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https://issues.apache.org/jira/browse/FLINK-6075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16015385#comment-16015385
]
ASF GitHub Bot commented on FLINK-6075:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3889#discussion_r117182703
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/RowTimeSortProcessFunction.scala
---
@@ -0,0 +1,169 @@
+/*
+ * 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.common.typeinfo.{BasicTypeInfo,
TypeInformation}
+import org.apache.flink.api.java.typeutils.{RowTypeInfo, ListTypeInfo}
+import org.apache.flink.runtime.state.{ FunctionInitializationContext,
FunctionSnapshotContext }
+import org.apache.flink.streaming.api.functions.ProcessFunction
+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._
+import org.apache.flink.api.java.tuple.{ Tuple2 => JTuple2 }
+import org.apache.flink.api.common.state.MapState
+import org.apache.flink.api.common.state.MapStateDescriptor
+import org.apache.flink.configuration.Configuration
+import java.util.Comparator
+import java.util.ArrayList
+import java.util.Collections
+import org.apache.flink.api.common.typeutils.TypeComparator
+import java.util.{List => JList, ArrayList => JArrayList}
+import org.apache.flink.table.runtime.types.{CRow, CRowTypeInfo}
+
+/**
+ * Process Function used for the aggregate in bounded rowtime sort without
offset/fetch
+ * [[org.apache.flink.streaming.api.datastream.DataStream]]
+ *
+ * @param fieldCount Is used to indicate fields in the current element to
forward
+ * @param inputType It is used to mark the type of the incoming data
+ * @param rowComparator the [[java.util.Comparator]] is used for this sort
aggregation
+ */
+class RowTimeSortProcessFunction(
+ private val fieldCount: Int,
+ private val inputRowType: CRowTypeInfo,
+ private val rowComparator: CollectionRowComparator)
+ extends ProcessFunction[CRow, CRow] {
+
+ Preconditions.checkNotNull(rowComparator)
+
+ private val sortArray: ArrayList[Row] = new ArrayList[Row]
+
+ // the state which keeps all the events that are not expired.
+ // Each timestamp will contain an associated list with the events
+ // received at that timestamp
+ private var dataState: MapState[Long, JList[Row]] = _
+
+ // the state which keeps the last triggering timestamp to filter late
events
+ private var lastTriggeringTsState: ValueState[Long] = _
+
+ private var outputC: CRow = _
+
+
+ override def open(config: Configuration) {
+
+ val keyTypeInformation: TypeInformation[Long] =
+ BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]]
+ val valueTypeInformation: TypeInformation[JList[Row]] = new
ListTypeInfo[Row](
+ inputRowType.asInstanceOf[CRowTypeInfo].rowType)
+
+ val mapStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]](
+ "dataState",
+ keyTypeInformation,
+ valueTypeInformation)
+
+ dataState = getRuntimeContext.getMapState(mapStateDescriptor)
+
+ val lastTriggeringTsDescriptor: ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("lastTriggeringTsState",
classOf[Long])
+ lastTriggeringTsState =
getRuntimeContext.getState(lastTriggeringTsDescriptor)
+ }
+
+
+ override def processElement(
+ inputC: CRow,
+ ctx: ProcessFunction[CRow, CRow]#Context,
+ out: Collector[CRow]): Unit = {
+
+ val input = inputC.row
+
+ if( outputC == null) {
+ outputC = new CRow(input, true)
+ }
+
+ // triggering timestamp for trigger calculation
+ val triggeringTs = ctx.timestamp
+
+ val lastTriggeringTs = lastTriggeringTsState.value
+
+ // check if the data is expired, if not, save the data and register
event time timer
+ if (triggeringTs > lastTriggeringTs) {
+ val data = dataState.get(triggeringTs)
+ if (null != data) {
+ data.add(input)
+ dataState.put(triggeringTs, data)
+ } else {
+ val data = new JArrayList[Row]
+ data.add(input)
+ dataState.put(triggeringTs, data)
+ // register event time timer
+ ctx.timerService.registerEventTimeTimer(triggeringTs)
+ }
+ }
+ }
+
+
+ override def onTimer(
+ timestamp: Long,
+ ctx: ProcessFunction[CRow, CRow]#OnTimerContext,
+ out: Collector[CRow]): Unit = {
+
+ // gets all window data from state for the calculation
+ val inputs: JList[Row] = dataState.get(timestamp)
+
+ if (null != inputs) {
+
+ var dataListIndex = 0
+
+ // no retraction needed for time order sort
+
+ //no selection of offset/fetch
+
+ dataListIndex = 0
+ sortArray.clear()
--- End diff --
`inputs` is not a `ListState` but an actual `ArrayList` that was returned
from the `dataState: MapState[JList[Row]]`. So we are copying the elements from
one `ArrayList` into another.
In `ProctimeSortProcessFunction` the `ListState[Row]` is much better than
`ValueState[JList[Row]]` because adding to the `ListState` is basically free,
while `ValueState` would need to deserialized the `List` every time we read or
write.
> Support Limit/Top(Sort) for Stream SQL
> --------------------------------------
>
> Key: FLINK-6075
> URL: https://issues.apache.org/jira/browse/FLINK-6075
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: radu
> Labels: features
> Attachments: sort.png
>
>
> These will be split in 3 separated JIRA issues. However, the design is the
> same only the processing function differs in terms of the output. Hence, the
> design is the same for all of them.
> Time target: Proc Time
> **SQL targeted query examples:**
> *Sort example*
> Q1)` SELECT a FROM stream1 GROUP BY HOP(proctime, INTERVAL '1' HOUR, INTERVAL
> '3' HOUR) ORDER BY b`
> Comment: window is defined using GROUP BY
> Comment: ASC or DESC keywords can be placed to mark the ordering type
> *Limit example*
> Q2) `SELECT a FROM stream1 WHERE rowtime BETWEEN current_timestamp - INTERVAL
> '1' HOUR AND current_timestamp ORDER BY b LIMIT 10`
> Comment: window is defined using time ranges in the WHERE clause
> Comment: window is row triggered
> *Top example*
> Q3) `SELECT sum(a) OVER (ORDER BY proctime RANGE INTERVAL '1' HOUR PRECEDING
> LIMIT 10) FROM stream1`
> Comment: limit over the contents of the sliding window
> General Comments:
> -All these SQL clauses are supported only over windows (bounded collections
> of data).
> -Each of the 3 operators will be supported with each of the types of
> expressing the windows.
> **Description**
> The 3 operations (limit, top and sort) are similar in behavior as they all
> require a sorted collection of the data on which the logic will be applied
> (i.e., select a subset of the items or the entire sorted set). These
> functions would make sense in the streaming context only in the context of a
> window. Without defining a window the functions could never emit as the sort
> operation would never trigger. If an SQL query will be provided without
> limits an error will be thrown (`SELECT a FROM stream1 TOP 10` -> ERROR).
> Although not targeted by this JIRA, in the case of working based on event
> time order, the retraction mechanisms of windows and the lateness mechanisms
> can be used to deal with out of order events and retraction/updates of
> results.
> **Functionality example**
> We exemplify with the query below for all the 3 types of operators (sorting,
> limit and top). Rowtime indicates when the HOP window will trigger – which
> can be observed in the fact that outputs are generated only at those moments.
> The HOP windows will trigger at every hour (fixed hour) and each event will
> contribute/ be duplicated for 2 consecutive hour intervals. Proctime
> indicates the processing time when a new event arrives in the system. Events
> are of the type (a,b) with the ordering being applied on the b field.
> `SELECT a FROM stream1 HOP(proctime, INTERVAL '1' HOUR, INTERVAL '2' HOUR)
> ORDER BY b (LIMIT 2/ TOP 2 / [ASC/DESC] `)
> ||Rowtime|| Proctime|| Stream1|| Limit 2|| Top 2|| Sort
> [ASC]||
> | |10:00:00 |(aaa, 11) | | |
> |
> | |10:05:00 |(aab, 7) | | | |
> |10-11 |11:00:00 | | aab,aaa |aab,aaa | aab,aaa
> |
> | |11:03:00 |(aac,21) | | | |
>
> |11-12 |12:00:00 | | aab,aaa |aab,aaa | aab,aaa,aac|
> | |12:10:00 |(abb,12) | | | |
>
> | |12:15:00 |(abb,12) | | | |
>
> |12-13 |13:00:00 | | abb,abb | abb,abb |
> abb,abb,aac|
> |...|
> **Implementation option**
> Considering that the SQL operators will be associated with window boundaries,
> the functionality will be implemented within the logic of the window as
> follows.
> * Window assigner – selected based on the type of window used in SQL
> (TUMBLING, SLIDING…)
> * Evictor/ Trigger – time or count evictor based on the definition of the
> window boundaries
> * Apply – window function that sorts data and selects the output to trigger
> (based on LIMIT/TOP parameters). All data will be sorted at once and result
> outputted when the window is triggered
> An alternative implementation can be to use a fold window function to sort
> the elements as they arrive, one at a time followed by a flatMap to filter
> the number of outputs.
> !sort.png!
> **General logic of Join**
> ```
> inputDataStream.window(new [Slide/Tumble][Time/Count]Window())
> //.trigger(new [Time/Count]Trigger()) – use default
> //.evictor(new [Time/Count]Evictor()) – use default
> .apply(SortAndFilter());
> ```
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