zhidongqu-db commented on code in PR #54011:
URL: https://github.com/apache/spark/pull/54011#discussion_r2743250956
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/vectorExpressions.scala:
##########
@@ -328,3 +344,469 @@ case class VectorNormalize(vector: Expression, degree:
Expression)
copy(vector = newChildren(0), degree = newChildren(1))
}
}
+
+// scalastyle:off line.size.limit
+@ExpressionDescription(
+ usage = """
+ _FUNC_(array) - Returns the element-wise mean of float vectors in a group.
+ All vectors must have the same dimension.
+ """,
+ examples = """
+ Examples:
+ > SELECT _FUNC_(col) FROM VALUES (array(1.0F, 2.0F)), (array(3.0F,
4.0F)) AS tab(col);
+ [2.0,3.0]
+ """,
+ since = "4.2.0",
+ group = "vector_funcs"
+)
+// scalastyle:on line.size.limit
+case class VectorAvg(
+ child: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0
+) extends ImperativeAggregate
+ with UnaryLike[Expression]
+ with QueryErrorsBase {
+
+ def this(child: Expression) = this(child, 0, 0)
+
+ override def prettyName: String = "vector_avg"
+
+ override def nullable: Boolean = true
+
+ override def dataType: DataType = ArrayType(FloatType, containsNull = false)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ child.dataType match {
+ case ArrayType(FloatType, _) =>
+ TypeCheckResult.TypeCheckSuccess
+ case _ =>
+ DataTypeMismatch(
+ errorSubClass = "UNEXPECTED_INPUT_TYPE",
+ messageParameters = Map(
+ "paramIndex" -> ordinalNumber(0),
+ "requiredType" -> toSQLType(ArrayType(FloatType)),
+ "inputSql" -> toSQLExpr(child),
+ "inputType" -> toSQLType(child.dataType)
+ )
+ )
+ }
+ }
+
+ // Aggregate buffer schema: (avg: BINARY, dim: INTEGER, count: LONG)
+ // avg is a BINARY representation of the average vector of floats in the
group
+ // dim is the dimension of the vector
+ // count is the number of vectors in the group
+ // null avg means no valid input has been seen yet
+ private lazy val avgAttr = AttributeReference(
+ "avg",
+ BinaryType,
+ nullable = true
+ )()
+ private lazy val dimAttr = AttributeReference(
+ "dim",
+ IntegerType,
+ nullable = true
+ )()
+ private lazy val countAttr =
+ AttributeReference("count", LongType, nullable = false)()
+
+ override def aggBufferSchema: StructType = StructType(
Review Comment:
done
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/vectorExpressions.scala:
##########
@@ -328,3 +344,469 @@ case class VectorNormalize(vector: Expression, degree:
Expression)
copy(vector = newChildren(0), degree = newChildren(1))
}
}
+
+// scalastyle:off line.size.limit
+@ExpressionDescription(
+ usage = """
+ _FUNC_(array) - Returns the element-wise mean of float vectors in a group.
+ All vectors must have the same dimension.
+ """,
+ examples = """
+ Examples:
+ > SELECT _FUNC_(col) FROM VALUES (array(1.0F, 2.0F)), (array(3.0F,
4.0F)) AS tab(col);
+ [2.0,3.0]
+ """,
+ since = "4.2.0",
+ group = "vector_funcs"
+)
+// scalastyle:on line.size.limit
+case class VectorAvg(
+ child: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0
+) extends ImperativeAggregate
+ with UnaryLike[Expression]
+ with QueryErrorsBase {
+
+ def this(child: Expression) = this(child, 0, 0)
+
+ override def prettyName: String = "vector_avg"
+
+ override def nullable: Boolean = true
+
+ override def dataType: DataType = ArrayType(FloatType, containsNull = false)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ child.dataType match {
+ case ArrayType(FloatType, _) =>
+ TypeCheckResult.TypeCheckSuccess
+ case _ =>
+ DataTypeMismatch(
+ errorSubClass = "UNEXPECTED_INPUT_TYPE",
+ messageParameters = Map(
+ "paramIndex" -> ordinalNumber(0),
+ "requiredType" -> toSQLType(ArrayType(FloatType)),
+ "inputSql" -> toSQLExpr(child),
+ "inputType" -> toSQLType(child.dataType)
+ )
+ )
+ }
+ }
+
+ // Aggregate buffer schema: (avg: BINARY, dim: INTEGER, count: LONG)
+ // avg is a BINARY representation of the average vector of floats in the
group
+ // dim is the dimension of the vector
+ // count is the number of vectors in the group
+ // null avg means no valid input has been seen yet
+ private lazy val avgAttr = AttributeReference(
+ "avg",
+ BinaryType,
+ nullable = true
+ )()
+ private lazy val dimAttr = AttributeReference(
+ "dim",
+ IntegerType,
+ nullable = true
+ )()
+ private lazy val countAttr =
+ AttributeReference("count", LongType, nullable = false)()
+
+ override def aggBufferSchema: StructType = StructType(
+ Seq(
+ StructField(
+ "avg",
+ BinaryType,
+ nullable = true
+ ),
+ StructField("dim", IntegerType, nullable = true),
+ StructField("count", LongType, nullable = false)
+ )
+ )
+
+ override def aggBufferAttributes: Seq[AttributeReference] =
+ Seq(avgAttr, dimAttr, countAttr)
+
+ override lazy val inputAggBufferAttributes: Seq[AttributeReference] =
+ aggBufferAttributes.map(_.newInstance())
+
+ // Buffer indices
+ private val avgIndex = 0
+ private val dimIndex = 1
+ private val countIndex = 2
+
+ override def withNewMutableAggBufferOffset(
+ newMutableAggBufferOffset: Int
+ ): ImperativeAggregate =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+ override def withNewInputAggBufferOffset(
+ newInputAggBufferOffset: Int
+ ): ImperativeAggregate =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ override def initialize(buffer: InternalRow): Unit = {
+ buffer.update(mutableAggBufferOffset + avgIndex, null)
+ buffer.update(mutableAggBufferOffset + dimIndex, null)
+ buffer.setLong(mutableAggBufferOffset + countIndex, 0L)
+ }
+
+ override def update(buffer: InternalRow, input: InternalRow): Unit = {
+ val inputValue = child.eval(input)
+ if (inputValue == null) {
+ return
+ }
+
+ val inputArray = inputValue.asInstanceOf[ArrayData]
+ val inputLen = inputArray.numElements()
+
+ // Check for NULL elements in input vector - skip if any NULL element found
+ for (i <- 0 until inputLen) {
+ if (inputArray.isNullAt(i)) {
+ return
+ }
+ }
+
+ val currentCount = buffer.getLong(mutableAggBufferOffset + countIndex)
+
+ if (currentCount == 0L) {
+ // First valid vector - just copy it as the initial average
+ val byteBuffer =
+ ByteBuffer.allocate(inputLen * 4).order(ByteOrder.LITTLE_ENDIAN)
+ for (i <- 0 until inputLen) {
+ byteBuffer.putFloat(inputArray.getFloat(i))
+ }
+ buffer.update(mutableAggBufferOffset + avgIndex, byteBuffer.array())
+ buffer.setInt(mutableAggBufferOffset + dimIndex, inputLen)
+ buffer.setLong(mutableAggBufferOffset + countIndex, 1L)
+ } else {
+ val currentDim = buffer.getInt(mutableAggBufferOffset + dimIndex)
+
+ // Empty array case - if current is empty and input is empty, keep empty
+ if (currentDim == 0 && inputLen == 0) {
+ buffer.setLong(mutableAggBufferOffset + countIndex, currentCount + 1L)
+ return
+ }
+
+ // Dimension mismatch check
+ if (currentDim != inputLen) {
+ throw QueryExecutionErrors.vectorDimensionMismatchError(
+ prettyName,
+ currentDim,
+ inputLen
+ )
+ }
+
+ // Update running average: new_avg = old_avg + (new_value - old_avg) /
(count + 1)
+ val newCount = currentCount + 1L
+ val currentAvgBytes = buffer.getBinary(mutableAggBufferOffset + avgIndex)
+ val currentAvgBuffer =
+ ByteBuffer.wrap(currentAvgBytes).order(ByteOrder.LITTLE_ENDIAN)
+ val newAvgBuffer =
+ ByteBuffer.allocate(currentDim * 4).order(ByteOrder.LITTLE_ENDIAN)
+ for (i <- 0 until currentDim) {
+ val oldAvg = currentAvgBuffer.getFloat()
+ val newVal = inputArray.getFloat(i)
+ newAvgBuffer.putFloat(oldAvg + ((newVal - oldAvg) / newCount.toFloat))
+ }
+ buffer.update(mutableAggBufferOffset + avgIndex, newAvgBuffer.array())
+ buffer.setLong(mutableAggBufferOffset + countIndex, newCount)
+ }
+ }
+
+ override def merge(buffer: InternalRow, inputBuffer: InternalRow): Unit = {
+ val inputCount = inputBuffer.getLong(inputAggBufferOffset + countIndex)
+ if (inputCount == 0L) {
+ return
+ }
+
+ val inputAvgBytes = inputBuffer.getBinary(inputAggBufferOffset + avgIndex)
+ val inputDim = inputBuffer.getInt(inputAggBufferOffset + dimIndex)
+ val currentCount = buffer.getLong(mutableAggBufferOffset + countIndex)
+
+ if (currentCount == 0L) {
+ // Copy input buffer to current buffer
+ buffer.update(mutableAggBufferOffset + avgIndex, inputAvgBytes.clone())
+ buffer.setInt(mutableAggBufferOffset + dimIndex, inputDim)
+ buffer.setLong(mutableAggBufferOffset + countIndex, inputCount)
+ } else {
+ val currentDim = buffer.getInt(mutableAggBufferOffset + dimIndex)
+
+ // Empty array case
+ if (currentDim == 0 && inputDim == 0) {
+ buffer.setLong(
+ mutableAggBufferOffset + countIndex,
+ currentCount + inputCount
+ )
+ return
+ }
+
+ // Dimension mismatch check
+ if (currentDim != inputDim) {
+ throw QueryExecutionErrors.vectorDimensionMismatchError(
+ prettyName,
+ currentDim,
+ inputDim
+ )
+ }
+
+ // Merge running averages:
+ // combined_avg = (left_avg * left_count) / (left_count + right_count) +
+ // (right_avg * right_count) / (left_count + right_count)
+ val newCount = currentCount + inputCount
+ val currentAvgBytes = buffer.getBinary(mutableAggBufferOffset + avgIndex)
+ val currentAvgBuffer =
+ ByteBuffer.wrap(currentAvgBytes).order(ByteOrder.LITTLE_ENDIAN)
+ val inputAvgBuffer =
+ ByteBuffer.wrap(inputAvgBytes).order(ByteOrder.LITTLE_ENDIAN)
+ val newAvgBuffer =
+ ByteBuffer.allocate(currentDim * 4).order(ByteOrder.LITTLE_ENDIAN)
+ for (_ <- 0 until currentDim) {
+ // getFloat() will auto-increment the buffer's current position by 4
+ val leftAvg = currentAvgBuffer.getFloat()
+ val rightAvg = inputAvgBuffer.getFloat()
+ newAvgBuffer.putFloat(
+ (leftAvg * currentCount) / newCount.toFloat +
+ (rightAvg * inputCount) / newCount.toFloat
+ )
+ }
+ buffer.update(mutableAggBufferOffset + avgIndex, newAvgBuffer.array())
+ buffer.setLong(mutableAggBufferOffset + countIndex, newCount)
+ }
+ }
+
+ override def eval(buffer: InternalRow): Any = {
+ val count = buffer.getLong(mutableAggBufferOffset + countIndex)
+ if (count == 0L) {
+ null
+ } else {
+ val dim = buffer.getInt(mutableAggBufferOffset + dimIndex)
+ val avgBytes = buffer.getBinary(mutableAggBufferOffset + avgIndex)
+ val avgBuffer = ByteBuffer.wrap(avgBytes).order(ByteOrder.LITTLE_ENDIAN)
+ val result = new Array[Float](dim)
+ for (i <- 0 until dim) {
+ result(i) = avgBuffer.getFloat()
+ }
+ ArrayData.toArrayData(result)
+ }
+ }
+
+ override protected def withNewChildInternal(newChild: Expression): VectorAvg
=
+ copy(child = newChild)
+}
+
+// scalastyle:off line.size.limit
+@ExpressionDescription(
+ usage = """
+ _FUNC_(array) - Returns the element-wise sum of float vectors in a group.
+ All vectors must have the same dimension.
+ """,
+ examples = """
+ Examples:
+ > SELECT _FUNC_(col) FROM VALUES (array(1.0F, 2.0F)), (array(3.0F,
4.0F)) AS tab(col);
+ [4.0,6.0]
+ """,
+ since = "4.2.0",
+ group = "vector_funcs"
+)
+// scalastyle:on line.size.limit
+case class VectorSum(
+ child: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0
+) extends ImperativeAggregate
+ with UnaryLike[Expression]
+ with QueryErrorsBase {
+
+ def this(child: Expression) = this(child, 0, 0)
+
+ override def prettyName: String = "vector_sum"
+
+ override def nullable: Boolean = true
+
+ override def dataType: DataType = ArrayType(FloatType, containsNull = false)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ child.dataType match {
+ case ArrayType(FloatType, _) =>
+ TypeCheckResult.TypeCheckSuccess
+ case _ =>
+ DataTypeMismatch(
+ errorSubClass = "UNEXPECTED_INPUT_TYPE",
+ messageParameters = Map(
+ "paramIndex" -> ordinalNumber(0),
+ "requiredType" -> toSQLType(ArrayType(FloatType)),
+ "inputSql" -> toSQLExpr(child),
+ "inputType" -> toSQLType(child.dataType)
+ )
+ )
+ }
+ }
+
+ // Aggregate buffer schema: (sum: BINARY, dim: INTEGER)
+ // sum is a BINARY representation of the sum vector of floats in the group
+ // dim is the dimension of the vector
+ // null sum means no valid input has been seen yet
+ private lazy val sumAttr = AttributeReference(
+ "sum",
+ BinaryType,
+ nullable = true
+ )()
+ private lazy val dimAttr = AttributeReference(
+ "dim",
+ IntegerType,
+ nullable = true
+ )()
+
+ override def aggBufferSchema: StructType = StructType(
Review Comment:
done
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