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https://issues.apache.org/jira/browse/FLINK-3474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15179677#comment-15179677
]
ASF GitHub Bot commented on FLINK-3474:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/1746#discussion_r55013024
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/runtime/aggregate/AggregateUtil.scala
---
@@ -0,0 +1,329 @@
+/*
+ * 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.flink.api.table.runtime.aggregate
+
+import java.util
+
+import org.apache.calcite.rel.`type`._
+import org.apache.calcite.rel.core.AggregateCall
+import org.apache.calcite.sql.SqlAggFunction
+import org.apache.calcite.sql.`type`.SqlTypeName._
+import org.apache.calcite.sql.`type`.{SqlTypeFactoryImpl, SqlTypeName}
+import org.apache.calcite.sql.fun._
+import org.apache.flink.api.common.functions.{GroupReduceFunction,
MapFunction}
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.table.plan.PlanGenException
+import org.apache.flink.api.table.typeinfo.RowTypeInfo
+import org.apache.flink.api.table.{Row, TableConfig}
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+object AggregateUtil {
+
+ type CalcitePair[T, R] = org.apache.calcite.util.Pair[T, R]
+ type JavaList[T] = java.util.List[T]
+
+ /**
+ * Create Flink operator functions for aggregates. It includes 2
implementations of Flink
+ * operator functions:
+ * [[org.apache.flink.api.common.functions.MapFunction]] and
+ * [[org.apache.flink.api.common.functions.GroupReduceFunction]](if it's
partial aggregate,
+ * should also implement
[[org.apache.flink.api.common.functions.CombineFunction]] as well).
+ * The output of [[org.apache.flink.api.common.functions.MapFunction]]
contains the
+ * intermediate aggregate values of all aggregate function, it's stored
in Row by the following
+ * format:
+ *
+ * {{{
+ * avg(x) aggOffsetInRow = 2 count(z)
aggOffsetInRow = 5
+ * | |
+ * v v
+ * +---------+---------+--------+--------+--------+--------+
+ * |groupKey1|groupKey2| sum1 | count1 | sum2 | count2 |
+ * +---------+---------+--------+--------+--------+--------+
+ * ^
+ * |
+ * sum(y) aggOffsetInRow = 4
+ * }}}
+ *
+ */
+ def createOperatorFunctionsForAggregates(namedAggregates:
Seq[CalcitePair[AggregateCall, String]],
+ inputType: RelDataType, outputType: RelDataType,
+ groupings: Array[Int]): AggregateResult = {
+
+ val aggregateFunctionsAndFieldIndexes =
+ transformToAggregateFunctions(namedAggregates.map(_.getKey),
inputType, groupings.length)
+ // store the aggregate fields of each aggregate function, by the same
order of aggregates.
+ val aggFieldIndexes = aggregateFunctionsAndFieldIndexes._1
+ val aggregates = aggregateFunctionsAndFieldIndexes._2
+
+ val mapFunction = (
+ config: TableConfig,
+ inputType: TypeInformation[Any],
+ returnType: TypeInformation[Any]) => {
+
+ val aggregateMapFunction = new AggregateMapFunction[Row, Row](
+ aggregates, aggFieldIndexes, groupings,
returnType.asInstanceOf[RowTypeInfo])
+
+ aggregateMapFunction.asInstanceOf[MapFunction[Any, Any]]
+ }
+
+ val bufferDataType: RelRecordType =
+ createAggregateBufferDataType(groupings, aggregates, inputType)
+
+ // the mapping relation between field index of intermediate aggregate
Row and output Row.
+ val groupingOffsetMapping = getGroupKeysMapping(inputType, outputType,
groupings)
+
+ // the mapping relation between aggregate function index in list and
its corresponding
+ // field index in output Row.
+ val aggOffsetMapping = getAggregateMapping(namedAggregates, outputType)
+
+ if (groupingOffsetMapping.length != groupings.length ||
+ aggOffsetMapping.length != namedAggregates.length) {
+ throw new PlanGenException("Could not find output field in input
data type " +
+ "or aggregate functions.")
+ }
+
+ val allPartialAggregate = aggregates.map(_.supportPartial).reduce(_ &&
_)
+
+ val intermediateRowArity = groupings.length +
aggregates.map(_.intermediateDataType.length).sum
+
+ val reduceGroupFunction =
+ if (allPartialAggregate) {
+ (config: TableConfig, inputType: TypeInformation[Row], returnType:
TypeInformation[Row]) =>
+ new AggregateReduceCombineFunction(aggregates,
groupingOffsetMapping,
+ aggOffsetMapping, intermediateRowArity)
+ } else {
+ (config: TableConfig, inputType: TypeInformation[Row], returnType:
TypeInformation[Row]) =>
+ new AggregateReduceGroupFunction(aggregates,
groupingOffsetMapping,
+ aggOffsetMapping, intermediateRowArity)
+ }
+
+ new AggregateResult(mapFunction, reduceGroupFunction, bufferDataType)
+ }
+
+ private def transformToAggregateFunctions(
+ aggregateCalls: Seq[AggregateCall],
+ inputType: RelDataType,
+ groupKeysCount: Int): (Array[Int], Array[Aggregate[_ <: Any]]) = {
+
+ // store the aggregate fields of each aggregate function, by the same
order of aggregates.
+ val aggFieldIndexes = new Array[Int](aggregateCalls.size)
+ val aggregates = new Array[Aggregate[_ <: Any]](aggregateCalls.size)
+
+ // set the start offset of aggregate buffer value to group keys'
length,
+ // as all the group keys would be moved to the start fields of
intermediate
+ // aggregate data.
+ var aggOffset = groupKeysCount
+
+ // create aggregate function instances by function type and aggregate
field data type.
+ aggregateCalls.zipWithIndex.foreach { case (aggregateCall, index) =>
+ val argList: util.List[Integer] = aggregateCall.getArgList
+ if (argList.isEmpty) {
+ if
(aggregateCall.getAggregation.isInstanceOf[SqlCountAggFunction]) {
+ aggFieldIndexes(index) = 0
+ } else {
+ throw new PlanGenException("Aggregate fields should not be
empty.")
+ }
+ } else {
+ if (argList.size() > 1) {
+ throw new PlanGenException("Currently, do not support aggregate
on multi fields.")
+ }
+ aggFieldIndexes(index) = argList.get(0)
+ }
+ val sqlTypeName =
inputType.getFieldList.get(aggFieldIndexes(index)).getType.getSqlTypeName
+ aggregateCall.getAggregation match {
+ case _: SqlSumAggFunction | _: SqlSumEmptyIsZeroAggFunction => {
+ sqlTypeName match {
--- End diff --
we can make the code more concise like this:
```
aggregates(index) = sqlTypeName match {
case TINYINT => new ByteSumAggregate
...
}
```
> Partial aggregate interface design and sort-based implementation
> ----------------------------------------------------------------
>
> Key: FLINK-3474
> URL: https://issues.apache.org/jira/browse/FLINK-3474
> Project: Flink
> Issue Type: Sub-task
> Components: Table API
> Reporter: Chengxiang Li
> Assignee: Chengxiang Li
>
> The scope of this sub task includes:
> # Partial aggregate interface.
> # Simple aggregate function implementation, such as SUM/AVG/COUNT/MIN/MAX.
> # DataSetAggregateRule which translate logical calcite aggregate node to
> Flink user functions. As hash-based combiner is not available yet(see PR
> #1517), we would use sort-based combine as default.
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