Github user gczsjdy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17359#discussion_r107418753
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/NGrams.scala
---
@@ -0,0 +1,258 @@
+/*
+ * 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.catalyst.expressions.aggregate
+
+import java.nio.ByteBuffer
+import java.util.HashMap
+
+import org.apache.spark.serializer.KryoSerializer
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult._
+import org.apache.spark.sql.catalyst.expressions.{Expression,
ExpressionDescription, ImplicitCastInputTypes, Literal}
+import
org.apache.spark.sql.catalyst.expressions.aggregate.NGrams.NGramBuffer
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData,
GenericArrayData}
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * Return the top-k n-grams in rows that consist of sequences of strings.
+ */
+@ExpressionDescription(
+ usage = """
+ _FUNC_(expr, n, k, accuracy) - Estimates the top-k n-grams in rows
that consist of sequences
+ of strings, represented as arrays of strings, or arrays of arrays of
strings. 'accuracy' is an
+ optional precision factor that controls memory usage.
+ The parameter 'n' specifies what type of n-grams are being
estimated. Unigrams are n = 1, and
+ bigrams are n = 2. Generally, n will not be greater than about 5.
The 'k' parameter specifies
+ how many of the highest-frequency n-grams will be returned by the
UDAF. The optional precision
+ factor 'accuracy' specifies how much memory to use for estimation;
more memory will give
+ more accurate frequency counts, but could crash the JVM. The value
will be the max between
+ 'accuracy'(0 if it's not specified) and 1000/k, which indicates the
max number of n-grams
+ which are kept in the internal HashMap.
+ The output is an array of maps with the top-k n-grams and
corresponding frequency.
+ """,
+ extended = """
+ Examples:
+ > SELECT ngrams(array("abc", "abc", "bcd", "abc", "bcd"), 2, 4);
+ [{["abc","bcd"]:2.0},
+ {["abc","abc"]:1.0},
+ {["bcd","abc"]:1.0}]
+ """)
+case class NGrams(
+ child: Expression,
+ nExpression: Expression,
+ kExpression: Expression,
+ accuracyExpression: Expression,
+ override val mutableAggBufferOffset: Int,
+ override val inputAggBufferOffset: Int)
+ extends TypedImperativeAggregate[NGramBuffer] with
ImplicitCastInputTypes {
+
+ def this(child: Expression, nExpression: Expression, kExpression:
Expression,
+ accuracyExpression: Expression) = {
+ this(child, nExpression, kExpression, NGrams.getAccuracy(kExpression,
accuracyExpression), 0, 0)
+ }
+
+ def this(child: Expression, nExpression: Expression, kExpression:
Expression) = {
+ this(child, nExpression, kExpression, Literal(0))
+ }
+
+ private lazy val n: Int = nExpression.eval().asInstanceOf[Int]
+ private lazy val k: Int = kExpression.eval().asInstanceOf[Int]
+ private lazy val accuracy: Int =
accuracyExpression.eval().asInstanceOf[Int]
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ Seq(TypeCollection(ArrayType(StringType, false),
ArrayType(ArrayType(StringType, false))),
+ IntegerType, IntegerType, IntegerType)
+ }
+
+ val isArrayOfString = child.dataType == ArrayType(StringType, false) ||
+ child.dataType == ArrayType(StringType, true)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!nExpression.foldable || !kExpression.foldable ||
!accuracyExpression.foldable) {
+ TypeCheckFailure(s"The accuracy or percentage provided must be a
constant literal")
+ } else if (accuracy <= 0) {
+ TypeCheckFailure(
+ s"The accuracy provided must be a positive integer literal
(current value = $accuracy)")
+ }
+ else {
+ TypeCheckSuccess
+ }
+ }
+
+ override def createAggregationBuffer(): NGramBuffer = {
+ new NGramBuffer(n, k, accuracy, new HashMap[Vector[UTF8String],
Double]())
+ }
+
+ def updateArray(genericArrayData: GenericArrayData, buffer: NGramBuffer,
inputRow: InternalRow) {
+ val values = (0 until
genericArrayData.numElements()).map(genericArrayData.get(_, StringType).
+ asInstanceOf[UTF8String]).toVector
+ val nGrams = getNGrams(values, n)
+ nGrams.foreach(buffer.add(_))
+ buffer.trim()
+ }
+ override def update(buffer: NGramBuffer, inputRow: InternalRow):
NGramBuffer = {
+ if (isArrayOfString) {
+ updateArray(child.eval(inputRow).asInstanceOf[GenericArrayData],
buffer, inputRow)
+ }
+ else {
+ val arrayOfArray =
child.eval(inputRow).asInstanceOf[GenericArrayData]
+ for (i <- 0 until arrayOfArray.numElements()) {
+
updateArray(arrayOfArray.getArray(i).asInstanceOf[GenericArrayData], buffer,
inputRow)
+ }
+ }
+ buffer
+ }
+
+ override def merge(buffer: NGramBuffer, input: NGramBuffer): NGramBuffer
= {
+ buffer.merge(input)
+ buffer.trim()
+ buffer
+ }
+
+ override def eval(buffer: NGramBuffer): Any = {
+ val topKNGrams = buffer.getTopKNGrams().map((keyValuePair:
(Vector[UTF8String], Double)) => {
+ val arrayKey = new GenericArrayData(Vector(new
GenericArrayData(keyValuePair._1)))
+ val arrayValue = new GenericArrayData(Vector(keyValuePair._2))
+ new ArrayBasedMapData(arrayKey, arrayValue)
+ }).toVector
+ new GenericArrayData(topKNGrams)
+ }
+
+ private def getNGrams(values: Vector[UTF8String], n: Int):
Vector[Vector[UTF8String]] = {
+ if (values.length >= n) {
+ values.sliding(n).toVector
+ }
+ else {
+ Vector()
+ }
+ }
+
+ override def withNewMutableAggBufferOffset(newOffset: Int): NGrams =
+ copy(mutableAggBufferOffset = newOffset)
+
+ override def withNewInputAggBufferOffset(newOffset: Int): NGrams =
+ copy(inputAggBufferOffset = newOffset)
+
+ override def children: Seq[Expression] = Seq(child, nExpression,
kExpression, accuracyExpression)
+
+ // Returns null for empty inputs
+ override def nullable: Boolean = true
+
+ override def dataType: DataType =
ArrayType(MapType(ArrayType(StringType), DoubleType))
+
+ override def prettyName: String = "ngrams"
+
+ override def serialize(obj: NGramBuffer): Array[Byte] = {
+ NGrams.serializer.serialize(obj)
+ }
+
+ override def deserialize(bytes: Array[Byte]): NGramBuffer = {
+ NGrams.serializer.deserialize(bytes)
+ }
+}
+
+object NGrams {
+
+ val DEFAULT_ACCURACY: Int = 1000
+
+ private def getAccuracy(kExpression: Expression, accuracyExpression:
Expression): Expression = {
+ val accuracy = accuracyExpression.eval().asInstanceOf[Int]
+ val k = kExpression.eval().asInstanceOf[Int]
+ Literal(accuracy.max(DEFAULT_ACCURACY / k))
+ }
+
+ val kryoSerializer: KryoSerializer = new KryoSerializer(new SparkConf())
+
+ import collection.JavaConverters._
+
+ class NGramBuffer(val n: Int,
+ val k: Int,
+ val precisionFactor: Int,
+ val frequencyMap: HashMap[Vector[UTF8String], Double])
{
+ def add(ng: Vector[UTF8String]): Unit = {
+ var currentFrequency: Double = frequencyMap.get(ng)
+ if (currentFrequency == null.asInstanceOf[Double]) {
+ currentFrequency = 1.0D
+ } else {
+ currentFrequency += 1
+ }
+ frequencyMap.put(ng, currentFrequency)
+ }
+
+ def merge(other: NGramBuffer): Unit = {
+ other.frequencyMap.asScala.foreach((keyValuePair:
(Vector[UTF8String], Double)) => {
+ val key = keyValuePair._1
+ val value = keyValuePair._2
+ val originalValue = frequencyMap.getOrDefault(key, 0.0D)
+ frequencyMap.put(key, originalValue + value)
--- End diff --
The doc says `Note that support for Java 7 is deprecated as of Spark 2.0.0
and may be removed in Spark 2.2.0.`, maybe I changed the code first and wait
for others to comment.
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