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https://issues.apache.org/jira/browse/FLINK-6233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16196725#comment-16196725
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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_r143417404
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/join/TimeBoundedStreamInnerJoin.scala
---
@@ -0,0 +1,410 @@
+/*
+ * 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.join
+
+import java.util.{ArrayList, List => JList}
+
+import org.apache.flink.api.common.functions.FlatJoinFunction
+import org.apache.flink.api.common.state._
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.java.typeutils.ListTypeInfo
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.streaming.api.functions.co.CoProcessFunction
+import org.apache.flink.table.api.Types
+import org.apache.flink.table.codegen.Compiler
+import org.apache.flink.table.runtime.CRowWrappingCollector
+import org.apache.flink.table.runtime.types.CRow
+import org.apache.flink.table.util.Logging
+import org.apache.flink.types.Row
+import org.apache.flink.util.Collector
+
+/**
+ * A CoProcessFunction to execute time-bounded stream inner-join.
+ * Two kinds of time criteria:
+ * "L.time between R.time + X and R.time + Y" or "R.time between L.time -
Y and L.time - X".
+ *
+ * @param leftLowerBound the lower bound for the left stream (X in the
criteria)
+ * @param leftUpperBound the upper bound for the left stream (Y in the
criteria)
+ * @param allowedLateness the lateness allowed for the two streams
+ * @param leftType the input type of left stream
+ * @param rightType the input type of right stream
+ * @param genJoinFuncName the function code of other non-equi conditions
+ * @param genJoinFuncCode the function name of other non-equi conditions
+ *
+ */
+abstract class TimeBoundedStreamInnerJoin(
+ private val leftLowerBound: Long,
+ private val leftUpperBound: Long,
+ private val allowedLateness: Long,
+ private val leftType: TypeInformation[Row],
+ private val rightType: TypeInformation[Row],
+ private val genJoinFuncName: String,
+ private val genJoinFuncCode: String,
+ private val leftTimeIdx: Int,
+ private val rightTimeIdx: Int)
+ extends CoProcessFunction[CRow, CRow, CRow]
+ with Compiler[FlatJoinFunction[Row, Row, Row]]
+ with Logging {
+
+ private var cRowWrapper: CRowWrappingCollector = _
+
+ // the join function for other conditions
+ private var joinFunction: FlatJoinFunction[Row, Row, Row] = _
+
+ // cache to store rows from the left stream
+ private var leftCache: MapState[Long, JList[Row]] = _
+ // cache to store rows from the right stream
+ private var rightCache: MapState[Long, JList[Row]] = _
+
+ // state to record the timer on the left stream. 0 means no timer set
+ private var leftTimerState: ValueState[Long] = _
+ // state to record the timer on the right stream. 0 means no timer set
+ private var rightTimerState: ValueState[Long] = _
+
+ private val leftRelativeSize: Long = -leftLowerBound
+ private val rightRelativeSize: Long = leftUpperBound
+
+ private var leftExpirationTime: Long = 0L;
+ private var rightExpirationTime: Long = 0L;
+
+ protected var leftOperatorTime: Long = 0L
+ protected var rightOperatorTime: Long = 0L
+
+
+ // for delayed cleanup
+ private val cleanupDelay = (leftRelativeSize + rightRelativeSize) / 2
+
+ if (allowedLateness < 0) {
+ throw new IllegalArgumentException("The allowed lateness must be
non-negative.")
+ }
+
+ /**
+ * Get the maximum interval between receiving a row and emitting it (as
part of a joined result).
+ * Only reasonable for row time join.
+ *
+ * @return the maximum delay for the outputs
+ */
+ def getMaxOutputDelay: Long = Math.max(leftRelativeSize,
rightRelativeSize) + allowedLateness
+
+ override def open(config: Configuration) {
+ LOG.debug(s"Compiling JoinFunction: $genJoinFuncName \n\n " +
+ s"Code:\n$genJoinFuncCode")
+ val clazz = compile(
+ getRuntimeContext.getUserCodeClassLoader,
+ genJoinFuncName,
+ genJoinFuncCode)
+ LOG.debug("Instantiating JoinFunction.")
+ joinFunction = clazz.newInstance()
+
+ cRowWrapper = new CRowWrappingCollector()
+ cRowWrapper.setChange(true)
+
+ // Initialize the data caches.
+ val leftListTypeInfo: TypeInformation[JList[Row]] = new
ListTypeInfo[Row](leftType)
+ val leftStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]](
+ "InnerJoinLeftCache",
+ Types.LONG.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]](
+ "InnerJoinRightCache",
+ Types.LONG.asInstanceOf[TypeInformation[Long]],
+ rightListTypeInfo)
+ rightCache = getRuntimeContext.getMapState(rightStateDescriptor)
+
+ // Initialize the timer states.
+ val leftTimerStateDesc: ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("InnerJoinLeftTimerState",
classOf[Long])
+ leftTimerState = getRuntimeContext.getState(leftTimerStateDesc)
+
+ val rightTimerStateDesc: ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("InnerJoinRightTimerState",
classOf[Long])
+ rightTimerState = getRuntimeContext.getState(rightTimerStateDesc)
+ }
+
+ /**
+ * Process rows from the left stream.
+ */
+ override def processElement1(
+ cRowValue: CRow,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
+ out: Collector[CRow]): Unit = {
+ updateOperatorTime(ctx)
+ val leftRow = cRowValue.row
+ val timeForLeftRow: Long = getTimeForLeftStream(ctx, leftRow)
+ val rightQualifiedLowerBound: Long = timeForLeftRow - rightRelativeSize
+ val rightQualifiedUpperBound: Long = timeForLeftRow + leftRelativeSize
+ cRowWrapper.out = out
+
+ if (rightOperatorTime < rightQualifiedUpperBound) {
+ // Put the leftRow into the cache for later use.
+ var leftRowList = leftCache.get(timeForLeftRow)
+ if (null == leftRowList) {
+ leftRowList = new ArrayList[Row](1)
+ }
+ leftRowList.add(leftRow)
+ leftCache.put(timeForLeftRow, leftRowList)
+ if (rightTimerState.value == 0) {
+ // Register a timer on the RIGHT stream to remove rows.
+ registerCleanUpTimer(ctx, timeForLeftRow, rightTimerState, leftRow
= true)
+ }
+ }
+ // We'd like to produce as many results as possible.
+ if (rightExpirationTime < rightQualifiedUpperBound) {
+ rightExpirationTime = calExpirationTime(leftOperatorTime,
rightRelativeSize)
+ // Join the leftRow with rows from the right cache.
+ val rightIterator = rightCache.iterator()
+ while (rightIterator.hasNext) {
+ val rightEntry = rightIterator.next
+ val rightTime = rightEntry.getKey
+ if (rightTime >= rightQualifiedLowerBound && rightTime <=
rightQualifiedUpperBound) {
+ val rightRows = rightEntry.getValue
+ var i = 0
+ while (i < rightRows.size) {
+ joinFunction.join(leftRow, rightRows.get(i), cRowWrapper)
+ i += 1
+ }
+ }
+
+ if (rightTime <= rightExpirationTime) {
+ // eager remove
+ rightIterator.remove()
+ }// We could do the short-cutting optimization here once we get a
state with ordered keys.
+ }
+ }
+ }
+
+ /**
+ * Process rows from the right stream.
+ */
+ override def processElement2(
+ cRowValue: CRow,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
+ out: Collector[CRow]): Unit = {
+ updateOperatorTime(ctx)
+ val rightRow = cRowValue.row
+ val timeForRightRow: Long = getTimeForRightStream(ctx, rightRow)
+ val leftQualifiedLowerBound: Long = timeForRightRow - leftRelativeSize
+ val leftQualifiedUpperBound: Long = timeForRightRow +
rightRelativeSize
+ cRowWrapper.out = out
+
+ if (leftOperatorTime < leftQualifiedUpperBound) {
+ // Put the rightRow into the cache for later use.
+ var rightRowList = rightCache.get(timeForRightRow)
+ if (null == rightRowList) {
+ rightRowList = new ArrayList[Row](1)
+ }
+ rightRowList.add(rightRow)
+ rightCache.put(timeForRightRow, rightRowList)
+ if (leftTimerState.value == 0) {
+ // Register a timer on the LEFT stream to remove rows.
+ registerCleanUpTimer(ctx, timeForRightRow, leftTimerState, leftRow
= false)
+ }
+ }
+ // We'd like to produce as many results as possible.
+ if (leftExpirationTime < leftQualifiedUpperBound) {
+ leftExpirationTime = calExpirationTime(rightOperatorTime,
leftRelativeSize)
+ // Join the rightRow with rows from the left cache.
+ val leftIterator = leftCache.iterator()
+ while (leftIterator.hasNext) {
+ val leftEntry = leftIterator.next
+ val leftTime = leftEntry.getKey
+ if (leftTime >= leftQualifiedLowerBound && leftTime <=
leftQualifiedUpperBound) {
+ val leftRows = leftEntry.getValue
+ var i = 0
+ while (i < leftRows.size) {
+ joinFunction.join(leftRows.get(i), rightRow, cRowWrapper)
+ i += 1
+ }
+ }
+ if (leftTime <= leftExpirationTime) {
+ // eager remove
+ leftIterator.remove()
+ } // We could do the short-cutting optimization here once we get a
state with ordered keys.
+ }
+ }
+ }
+
+ /**
+ * Called when a registered timer is fired.
+ * Remove rows whose timestamps are earlier than the expiration time,
+ * and register a new timer for the remaining rows.
+ *
+ * @param timestamp the timestamp of the timer
+ * @param ctx the context to register timer or get current time
+ * @param out the collector for returning result values
+ */
+ override def onTimer(
+ timestamp: Long,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#OnTimerContext,
+ out: Collector[CRow]): Unit = {
+ updateOperatorTime(ctx)
+ // In the future, we should separate the left and right watermarks.
Otherwise, the
+ // registered timer of the faster stream will be delayed, even if the
watermarks have
+ // already been emitted by the source.
+ if (leftTimerState.value == timestamp) {
+ rightExpirationTime = calExpirationTime(leftOperatorTime,
rightRelativeSize)
+ removeExpiredRows(
+ rightExpirationTime,
+ rightCache,
+ leftTimerState,
+ ctx,
+ removeLeft = false
+ )
+ }
+
+ if (rightTimerState.value == timestamp) {
+ leftExpirationTime = calExpirationTime(rightOperatorTime,
leftRelativeSize)
+ removeExpiredRows(
+ leftExpirationTime,
+ leftCache,
+ rightTimerState,
+ ctx,
+ removeLeft = true
+ )
+ }
+ }
+
+ /**
+ * Calculate the expiration time with the given operator time and
relative window size.
+ *
+ * @param operatorTime the operator time
+ * @param relativeSize the relative window size
+ * @return the expiration time for cached rows
+ */
+ private def calExpirationTime(operatorTime: Long, relativeSize: Long):
Long = {
+ if (operatorTime < Long.MaxValue) {
--- End diff --
But what does the current check prevent then? We might still run in an
overflow if `relativeSize` is negative. Moreover, the case of `operatorTime ==
Long.MaxValue` may only happen at the end of the job when no more records will
be received. In this case the overflow would have no consequence, right?
Doesn't it make more sense to prevent the registration of negative timers?
> 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 < 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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