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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-6075) Support Limit/Top(Sort) for Stream SQL
Date Wed, 19 Apr 2017 15:02:42 GMT

    [ https://issues.apache.org/jira/browse/FLINK-6075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15974849#comment-15974849
] 

ASF GitHub Bot commented on FLINK-6075:
---------------------------------------

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

    https://github.com/apache/flink/pull/3714#discussion_r112205545
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamSort.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.plan.nodes.datastream
    +
    +import org.apache.calcite.plan.{ RelOptCluster, RelTraitSet }
    +import org.apache.calcite.rel.`type`.RelDataType
    +import org.apache.calcite.rel.core.AggregateCall
    +import org.apache.calcite.rel.{ RelNode, RelWriter, SingleRel }
    +import org.apache.flink.api.java.tuple.Tuple
    +import org.apache.flink.streaming.api.datastream.{ AllWindowedStream, DataStream, KeyedStream,
WindowedStream }
    +import org.apache.flink.streaming.api.windowing.assigners._
    +import org.apache.flink.streaming.api.windowing.time.Time
    +import org.apache.flink.streaming.api.windowing.windows.{ Window => DataStreamWindow
}
    +import org.apache.flink.table.api.StreamTableEnvironment
    +import org.apache.flink.table.calcite.FlinkRelBuilder.NamedWindowProperty
    +import org.apache.flink.table.calcite.FlinkTypeFactory
    +import org.apache.flink.table.expressions._
    +import org.apache.flink.table.plan.logical._
    +import org.apache.flink.table.plan.nodes.CommonAggregate
    +import org.apache.flink.table.plan.nodes.datastream.DataStreamAggregate._
    +import org.apache.flink.table.runtime.aggregate.AggregateUtil._
    +import org.apache.flink.table.runtime.aggregate._
    +import org.apache.flink.table.typeutils.TypeCheckUtils.isTimeInterval
    +import org.apache.flink.table.typeutils.{ RowIntervalTypeInfo, TimeIntervalTypeInfo }
    +import org.apache.flink.types.Row
    +import org.apache.calcite.rel.logical.LogicalSort
    +import org.apache.calcite.sql.SqlAggFunction
    +import org.apache.flink.table.plan.nodes.datastream.DataStreamRel
    +import org.apache.flink.table.api.TableException
    +import org.apache.calcite.sql.fun.SqlSingleValueAggFunction
    +import org.apache.flink.api.common.functions.RichMapFunction
    +import org.apache.flink.api.common.functions.RichFlatMapFunction
    +import org.apache.flink.configuration.Configuration
    +import org.apache.flink.util.Collector
    +import org.apache.flink.api.common.state.ValueState
    +import org.apache.flink.api.common.state.ValueStateDescriptor
    +import org.apache.flink.api.java.typeutils.RowTypeInfo
    +import org.apache.flink.table.functions.ProcTimeType
    +import org.apache.flink.table.functions.RowTimeType
    +import org.apache.calcite.rel.core.Sort
    +import org.apache.flink.api.java.functions.NullByteKeySelector
    +import org.apache.calcite.rel.RelFieldCollation.Direction
    +import org.apache.flink.table.runtime.aggregate.SortUtil._
    +
    +/**
    +  * Flink RelNode which matches along with Sort Rule.
    +  *
    +  */
    +class DataStreamSort(
    +  sort: LogicalSort,
    +  cluster: RelOptCluster,
    +  traitSet: RelTraitSet,
    +  inputNode: RelNode,
    +  rowRelDataType: RelDataType,
    +  inputType: RelDataType,
    +  description: String)
    +    extends SingleRel(cluster, traitSet, inputNode) with DataStreamRel {
    +
    +  override def deriveRowType(): RelDataType = rowRelDataType
    +
    +  override def copy(traitSet: RelTraitSet, inputs: java.util.List[RelNode]): RelNode
= {
    +    new DataStreamSort(
    +      sort,
    +      cluster,
    +      traitSet,
    +      inputs.get(0),
    +      rowRelDataType,
    +      inputType,
    +      description + sort.getId())
    +  }
    +
    +  override def toString: String = {
    +    s"Sort($sort)" +
    +      " on fields: (${sort.collation.getFieldCollations})"
    +  }
    +
    +  override def explainTerms(pw: RelWriter): RelWriter = {
    +    super.explainTerms(pw)
    +      .item("aggregate", sort)
    +      .item("sort fields",sort.collation.getFieldCollations)
    +      .itemIf("offset", sort.offset, sort.offset!=null)
    +      .itemIf("fetch", sort.fetch, sort.fetch!=null)
    +      .item("input", inputNode)
    +  }
    +
    +  override def translateToPlan(tableEnv: StreamTableEnvironment): DataStream[Row] = {
    +
    +    val inputDS = getInput.asInstanceOf[DataStreamRel].translateToPlan(tableEnv)
    +    
    +    //need to identify time between others order fields. Time needs to be first sort
element
    +    val timeType = SortUtil.getTimeType(sort,inputType)
    --- End diff --
    
    +space `(sort, inputType)`


> 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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