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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5653) Add processing time OVER ROWS BETWEEN x PRECEDING aggregation to SQL
Date Mon, 20 Mar 2017 20:15:42 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5653?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15933449#comment-15933449
] 

ASF GitHub Bot commented on FLINK-5653:
---------------------------------------

Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3574#discussion_r106966705
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateAggOverFunction.scala
---
    @@ -0,0 +1,102 @@
    +/*
    + * 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.runtime.aggregate
    +
    +import java.util.{ArrayList => JArrayList, List => JList}
    +import org.apache.flink.api.common.functions.{AggregateFunction => DataStreamAggOverFunc}
    +import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
    +import org.apache.flink.types.Row
    +import org.apache.flink.util.Preconditions
    +
    +/**
    +  * Aggregate Function used for the aggregate operator in
    +  * [[org.apache.flink.streaming.api.datastream.WindowedStream]] 
    +  *
    +  * @param aggregates       the list of all [[org.apache.flink.table.functions.AggregateFunction]]
    +  *                         used for this aggregation
    +  * @param aggFields   the position (in the input Row) of the input value for each aggregate
    +  * @param forwardedFieldCount   the number of elements in the Row to kept after the
aggregation
    +  */
    +class AggregateAggOverFunction(
    +    private val aggregates: Array[AggregateFunction[_]],
    +    private val aggFields: Array[Int],
    +    private val forwardedFieldCount: Int)
    +  extends DataStreamAggOverFunc[Row, Row, Row] {
    +
    +  Preconditions.checkNotNull(aggregates)
    +  Preconditions.checkNotNull(aggFields)
    +  Preconditions.checkArgument(aggregates.length == aggFields.length)
    +  
    +  private var lastVal:Row = _
    +  
    +  override def createAccumulator(): Row = {
    +    val accumulatorRow: Row = new Row(aggregates.length)
    +    var i = 0
    +    while (i < aggregates.length) {
    +      accumulatorRow.setField(i, aggregates(i).createAccumulator())
    +      i += 1
    +    }
    +    accumulatorRow
    +  }
    +
    +  override def add(value: Row, accumulatorRow: Row): Unit = {
    +    var i = 0
    +    while (i < aggregates.length) {
    +      val acc = accumulatorRow.getField(i).asInstanceOf[Accumulator]
    +      val v = value.getField(aggFields(i))
    +      aggregates(i).accumulate(acc, v)
    +      i += 1
    +    }
    +    lastVal = value;
    +  }
    +
    +  override def getResult(accumulatorRow: Row): Row = {
    +    val output = new Row(forwardedFieldCount + aggFields.length)
    +
    +    var i = 0
    +    // set the output value of forward fields
    +    while (i < forwardedFieldCount) {
    +      output.setField(i, lastVal.getField(i))
    +      i += 1
    +    }
    +    
    +    i = 0
    +    while (i < aggregates.length) {
    +      val acc = accumulatorRow.getField(i).asInstanceOf[Accumulator]
    +      output.setField(forwardedFieldCount + i, aggregates(i).getValue(acc))
    +      i += 1
    +    }
    +    output
    +  }
    +
    +  override def merge(aAccumulatorRow: Row, bAccumulatorRow: Row): Row = {
    +
    +    var i = 0
    +    while (i < aggregates.length) {
    --- End diff --
    
    I'm sorry, I just realized that we cannot use the `AggregateFunction` interface. 
    We would need to merge the forwarded fields as well. Since we do not know which accumulator
holds the forwarded fields of the last row, it is not possible to use this interface. I'm
sorry that I led you into this direction. :-(
    



> Add processing time OVER ROWS BETWEEN x PRECEDING aggregation to SQL
> --------------------------------------------------------------------
>
>                 Key: FLINK-5653
>                 URL: https://issues.apache.org/jira/browse/FLINK-5653
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: Stefano Bortoli
>
> The goal of this issue is to add support for OVER ROWS 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() ROWS BETWEEN 2 PRECEDING AND CURRENT
ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY procTime() ROWS BETWEEN 2 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 PARTITION BY clause is optional (no partitioning results in single threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a parameterless
scalar function that just indicates processing time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5656)
> - 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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