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From fhueske <...@git.apache.org>
Subject [GitHub] flink pull request #3397: [FLINK-5803][TableAPI&SQL] Add [partitioned] proce...
Date Tue, 07 Mar 2017 11:21:20 GMT
Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3397#discussion_r104642668
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
---
    @@ -0,0 +1,199 @@
    +/*
    + * 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.table.plan.nodes.datastream
    +
    +import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
    +import org.apache.calcite.rel.`type`.RelDataType
    +import org.apache.calcite.rel.core.AggregateCall
    +import org.apache.calcite.rel.{RelNode, RelWriter, SingleRel}
    +import org.apache.flink.api.java.typeutils.RowTypeInfo
    +import org.apache.flink.streaming.api.datastream.DataStream
    +import org.apache.flink.table.api.{StreamTableEnvironment, TableException}
    +import org.apache.flink.table.calcite.FlinkTypeFactory
    +import org.apache.flink.table.runtime.aggregate.AggregateUtil.{CalcitePair, _}
    +import org.apache.flink.table.runtime.aggregate._
    +import org.apache.flink.table.plan.nodes.OverAggregate
    +import org.apache.flink.types.Row
    +import org.apache.calcite.rel.core.Window
    +import org.apache.calcite.rel.core.Window.Group
    +import java.util.{List => JList}
    +
    +import org.apache.flink.table.functions.{ProcTimeType, RowTimeType}
    +
    +import scala.collection.JavaConverters._
    +import scala.collection.immutable.IndexedSeq
    +
    +class DataStreamOverAggregate(
    +    logicWindow: Window,
    +    cluster: RelOptCluster,
    +    traitSet: RelTraitSet,
    +    inputNode: RelNode,
    +    rowRelDataType: RelDataType,
    +    inputType: RelDataType)
    +  extends SingleRel(cluster, traitSet, inputNode)
    +  with OverAggregate
    +  with DataStreamRel {
    +
    +  override def deriveRowType(): RelDataType = rowRelDataType
    +
    +  override def copy(traitSet: RelTraitSet, inputs: JList[RelNode]): RelNode = {
    +    new DataStreamOverAggregate(
    +      logicWindow,
    +      cluster,
    +      traitSet,
    +      inputs.get(0),
    +      getRowType,
    +      inputType)
    +  }
    +
    +  override def toString: String = {
    +    s"OverAggregate(${aggOpName})"
    +  }
    +
    +  override def explainTerms(pw: RelWriter): RelWriter = {
    +    val (
    +      overWindow: Group,
    +      partition: Array[Int],
    +      namedAggregates: IndexedSeq[CalcitePair[AggregateCall, String]]
    +      ) = genPartitionKeysAndNamedAggregates
    +
    +    super.explainTerms(pw)
    +      .itemIf("partitionBy", partitionToString(inputType, partition), partition.nonEmpty)
    +        .item("orderBy",orderingToString(inputType, overWindow.orderKeys.getFieldCollations))
    +      .item("range", windowRange(overWindow))
    +      .item(
    +        "select", aggregationToString(
    +          inputType,
    +          getRowType,
    +          namedAggregates))
    +  }
    +
    +  override def translateToPlan(tableEnv: StreamTableEnvironment): DataStream[Row] = {
    +    if (logicWindow.groups.size > 1) {
    +      throw new TableException(
    +        "Unsupported use of OVER windows. All aggregates must be computed on the same
window.")
    +    }
    +
    +    val overWindow: org.apache.calcite.rel.core.Window.Group = logicWindow.groups.get(0)
    +
    +    val inputDS = input.asInstanceOf[DataStreamRel].translateToPlan(tableEnv)
    +
    +    if (overWindow.orderKeys.getFieldCollations.size() != 1) {
    +      throw new TableException(
    +        "Unsupported use of OVER windows. The window may only be ordered by a single
time column.")
    +    }
    +
    +    val timeType = inputType
    +      .getFieldList
    +      .get(overWindow.orderKeys.getFieldCollations.get(0).getFieldIndex)
    +      .getValue
    +
    +    timeType match {
    +      case _: ProcTimeType =>
    +        // both ROWS and RANGE clause with UNBOUNDED PRECEDING and CURRENT ROW condition.
    +        if (overWindow.lowerBound.isUnbounded &&
    +          overWindow.upperBound.isCurrentRow) {
    +          createUnboundedAndCurrentRowProcessingTimeOverWindow(inputDS)
    +        } else {
    +          throw new TableException(
    +              "OVER window only support ProcessingTime UNBOUNDED PRECEDING and CURRENT
ROW " +
    +              "condition.")
    +        }
    +      case _: RowTimeType =>
    +        throw new TableException("OVER Window of the EventTime type is not currently
supported.")
    +      case _ =>
    +        throw new TableException(s"Unsupported time type {$timeType}")
    +    }
    +
    +  }
    +
    +  def createUnboundedAndCurrentRowProcessingTimeOverWindow(
    +    inputDS: DataStream[Row]): DataStream[Row]  = {
    +
    +    val (_,
    +      partition: Array[Int],
    +      namedAggregates: IndexedSeq[CalcitePair[AggregateCall, String]]
    +      ) = genPartitionKeysAndNamedAggregates
    --- End diff --
    
    change `genPartitionKeysAndNamedAggregates()` to only generate named aggregates and rename.
    Functions that do only one thing are easier to understand and partition keys can be generated
in one line: `overWindow.keys.toArray`.


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