[GitHub] [spark] AngersZhuuuu commented on a change in pull request #34380: [SPARK-37082][SQL] Implements histogram_numeric aggregation function which supports partial aggregation.

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AngersZhuuuu commented on a change in pull request #34380:
URL: https://github.com/apache/spark/pull/34380#discussion_r736340592



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File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/HistogramNumeric.scala
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@@ -0,0 +1,167 @@
+/*
+ * 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 scala.collection.JavaConverters._
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription, ImplicitCastInputTypes}
+import org.apache.spark.sql.catalyst.trees.BinaryLike
+import org.apache.spark.sql.catalyst.util.{DistributedHistogramSerializer, DistributeHistogram, GenericArrayData}
+import org.apache.spark.sql.errors.QueryExecutionErrors
+import org.apache.spark.sql.types.{AbstractDataType, ArrayType, DataType, DateType, DoubleType, IntegerType, NumericType, StructField, StructType, TimestampNTZType, TimestampType, TypeCollection}
+
+/**
+ * Computes an approximate histogram of a numerical column using a user-specified number of bins.
+ *
+ * The output is an array of (x,y) pairs as struct objects that represents the histogram's
+ * bin centers and heights.
+ */
+@ExpressionDescription(
+  usage = """
+    _FUNC_(expr, nb) - Computes a histogram on numeric 'expr' using nb bins.
+      The return value is an array of (x,y) pairs representing the centers of the
+      histogram's bins. As the value of 'nb' is increased, the histogram approximation
+      gets finer-grained, but may yield artifacts around outliers. In practice, 20-40
+      histogram bins appear to work well, with more bins being required for skewed or
+      smaller datasets. Note that this function creates a histogram with non-uniform
+      bin widths. It offers no guarantees in terms of the mean-squared-error of the
+      histogram, but in practice is comparable to the histograms produced by the R/S-Plus
+      statistical computing packages.
+    """,
+  examples = """
+    Examples:
+      > SELECT _FUNC_(col, 5) FROM VALUES (0), (1), (2), (10) AS tab(col);
+       [{"x":0.0,"y":1.0},{"x":1.0,"y":1.0},{"x":2.0,"y":1.0},{"x":10.0,"y":1.0}]
+  """,
+  group = "agg_funcs",
+  since = "3.3.0")
+case class HistogramNumeric(
+    child: Expression,
+    nBins: Expression,
+    override val mutableAggBufferOffset: Int,
+    override val inputAggBufferOffset: Int)
+  extends TypedImperativeAggregate[DistributeHistogram] with ImplicitCastInputTypes
+  with BinaryLike[Expression] {
+
+  def this(child: Expression, nBins: Expression) = {
+    this(child, nBins, 0, 0)
+  }
+
+  private lazy val nb = nBins.eval() match {
+    case null => null
+    case n: Int => n
+  }
+
+  override def inputTypes: Seq[AbstractDataType] = {
+    // Support NumericType, DateType, TimestampType and TimestampNTZType since their internal types
+    // are all numeric, and can be easily cast to double for processing.

Review comment:
       Done




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