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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5654) Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
Date Wed, 22 Mar 2017 12:42:41 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15936226#comment-15936226
] 

ASF GitHub Bot commented on FLINK-5654:
---------------------------------------

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

    https://github.com/apache/flink/pull/3590#discussion_r107402759
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/ProcTimeBoundedProcessingOverProcessFunction.scala
---
    @@ -0,0 +1,141 @@
    +/*
    + * 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 org.apache.flink.api.common.state.{ListState, ListStateDescriptor}
    +import org.apache.flink.api.java.typeutils.RowTypeInfo
    +import org.apache.flink.configuration.Configuration
    +import org.apache.flink.runtime.state.{FunctionInitializationContext, FunctionSnapshotContext}
    +import org.apache.flink.streaming.api.checkpoint.CheckpointedFunction
    +import org.apache.flink.streaming.api.functions.ProcessFunction
    +import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
    +import org.apache.flink.types.Row
    +import org.apache.flink.util.{Collector, Preconditions}
    +import org.apache.flink.api.common.state.ValueState
    +import org.apache.flink.api.common.state.ValueStateDescriptor
    +import scala.util.control.Breaks._
    +
    +/**
    +  * Process Function used for the aggregate in partitioned bounded windows in
    +  * [[org.apache.flink.streaming.api.datastream.DataStream]]
    +  *
    +  * @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 Is used to indicate fields in the current element to forward
    +  * @param rowTypeInfo Is used to indicate the field schema
    +  * @param time_boundary Is used to indicate the processing time boundaries
    +  */
    +class ProcTimeBoundedProcessingOverProcessFunction(
    +    private val aggregates: Array[AggregateFunction[_]],
    +    private val aggFields: Array[Int],
    +    private val forwardedFieldCount: Int,
    +    private val rowTypeInfo: RowTypeInfo,
    +    private val time_boundary: Long)
    +  extends ProcessFunction[Row, Row] {
    +
    +  Preconditions.checkNotNull(aggregates)
    +  Preconditions.checkNotNull(aggFields)
    +  Preconditions.checkArgument(aggregates.length == aggFields.length)
    +
    +  private var accumulators: Row = _
    +  private var output: Row = _
    +  private var windowBuffer: ListState[Tuple2[Long,Row]] = null
    +  private var state: ValueState[Row] = _
    +
    +  
    +  override def open(config: Configuration) {
    +    output = new Row(forwardedFieldCount + aggregates.length)
    +    
    +    accumulators = new Row(aggregates.length)
    +    var i = 0
    +    while (i < aggregates.length) {
    +        accumulators.setField(i, aggregates(i).createAccumulator())
    +        i += 1
    +      } 
    +    
    +    // We keep the elements received in a list state 
    +    // together with the ingestion time in the operator
    +    val bufferDescriptor: ListStateDescriptor[Tuple2[Long,Row]] = 
    +    new ListStateDescriptor[Tuple2[Long,Row]]("windowBufferState", classOf[Tuple2[Long,Row]])
    +    windowBuffer = getRuntimeContext.getListState(bufferDescriptor)
    +
    +    val stateDescriptor: ValueStateDescriptor[Row] =
    +    new ValueStateDescriptor[Row]("overState", classOf[Row] , accumulators)      
    +    state = getRuntimeContext.getState(stateDescriptor)
    +  }
    +
    +  override def processElement(
    +    input: Row,
    +    ctx: ProcessFunction[Row, Row]#Context,
    +    out: Collector[Row]): Unit = {
    +
    +    var current_time = System.currentTimeMillis()
    +    //buffer the event incoming event
    +    windowBuffer.add(new Tuple2(
    +      current_time,
    +      input))
    +      
    +    var i = 0
    +
    +    var accumulators = state.value()
    +
    +    //set the fields of the last event to carry on with the aggregates
    +    i = 0
    +    while (i < forwardedFieldCount) {
    +      output.setField(i, input.getField(i))
    +      i += 1
    +    }
    +
    +     //update the elements to be removed and retract them from aggregators
    +    var iter = windowBuffer.get.iterator()
    +    var continue:Boolean = true
    --- End diff --
    
    @fhueske 2 points:
    -If we traverse the whole list of all elements - than what is the difference compared
to having the window as before and going through the whole list to aggregate?...complexity
would be the same...
    -There is no need to go through the whole list. The list is sorted based on processing
time (i.e. based on incoming order of events). As we bound things based on time it means that
we only need to go through the oldest elements until we find one which is still within the
scope of the window. When we find this we can stop the search as we ensured we have only the
right elements remaining in the buffer.
    Considering that the reason to switch from window to process function was to reduce the
number of operations - i would say we need to keep this...otherwise it is basically the same
and in the case i would ask you to merge the window implementation


> Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ---------------------------------------------------------------------
>
>                 Key: FLINK-5654
>                 URL: https://issues.apache.org/jira/browse/FLINK-5654
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: radu
>
> 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 INTERVAL '1' HOUR PRECEDING
AND CURRENT ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1' HOUR 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-5657)
> - 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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