with false positives. Using the frequent element
+ * count algorithm described in <a href="https://doi.org/10.1145/762471.762473">here</a>,
+ * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Array[String]): DataFrame = {
+ freqItems(cols, 0.01)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * {{{
+ * val rows = Seq.tabulate(100) { i =>
+ * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+ * }
+ * val df = spark.createDataFrame(rows).toDF("a", "b")
+ * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+ * // "a" and "b"
+ * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+ * freqSingles.show()
+ * +-----------+-------------+
+ * |a_freqItems| b_freqItems|
+ * +-----------+-------------+
+ * | [1, 99]|[-1.0, -99.0]|
+ * +-----------+-------------+
+ * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+ * val pairDf = df.select(struct("a", "b").as("a-b"))
+ * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+ * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+ * +----------+
+ * | freq_ab|
+ * +----------+
+ * | [1,-1.0]|
+ * | ... |
+ * +----------+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String], support: Double): DataFrame = {
+ freqItems(cols.toArray, support)
+ }
+
+ /**
+ * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+ * frequent element count algorithm described in <a
+ * href="https://doi.org/10.1145/762471.762473">here</a>, proposed by Karp, Schenker, and
+ * Papadimitriou. Uses a `default` support of 1%.
+ *
+ * This function is meant for exploratory data analysis, as we make no guarantee about the
+ * backward compatibility of the schema of the resulting `DataFrame`.
+ *
+ * @param cols
+ * the names of the columns to search frequent items in.
+ * @return
+ * A Local DataFrame with the Array of frequent items for each column.
+ *
+ * @since 3.4.0
+ */
+ def freqItems(cols: Seq[String]): DataFrame = {
+ freqItems(cols.toArray, 0.01)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * {{{
+ * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+ * (3, 3))).toDF("key", "value")
+ * val fractions = Map(1 -> 1.0, 3 -> 0.5)
+ * df.stat.sampleBy("key", fractions, 36L).show()
+ * +---+-----+
+ * |key|value|
+ * +---+-----+
+ * | 1| 1|
+ * | 1| 2|
+ * | 3| 2|
+ * +---+-----+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {
+ sampleBy(Column(col), fractions, seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Returns a stratified sample without replacement based on the fraction given on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * The stratified sample can be performed over multiple columns:
+ * {{{
+ * import org.apache.spark.sql.Row
+ * import org.apache.spark.sql.functions.struct
+ *
+ * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
+ * ("Alice", 10))).toDF("name", "age")
+ * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
+ * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+ * +-----+---+
+ * | name|age|
+ * +-----+---+
+ * | Nico| 8|
+ * |Alice| 10|
+ * +-----+---+
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = {
+ require(
+ fractions.values.forall(p => p >= 0.0 && p <= 1.0),
+ s"Fractions must be in [0, 1], but got $fractions.")
+ import org.apache.spark.sql.functions.{rand, udf}
+ val r = rand(seed)
+ val f = udf { (stratum: Any, x: Double) =>
+ x < fractions.getOrElse(stratum.asInstanceOf[T], 0.0)
+ }
+ sparkSession.newDataFrame { builder =>
+ builder.getFilterBuilder.setInput(root).setCondition(f(col, r).expr)
+ }
+ }
+
+ /**
+ * (Java-specific) Returns a stratified sample without replacement based on the fraction given
+ * on each stratum.
+ * @param col
+ * column that defines strata
+ * @param fractions
+ * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as
+ * zero.
+ * @param seed
+ * random seed
+ * @tparam T
+ * stratum type
+ * @return
+ * a new `DataFrame` that represents the stratified sample
+ *
+ * @since 3.4.0
+ */
+ def sampleBy[T](col: Column, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = {
+ sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), depth, width, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param colName
+ * name of the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(
+ colName: String,
+ eps: Double,
+ confidence: Double,
+ seed: Int): CountMinSketch = {
+ countMinSketch(Column(colName), eps, confidence, seed)
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param depth
+ * depth of the sketch
+ * @param width
+ * width of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(depth, width, seed))
+ }
+
+ /**
+ * Builds a Count-min Sketch over a specified column.
+ *
+ * @param col
+ * the column over which the sketch is built
+ * @param eps
+ * relative error of the sketch
+ * @param confidence
+ * confidence of the sketch
+ * @param seed
+ * random seed
+ * @return
+ * a `CountMinSketch` over column `colName`
+ * @since 3.4.0
+ */
+ def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch = {
+ countMinSketch(col, CountMinSketch.create(eps, confidence, seed))
+ }
+
+ private def countMinSketch(col: Column, zero: CountMinSketch): CountMinSketch = {
+ throw new UnsupportedOperationException("countMinSketch is not implemented.")
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param fpp
+ * expected false positive probability of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, -1L, fpp)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param colName
+ * name of the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(Column(colName), expectedNumItems, numBits, Double.NaN)
+ }
+
+ /**
+ * Builds a Bloom filter over a specified column.
+ *
+ * @param col
+ * the column over which the filter is built
+ * @param expectedNumItems
+ * expected number of items which will be put into the filter.
+ * @param numBits
+ * expected number of bits of the filter.
+ * @since 3.4.0
+ */
+ def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter = {
+ buildBloomFilter(col, expectedNumItems, numBits, Double.NaN)
+ }
+
+ private def buildBloomFilter(
+ col: Column,
+ expectedNumItems: Long,
+ numBits: Long,
+ fpp: Double): BloomFilter = {
+ throw new UnsupportedOperationException("buildBloomFilter is not implemented.")
Review Comment:
create [SPARK-42664](https://issues.apache.org/jira/browse/SPARK-42664)
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[GitHub] [spark] LuciferYang commented on pull request #40255: [SPARK-42558][CONNECT] Partial implement `DataFrameStatFunctions`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1453872602
> @LuciferYang can you update the binary compatibility tests?
done
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[GitHub] [spark] LuciferYang commented on a diff in pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #40255:
URL: https://github.com/apache/spark/pull/40255#discussion_r1125591758
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -0,0 +1,605 @@
+/*
+ * 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.sql
+
+import java.{lang => jl, util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto.{Relation, StatSampleBy}
+import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder
+import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * Statistic functions for `DataFrame`s.
+ *
+ * @since 3.4.0
+ */
+final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) {
+
+ /**
+ * Calculates the approximate quantiles of a numerical column of a DataFrame.
+ *
+ * The result of this algorithm has the following deterministic bound: If the DataFrame has N
+ * elements and if we request the quantile at probability `p` up to error `err`, then the
+ * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is
+ * close to (p * N). More precisely,
+ *
+ * {{{
+ * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
+ * }}}
+ *
+ * This method implements a variation of the Greenwald-Khanna algorithm (with some speed
+ * optimizations). The algorithm was first present in <a
+ * href="https://doi.org/10.1145/375663.375670"> Space-efficient Online Computation of Quantile
+ * Summaries</a> by Greenwald and Khanna.
+ *
+ * @param col
+ * the name of the numerical column
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities
+ *
+ * @note
+ * null and NaN values will be removed from the numerical column before calculation. If the
+ * dataframe is empty or the column only contains null or NaN, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ col: String,
+ probabilities: Array[Double],
+ relativeError: Double): Array[Double] = {
+ approxQuantile(Array(col), probabilities, relativeError).head
+ }
+
+ /**
+ * Calculates the approximate quantiles of numerical columns of a DataFrame.
+ * @see
+ * `approxQuantile(col:Str* approxQuantile)` for detailed description.
+ *
+ * @param cols
+ * the names of the numerical columns
+ * @param probabilities
+ * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the
+ * minimum, 0.5 is the median, 1 is the maximum.
+ * @param relativeError
+ * The relative target precision to achieve (greater than or equal to 0). If set to zero, the
+ * exact quantiles are computed, which could be very expensive. Note that values greater than
+ * 1 are accepted but give the same result as 1.
+ * @return
+ * the approximate quantiles at the given probabilities of each column
+ *
+ * @note
+ * null and NaN values will be ignored in numerical columns before calculation. For columns
+ * only containing null or NaN values, an empty array is returned.
+ *
+ * @since 3.4.0
+ */
+ def approxQuantile(
+ cols: Array[String],
+ probabilities: Array[Double],
+ relativeError: Double): Array[Array[Double]] = {
+ require(
+ probabilities.forall(p => p >= 0.0 && p <= 1.0),
+ "percentile should be in the range [0.0, 1.0]")
+ require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError")
+ sparkSession
+ .newDataset(approxQuantileResultEncoder) { builder =>
+ val approxQuantileBuilder = builder.getApproxQuantileBuilder
+ .setInput(root)
+ .setRelativeError(relativeError)
+ cols.foreach(approxQuantileBuilder.addCols)
+ probabilities.foreach(approxQuantileBuilder.addProbabilities)
+ }
+ .head()
+ }
+
+ /**
+ * Python-friendly version of [[approxQuantile()]]
+ */
+ private[spark] def approxQuantile(
Review Comment:
already removed
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[GitHub] [spark] LuciferYang commented on pull request #40255: [SPARK-42558][CONNECT] Implement `DataFrameStatFunctions` except `bloomFilter` functions
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on PR #40255:
URL: https://github.com/apache/spark/pull/40255#issuecomment-1454994633
GA passed
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