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https://issues.apache.org/jira/browse/FLINK-6232?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16005645#comment-16005645
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ASF GitHub Bot commented on FLINK-6232:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3715#discussion_r115866096
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/join/ProcTimeInnerJoin.scala
---
@@ -0,0 +1,323 @@
+/*
+ * 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
+import java.util.{List => JList}
+
+import org.apache.flink.api.common.functions.RichFilterFunction
+import org.apache.flink.api.common.state._
+import org.apache.flink.api.common.typeinfo.{BasicTypeInfo,
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.runtime.types.{CRow, CRowTypeInfo}
+import org.apache.flink.types.Row
+import org.apache.flink.util.Collector
+
+/**
+ * A CoProcessFunction to support stream join stream, currently just
support inner-join
+ *
+ * @param leftStreamWindowSize the left stream window size
+ * @param rightStreamWindowSize the right stream window size
+ * @param element1Type the input type of left stream
+ * @param element2Type the input type of right stream
+ * @param filterFunc the function of other non-equi condition include
time condition
+ *
+ */
+class ProcTimeInnerJoin(
+ private val leftStreamWindowSize: Long,
+ private val rightStreamWindowSize: Long,
+ private val element1Type: TypeInformation[CRow],
+ private val element2Type: TypeInformation[CRow],
+ private val filterFunc: RichFilterFunction[Row])
+ extends CoProcessFunction[CRow, CRow, CRow] {
+
+ private var outputC: CRow = _
+ private var listToRemove: JList[Long] = _
+
+ /** state to hold left stream element **/
+ private var row1MapState: MapState[Long, JList[Row]] = _
+ /** state to hold right stream element **/
+ private var row2MapState: MapState[Long, JList[Row]] = _
+
+ /** state to record last timer of left stream, 0 means no timer **/
+ private var timerState1: ValueState[Long] = _
+ /** state to record last timer of right stream, 0 means no timer **/
+ private var timerState2: ValueState[Long] = _
+
+
+ override def open(config: Configuration) {
+ outputC = new CRow(new Row(element1Type.getArity +
element2Type.getArity), true)
+ filterFunc.setRuntimeContext(getRuntimeContext)
+ filterFunc.open(config)
+
+ listToRemove = new util.ArrayList[Long]()
+
+ // initialize row state
+ val rowListTypeInfo1: TypeInformation[JList[Row]] =
+ new ListTypeInfo[Row](element1Type.asInstanceOf[CRowTypeInfo].rowType)
+ val mapStateDescriptor1: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]]("row1mapstate",
+ BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
rowListTypeInfo1)
+ row1MapState = getRuntimeContext.getMapState(mapStateDescriptor1)
+
+ val rowListTypeInfo2: TypeInformation[JList[Row]] =
+ new
ListTypeInfo[Row](element2Type.asInstanceOf[CRowTypeInfo].rowType)
+ val mapStateDescriptor2: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]]("row2mapstate",
+ BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
rowListTypeInfo2)
+ row2MapState = getRuntimeContext.getMapState(mapStateDescriptor2)
+
+ // initialize timer state
+ val valueStateDescriptor1: ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("timervaluestate1", classOf[Long])
+ timerState1 = getRuntimeContext.getState(valueStateDescriptor1)
+
+ val valueStateDescriptor2: ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("timervaluestate2", classOf[Long])
+ timerState2 = getRuntimeContext.getState(valueStateDescriptor2)
+ }
+
+ /**
+ * Process leftstream records
+ *
+ * @param valueC The input value.
+ * @param ctx The ctx to register timer or get current time
+ * @param out The collector for returning result values.
+ *
+ */
+ override def processElement1(
+ valueC: CRow,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
+ out: Collector[CRow]): Unit = {
+
+ processElement(
+ valueC,
+ ctx,
+ out,
+ leftStreamWindowSize,
+ timerState1,
+ row1MapState,
+ row2MapState,
+ rightStreamWindowSize,
+ true
+ )
+ }
+
+ /**
+ * Process rightstream records
+ *
+ * @param valueC The input value.
+ * @param ctx The ctx to register timer or get current time
+ * @param out The collector for returning result values.
+ *
+ */
+ override def processElement2(
+ valueC: CRow,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
+ out: Collector[CRow]): Unit = {
+
+ processElement(
+ valueC,
+ ctx,
+ out,
+ rightStreamWindowSize,
+ timerState2,
+ row2MapState,
+ row1MapState,
+ leftStreamWindowSize,
+ false
+ )
+ }
+
+ /**
+ * Called when a processing timer trigger.
+ * Expire left/right records which earlier than current time -
windowsize.
+ *
+ * @param timestamp The timestamp of the firing timer.
+ * @param ctx The ctx 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 = {
+
+ if (timerState1.value == timestamp) {
+ expireOutTimeRow(
+ timestamp,
+ leftStreamWindowSize,
+ row1MapState,
+ timerState1,
+ ctx
+ )
+ }
+
+ if (timerState2.value == timestamp) {
+ expireOutTimeRow(
+ timestamp,
+ rightStreamWindowSize,
+ row2MapState,
+ timerState2,
+ ctx
+ )
+ }
+ }
+
+ /**
+ * Puts an element from the input stream into state and search the
other state to
+ * output records meet the condition, and registers a timer for the
current record
+ * if there is no timer at present.
+ */
+ private def processElement(
+ valueC: CRow,
+ ctx: CoProcessFunction[CRow, CRow, CRow]#Context,
+ out: Collector[CRow],
+ winSize: Long,
+ timerState: ValueState[Long],
+ rowMapState: MapState[Long, JList[Row]],
+ oppoRowMapState: MapState[Long, JList[Row]],
+ oppoWinSize: Long,
+ isPositive: Boolean): Unit = {
+
+ val value = valueC.row
+
+ val curProcessTime = ctx.timerService.currentProcessingTime
+ val oppoExpiredTime =
+ if (oppoWinSize == 0) Long.MinValue else curProcessTime - oppoWinSize
+
+ // only when windowsize != 0, we need to store the element
+ if (winSize != 0) {
+ // register a timer to expire the element
+ if (timerState.value == 0 && winSize != -1) {
+ ctx.timerService.registerProcessingTimeTimer(curProcessTime +
winSize + 1)
+ timerState.update(curProcessTime + winSize + 1)
+ }
+
+ var rowList = rowMapState.get(curProcessTime)
+ if (rowList == null) {
+ rowList = new util.ArrayList[Row]()
+ }
+ rowList.add(value)
+ rowMapState.put(curProcessTime, rowList)
+
+ }
+
+ // loop the rightstream elments
+ val oppositeKeyIter = oppoRowMapState.keys().iterator()
+ while (oppositeKeyIter.hasNext) {
+ val eleTime = oppositeKeyIter.next()
+ if (eleTime < oppoExpiredTime) {
+ listToRemove.add(eleTime)
+ } else {
+ val oppoRowList = oppoRowMapState.get(eleTime)
+ var i = 0
+ while (i < oppoRowList.size) {
--- End diff --
We can use a `JoinFunction` to apply the filter condition and compose the
output row. Have a look at the `DataSetJoin` which is doing the same. This will
be more efficient, because we do not need loops and the condition is applied
before any work is done to assemble the output row.
> Support proctime inner equi-join between two streams in the SQL API
> -------------------------------------------------------------------
>
> Key: FLINK-6232
> URL: https://issues.apache.org/jira/browse/FLINK-6232
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: hongyuhong
> Assignee: hongyuhong
>
> 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.proctime, o.productId, o.orderId, s.proctime AS shipTime
> FROM Orders AS o
> JOIN Shipments AS s
> ON o.orderId = s.orderId
> AND o.proctime BETWEEN s.proctime AND s.proctime + 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.proctime BETWEEN s.proctime AND s.proctime +
> INTERVAL '1' HOUR}} only can use proctime that is a system attribute, the
> time condition only support bounded time range like {{o.proctime BETWEEN
> s.proctime - INTERVAL '1' HOUR AND s.proctime + INTERVAL '1' HOUR}}, not
> support unbounded like {{o.proctime > s.protime}}, and should include both
> two stream's proctime attribute, {{o.proctime between proctime() and
> proctime() + 1}} should also not be supported.
> This issue includes:
> * Design of the DataStream operator to deal with stream join
> * Translation from Calcite's RelNode representation (LogicalJoin).
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