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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-6233) Support rowtime inner equi-join between two streams in the SQL API
Date Tue, 19 Sep 2017 15:12:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-6233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16171860#comment-16171860
] 

ASF GitHub Bot commented on FLINK-6233:
---------------------------------------

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

    https://github.com/apache/flink/pull/4625#discussion_r139723011
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/join/TimeBoundedStreamInnerJoin.scala
---
    @@ -131,340 +116,308 @@ class TimeBoundedStreamInnerJoin(
         // Initialize the data caches.
         val leftListTypeInfo: TypeInformation[JList[Row]] = new ListTypeInfo[Row](leftType)
         val leftStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
    -      new MapStateDescriptor[Long, JList[Row]](timeIndicator + "InnerJoinLeftCache",
    -        BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]], leftListTypeInfo)
    +      new MapStateDescriptor[Long, JList[Row]](
    +        timeIndicator + "InnerJoinLeftCache",
    +        BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
    +        leftListTypeInfo)
         leftCache = getRuntimeContext.getMapState(leftStateDescriptor)
     
         val rightListTypeInfo: TypeInformation[JList[Row]] = new ListTypeInfo[Row](rightType)
         val rightStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
    -      new MapStateDescriptor[Long, JList[Row]](timeIndicator + "InnerJoinRightCache",
    -        BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]], rightListTypeInfo)
    +      new MapStateDescriptor[Long, JList[Row]](
    +        timeIndicator + "InnerJoinRightCache",
    +        BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
    +        rightListTypeInfo)
         rightCache = getRuntimeContext.getMapState(rightStateDescriptor)
     
         // Initialize the timer states.
         val leftTimerStateDesc: ValueStateDescriptor[Long] =
    -      new ValueStateDescriptor[Long](timeIndicator + "InnerJoinLeftTimerState",
    -        classOf[Long])
    +      new ValueStateDescriptor[Long](timeIndicator + "InnerJoinLeftTimerState", classOf[Long])
         leftTimerState = getRuntimeContext.getState(leftTimerStateDesc)
     
         val rightTimerStateDesc: ValueStateDescriptor[Long] =
    -      new ValueStateDescriptor[Long](timeIndicator + "InnerJoinRightTimerState",
    -        classOf[Long])
    +      new ValueStateDescriptor[Long](timeIndicator + "InnerJoinRightTimerState", classOf[Long])
         rightTimerState = getRuntimeContext.getState(rightTimerStateDesc)
       }
     
       /**
    -    * Process records from the left stream.
    -    *
    -    * @param cRowValue the input record
    -    * @param ctx       the context to register timer or get current time
    -    * @param out       the collector for outputting results
    -    *
    +    * Process rows from the left stream.
         */
       override def processElement1(
    -    cRowValue: CRow,
    -    ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    -    out: Collector[CRow]): Unit = {
    -    val timeForRecord: Long = getTimeForRecord(ctx, cRowValue, true)
    -    getCurrentOperatorTime(ctx)
    +      cRowValue: CRow,
    +      ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    +      out: Collector[CRow]): Unit = {
    +    updateOperatorTime(ctx)
    +    val rowTime: Long = getTimeForLeftStream(ctx, cRowValue)
    +    val oppositeLowerBound: Long = rowTime - rightRelativeSize
    +    val oppositeUpperBound: Long = rowTime + leftRelativeSize
         processElement(
           cRowValue,
    -      timeForRecord,
    +      rowTime,
           ctx,
           out,
           leftOperatorTime,
    +      oppositeLowerBound,
    +      oppositeUpperBound,
           rightOperatorTime,
           rightTimerState,
           leftCache,
           rightCache,
    -      true
    +      leftRow = true
         )
       }
     
       /**
    -    * Process records from the right stream.
    -    *
    -    * @param cRowValue the input record
    -    * @param ctx       the context to get current time
    -    * @param out       the collector for outputting results
    -    *
    +    * Process rows from the right stream.
         */
       override def processElement2(
    -    cRowValue: CRow,
    -    ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    -    out: Collector[CRow]): Unit = {
    -    val timeForRecord: Long = getTimeForRecord(ctx, cRowValue, false)
    -    getCurrentOperatorTime(ctx)
    +      cRowValue: CRow,
    +      ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    +      out: Collector[CRow]): Unit = {
    +    updateOperatorTime(ctx)
    +    val rowTime: Long = getTimeForRightStream(ctx, cRowValue)
    +    val oppositeLowerBound: Long = rowTime - leftRelativeSize
    +    val oppositeUpperBound: Long =  rowTime + rightRelativeSize
         processElement(
           cRowValue,
    -      timeForRecord,
    +      rowTime,
           ctx,
           out,
           rightOperatorTime,
    +      oppositeLowerBound,
    +      oppositeUpperBound,
           leftOperatorTime,
           leftTimerState,
           rightCache,
           leftCache,
    -      false
    +      leftRow = false
         )
       }
     
       /**
    -    * Put a record from the input stream into the cache and iterate the opposite cache
to
    -    * output records meeting the join conditions. If there is no timer set for the OPPOSITE
    +    * Put a row from the input stream into the cache and iterate the opposite cache to
    +    * output join results meeting the conditions. If there is no timer set for the OPPOSITE
         * STREAM, register one.
         */
       private def processElement(
    -    cRowValue: CRow,
    -    timeForRecord: Long,
    -    ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    -    out: Collector[CRow],
    -    myWatermark: Long,
    -    oppositeWatermark: Long,
    -    oppositeTimeState: ValueState[Long],
    -    recordListCache: MapState[Long, JList[Row]],
    -    oppositeCache: MapState[Long, JList[Row]],
    -    leftRecord: Boolean): Unit = {
    -    if (relativeWindowSize > 0) {
    -      //TODO Shall we consider adding a method for initialization with the context and
collector?
    -      cRowWrapper.out = out
    -
    -      val record = cRowValue.row
    -
    -      //TODO Only if the time of the record is greater than the watermark, can we continue.
    -      if (timeForRecord >= myWatermark - allowedLateness) {
    -        val oppositeLowerBound: Long =
    -          if (leftRecord) timeForRecord - rightRelativeSize else timeForRecord - leftRelativeSize
    -
    -        val oppositeUpperBound: Long =
    -          if (leftRecord) timeForRecord + leftRelativeSize else timeForRecord + rightRelativeSize
    -
    -        // Put the record into the cache for later use.
    -        val recordList = if (recordListCache.contains(timeForRecord)) {
    -          recordListCache.get(timeForRecord)
    -        } else {
    -          new util.ArrayList[Row]()
    -        }
    -        recordList.add(record)
    -        recordListCache.put(timeForRecord, recordList)
    -
    -        // Register a timer on THE OTHER STREAM to remove records from the cache once
they are
    -        // expired.
    -        if (oppositeTimeState.value == 0) {
    -          registerCleanUpTimer(
    -            ctx, timeForRecord, oppositeWatermark, oppositeTimeState, leftRecord, true)
    -        }
    +      cRowValue: CRow,
    +      timeForRow: Long,
    +      ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
    +      out: Collector[CRow],
    +      myWatermark: Long,
    +      oppositeLowerBound: Long,
    +      oppositeUpperBound: Long,
    +      oppositeWatermark: Long,
    +      oppositeTimeState: ValueState[Long],
    +      rowListCache: MapState[Long, JList[Row]],
    +      oppositeCache: MapState[Long, JList[Row]],
    +      leftRow: Boolean): Unit = {
    +    cRowWrapper.out = out
    +    val row = cRowValue.row
    +    if (!checkRowOutOfDate(timeForRow, myWatermark)) {
    +      // Put the row into the cache for later use.
    +      var rowList = rowListCache.get(timeForRow)
    +      if (null == rowList) {
    +        rowList = new ArrayList[Row](1)
    +      }
    +      rowList.add(row)
    +      rowListCache.put(timeForRow, rowList)
    +      // Register a timer on THE OPPOSITE STREAM to remove rows from the cache once they
are
    +      // expired.
    +      if (oppositeTimeState.value == 0) {
    +        registerCleanUpTimer(
    +          ctx, timeForRow, oppositeWatermark, oppositeTimeState, leftRow, firstTimer
= true)
    +      }
     
    -        // Join the record with records from the opposite stream.
    -        val oppositeIterator = oppositeCache.iterator()
    -        var oppositeEntry: Entry[Long, util.List[Row]] = null
    -        var oppositeTime: Long = 0L;
    -        while (oppositeIterator.hasNext) {
    -          oppositeEntry = oppositeIterator.next
    -          oppositeTime = oppositeEntry.getKey
    -          if (oppositeTime < oppositeLowerBound - allowedLateness) {
    -            //TODO Considering the data out-of-order, we should not remove records here.
    -          } else if (oppositeTime >= oppositeLowerBound && oppositeTime <=
oppositeUpperBound) {
    -            val oppositeRows = oppositeEntry.getValue
    -            var i = 0
    -            if (leftRecord) {
    -              while (i < oppositeRows.size) {
    -                joinFunction.join(record, oppositeRows.get(i), cRowWrapper)
    -                i += 1
    -              }
    -            } else {
    -              while (i < oppositeRows.size) {
    -                joinFunction.join(oppositeRows.get(i), record, cRowWrapper)
    -                i += 1
    -              }
    +      // Join the row with rows from the opposite stream.
    +      val oppositeIterator = oppositeCache.iterator()
    +      while (oppositeIterator.hasNext) {
    +        val oppositeEntry = oppositeIterator.next
    +        val oppositeTime = oppositeEntry.getKey
    +        if (oppositeTime >= oppositeLowerBound && oppositeTime <= oppositeUpperBound)
{
    +          val oppositeRows = oppositeEntry.getValue
    +          var i = 0
    +          if (leftRow) {
    +            while (i < oppositeRows.size) {
    +              joinFunction.join(row, oppositeRows.get(i), cRowWrapper)
    +              i += 1
    +            }
    +          } else {
    +            while (i < oppositeRows.size) {
    +              joinFunction.join(oppositeRows.get(i), row, cRowWrapper)
    +              i += 1
                 }
    -          } else if (oppositeTime > oppositeUpperBound) {
    -            //TODO If the keys are ordered, can we break here?
               }
             }
    -      } else {
    -        //TODO Need some extra logic here?
    -        LOG.warn(s"$record is out-of-date.")
    +        // We could do the short-cutting optimization here once we get a state with ordered
keys.
           }
         }
    +    // We need to deal with the late data in the future.
    --- End diff --
    
    Yes, I agree. That would be a bigger effort though, because it should involve the other
operators as well. So far we do not provide any metrics for the relational operators but it
would make sense to do so. 


> Support rowtime inner equi-join between two streams in the SQL API
> ------------------------------------------------------------------
>
>                 Key: FLINK-6233
>                 URL: https://issues.apache.org/jira/browse/FLINK-6233
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: hongyuhong
>            Assignee: Xingcan Cui
>
> The goal of this issue is to add support for inner equi-join on proc time streams to
the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT o.rowtime , o.productId, o.orderId, s.rowtime AS shipTime 
> FROM Orders AS o 
> JOIN Shipments AS s 
> ON o.orderId = s.orderId 
> AND o.rowtime BETWEEN s.rowtime AND s.rowtime + INTERVAL '1' HOUR;
> {code}
> The following restrictions should initially apply:
> * The join hint only support inner join
> * The ON clause should include equi-join condition
> * The time-condition {{o.rowtime BETWEEN s.rowtime AND s.rowtime + INTERVAL '1' HOUR}}
only can use rowtime that is a system attribute, the time condition only support bounded time
range like {{o.rowtime BETWEEN s.rowtime - INTERVAL '1' HOUR AND s.rowtime + INTERVAL '1'
HOUR}}, not support unbounded like {{o.rowtime &lt; s.rowtime}} ,  and  should include
both two stream's rowtime attribute, {{o.rowtime between rowtime () and rowtime () + 1}} should
also not be supported.
> An row-time streams join will not be able to handle late data, because this would mean
in insert a row into a sorted order shift all other computations. This would be too expensive
to maintain. Therefore, we will throw an error if a user tries to use an row-time stream join
with late data handling.
> This issue includes:
> * Design of the DataStream operator to deal with stream join
> * Translation from Calcite's RelNode representation (LogicalJoin). 



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