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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5803) Add [partitioned] processing time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
Date Tue, 07 Mar 2017 11:21:38 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5803?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15899274#comment-15899274
] 

ASF GitHub Bot commented on FLINK-5803:
---------------------------------------

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`.


> Add [partitioned] processing time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation
to SQL
> -------------------------------------------------------------------------------------------
>
>                 Key: FLINK-5803
>                 URL: https://issues.apache.org/jira/browse/FLINK-5803
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: sunjincheng
>            Assignee: sunjincheng
>
> The goal of this issue is to add support for OVER RANGE aggregations on processing time
streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT 
>   a, 
>   SUM(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN UNBOUNDED PRECEDING AND
CURRENT ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN UNBOUNDED PRECEDING AND
CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a parameterless
scalar function that just indicates processing time mode.
> - bounded PRECEDING is not supported (see FLINK-5654)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some of the restrictions
are trivial to address, we can add the functionality in this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with RexOver expression).



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