WencongLiu commented on code in PR #23362: URL: https://github.com/apache/flink/pull/23362#discussion_r1357721372
########## docs/content/docs/dev/datastream/dataset_migration.md: ########## @@ -0,0 +1,699 @@ +--- +title: "How To Migrate From DataSet to DataStream" +weight: 302 +type: docs +bookToc: false +aliases: + - /dev/dataset_migration.html +--- +<!-- +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. +--> + +# How to Migrate from DataSet to DataStream + +The DataSet API has been formally deprecated and will no longer receive active maintenance and support. It will be removed in the +Flink 2.0 version. Flink users are recommended to migrate from the DataSet API to the DataStream API, Table API and SQL for their +data processing requirements. + +For the most of DataSet APIs, the users can utilize the DataStream API to get the same calculation result in the batch jobs. However, +different DataSet API can be implemented by DataStream API with various difference on semantic and behavior. All DataSet APIs can be +categorized into four types: + +Category 1: These DataSet APIs can be implemented by DataStream APIs with same semantic and same calculation behavior. + +Category 2: These DataSet APIs can be implemented by DataStream APIs with different semantic but same calculation behavior. This will +make the job code more complex. + +Category 3: These DataSet APIs can be implemented by DataStream APIs with different semantic and different calculation behavior. This +will involve additional computation and shuffle costs. + +Category 4: These DataSet APIs are not supported by DataStream APIs. + +The subsequent sections will first introduce how to set the execution environment and provide detailed explanations on how to implement +each category of DataSet APIs using the DataStream APIs, highlighting the specific considerations and challenges associated with each +category. + + +## Setting the execution environment + +To execute a DataSet pipeline by DataStream API, we should first start by replacing ExecutionEnvironment with StreamExecutionEnvironment. +{{< tabs executionenv >}} +{{< tab "DataSet">}} +```java +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment(); +``` +{{< /tab >}} +{{< /tabs>}} + +As the source of DataSet is always bounded, the execution mode is suggested to be set to RuntimeMode.BATCH to allow Flink to apply +additional optimizations for batch processing. +```java +StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment(); +executionEnvironment.setRuntimeMode(RuntimeExecutionMode.BATCH); +``` + +## Implement the DataSet API by DataStream + +### Category 1 + +For Category 1, the usage of the API in DataStream is almost identical to that in DataSet. This means that implementing these +DataSet APIs by the DataStream API is relatively straightforward and does not require significant modifications or complexity +in the job code. + +#### Map + +{{< tabs mapfunc >}} +{{< tab "DataSet">}} +```java +dataSet.map(new MapFunction(){ + // implement user-defined map logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.map(new MapFunction(){ + // implement user-defined map logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### FlatMap + +{{< tabs flatmapfunc >}} +{{< tab "DataSet">}} +```java +dataSet.flatMap(new FlatMapFunction(){ + // implement user-defined flatmap logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.flatMap(new FlatMapFunction(){ + // implement user-defined flatmap logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Filter + +{{< tabs filterfunc >}} +{{< tab "DataSet">}} +```java +dataSet.filter(new FilterFunction(){ + // implement user-defined filter logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.filter(new FilterFunction(){ + // implement user-defined filter logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Union + +{{< tabs unionfunc >}} +{{< tab "DataSet">}} +```java +dataSet1.union(dataSet2); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream1.union(dataStream2); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### Rebalance + +{{< tabs rebalancefunc >}} +{{< tab "DataSet">}} +```java +dataSet.rebalance(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.rebalance(); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Reduce on Grouped DataSet + +{{< tabs reducegroupfunc >}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +dataSet.groupBy(value -> value.f0) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +dataStream.keyBy(value -> value.f0) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Aggregate on Grouped DataSet + +{{< tabs aggregategroupfunc >}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +// compute sum of the second field +dataSet.groupBy(value -> value.f0).aggregate(SUM, 1); +// compute min of the second field +dataSet.groupBy(value -> value.f0).aggregate(MIN, 1); +// compute max of the second field +dataSet.groupBy(value -> value.f0).aggregate(MAX, 1); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +// compute sum of the second field +dataStream.keyBy(value -> value.f0).sum(1); +// compute min of the second field +dataStream.keyBy(value -> value.f0).min(1); +// compute max of the second field +dataStream.keyBy(value -> value.f0).max(1); +``` +{{< /tab >}} +{{< /tabs>}} + + +### Category 2 + +For category 2, these DataSet APIs can be implemented by DataStream APIs with different semantic but same calculation behavior. +The developers need to adapt their code to accommodate these variations, which introduces additional complexity. + +#### Project + +{{< tabs projectfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple3<Integer, Double, String>> dataSet = // [...] +dataSet.project(2,0); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple3<Integer, Double, String>> dataStream = // [...] +dataStream.map(value -> Tuple2.of(value.f2, value.f0)); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Distinct + +{{< tabs distinctfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +dataSet.distinct(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +DataStream<Integer> output = dataStream + .keyBy(value -> value) + .reduce((value1, value2) -> value1); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Hash-Partition + +{{< tabs hashpartitionfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +dataSet.partitionByHash(value -> value.f0); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +// partition by the hashcode of key +dataStream.partitionCustom((key, numSubpartition) -> key.hashCode() % numSubpartition, value -> value.f0); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Reduce on Full DataSet + +If developers want to compute data of full datastream, GlobalWindow could be used to collect all records of datastream. +However, a special trigger is also required to trigger the computation of GlobalWindow at the end of its inputs. Here is an example +code snippet of the trigger. + +```java +public class EOFTrigger extends Trigger<Object, GlobalWindow> { + + private boolean hasRegistered; + + @Override + public TriggerResult onElement( + Object element, long timestamp, GlobalWindow window, TriggerContext ctx) { + if (!hasRegistered) { + ctx.registerEventTimeTimer(Long.MAX_VALUE); + hasRegistered = true; + } + return TriggerResult.CONTINUE; + } + + @Override + public TriggerResult onEventTime(long time, GlobalWindow window, TriggerContext ctx) { + return TriggerResult.FIRE; + } + + @Override + public TriggerResult onProcessingTime(long time, GlobalWindow window, TriggerContext ctx) { + return TriggerResult.CONTINUE; + } + + @Override + public void clear(GlobalWindow window, TriggerContext ctx) throws Exception {} +} +``` +Then the reduce operation on full datastream could be performed by EOFTrigger. + +{{< tabs reducefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<String> dataSet = // [...] +dataSet.reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<String> dataStream = // [...] +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### Aggregate on Full DataSet + +The aggregate on full datastream could also be performed by EOFTrigger. + +{{< tabs aggregatefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<Integer, Integer>> dataSet = // [...] +// compute sum of the second field +dataSet.aggregate(SUM, 1); +// compute min of the second field +dataSet.aggregate(MIN, 1); +// compute max of the second field +dataSet.aggregate(MAX, 1); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<Integer, Integer>> dataStream = // [...] +// compute sum of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).sum(1); +// compute min of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).min(1); +// compute max of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).max(1); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### GroupReduce on Full DataSet + +The grpup reduce on full datastream could also be performed by EOFTrigger. + +{{< tabs groupreducefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +dataSet.reduceGroup(new GroupReduceFunction(){ + // implement user-defined group reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +// assign a same timestamp to all records +assignSameTimestamp(dataStream); +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()) + .apply(new WindowFunction(){ + // implement user-defined group reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + +### Category 3 + +For category 3, these DataSet APIs can be implemented by DataStream APIs with different semantic and different calculation behavior. Additional +calculation steps will be added. + +To collect records from each key, the DataStream assigns a same timestamp to all records and uses a fixed-length time window to gather them. +However, this introduces additional computational costs due to timestamp processing. + +Here is an example code snippet. The function assignSameTimestamp is used to explain the detailed behavior and will be utilized in the +subsequent sections: +```java +// assign a same timestamp to all records +void assignSameTimestamp(DataStream<Tuple2<String, Integer>> dataStream) { + dataStream.assignTimestampsAndWatermarks( + WatermarkStrategy.<Tuple2<String, Integer>>forMonotonousTimestamps() + .withTimestampAssigner((event, timestamp) -> 0)); +} +``` + +To collect records from each subtask, every record needs to be assigned a unique subtask ID and grouped accordingly within the window. +This additional step of assigning the subtask ID and performing a groupby operation introduces shuffle costs. Here is an example code +snippet showing how to assign a subtask ID to each record. The function assignSubtaskID is used to explain the detailed behavior and will +be utilized in the subsequent sections: +```java +// assign subtask ID to all records +DataStream<Tuple2<String, Integer>> assignSubtaskID(DataStream<Integer> dataStream) { + return dataStream.map(new RichMapFunction<Integer, Tuple2<String, Integer>>() { + @Override + public Tuple2<String, Integer> map(Integer value) { + return Tuple2.of(String.valueOf(getRuntimeContext().getIndexOfThisSubtask()), value); + } + }); +} +``` + +#### MapPartition/SortPartition + +{{< tabs mapsortpartitionfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +// MapPartition +dataSet.mapPartition(new MapPartitionFunction(){ + // implement user-defined map partition logic + }); +// SortPartition +dataSet.sortPartition(0, Order.ASCENDING); +dataSet.sortPartition(0, Order.DESCENDING) +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +// assign subtask ID to all records +DataStream<Tuple2<String, Integer>> dataStream1 = assignSubtaskID(dataStream); +// assign a same timestamp to all records +assignSameTimestamp(dataStream1); +dataStream1.keyBy(value -> value.f0) + .window(TumblingEventTimeWindows.of(Time.seconds(1))) Review Comment: Good idea.😄 I've migrate all examples that utilize the record timestamp to `EOFTrigger`. ########## docs/content/docs/dev/datastream/dataset_migration.md: ########## @@ -0,0 +1,699 @@ +--- +title: "How To Migrate From DataSet to DataStream" +weight: 302 +type: docs +bookToc: false +aliases: + - /dev/dataset_migration.html +--- +<!-- +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. +--> + +# How to Migrate from DataSet to DataStream + +The DataSet API has been formally deprecated and will no longer receive active maintenance and support. It will be removed in the +Flink 2.0 version. Flink users are recommended to migrate from the DataSet API to the DataStream API, Table API and SQL for their +data processing requirements. + +For the most of DataSet APIs, the users can utilize the DataStream API to get the same calculation result in the batch jobs. However, +different DataSet API can be implemented by DataStream API with various difference on semantic and behavior. All DataSet APIs can be +categorized into four types: + +Category 1: These DataSet APIs can be implemented by DataStream APIs with same semantic and same calculation behavior. + +Category 2: These DataSet APIs can be implemented by DataStream APIs with different semantic but same calculation behavior. This will +make the job code more complex. + +Category 3: These DataSet APIs can be implemented by DataStream APIs with different semantic and different calculation behavior. This +will involve additional computation and shuffle costs. + +Category 4: These DataSet APIs are not supported by DataStream APIs. + +The subsequent sections will first introduce how to set the execution environment and provide detailed explanations on how to implement +each category of DataSet APIs using the DataStream APIs, highlighting the specific considerations and challenges associated with each +category. + + +## Setting the execution environment + +To execute a DataSet pipeline by DataStream API, we should first start by replacing ExecutionEnvironment with StreamExecutionEnvironment. +{{< tabs executionenv >}} +{{< tab "DataSet">}} +```java +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment(); +``` +{{< /tab >}} +{{< /tabs>}} + +As the source of DataSet is always bounded, the execution mode is suggested to be set to RuntimeMode.BATCH to allow Flink to apply +additional optimizations for batch processing. +```java +StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment(); +executionEnvironment.setRuntimeMode(RuntimeExecutionMode.BATCH); +``` + +## Implement the DataSet API by DataStream + +### Category 1 + +For Category 1, the usage of the API in DataStream is almost identical to that in DataSet. This means that implementing these +DataSet APIs by the DataStream API is relatively straightforward and does not require significant modifications or complexity +in the job code. + +#### Map + +{{< tabs mapfunc >}} +{{< tab "DataSet">}} +```java +dataSet.map(new MapFunction(){ + // implement user-defined map logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.map(new MapFunction(){ + // implement user-defined map logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### FlatMap + +{{< tabs flatmapfunc >}} +{{< tab "DataSet">}} +```java +dataSet.flatMap(new FlatMapFunction(){ + // implement user-defined flatmap logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.flatMap(new FlatMapFunction(){ + // implement user-defined flatmap logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Filter + +{{< tabs filterfunc >}} +{{< tab "DataSet">}} +```java +dataSet.filter(new FilterFunction(){ + // implement user-defined filter logic +}); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.filter(new FilterFunction(){ + // implement user-defined filter logic +}); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Union + +{{< tabs unionfunc >}} +{{< tab "DataSet">}} +```java +dataSet1.union(dataSet2); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream1.union(dataStream2); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### Rebalance + +{{< tabs rebalancefunc >}} +{{< tab "DataSet">}} +```java +dataSet.rebalance(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +dataStream.rebalance(); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Reduce on Grouped DataSet + +{{< tabs reducegroupfunc >}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +dataSet.groupBy(value -> value.f0) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +dataStream.keyBy(value -> value.f0) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Aggregate on Grouped DataSet + +{{< tabs aggregategroupfunc >}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +// compute sum of the second field +dataSet.groupBy(value -> value.f0).aggregate(SUM, 1); +// compute min of the second field +dataSet.groupBy(value -> value.f0).aggregate(MIN, 1); +// compute max of the second field +dataSet.groupBy(value -> value.f0).aggregate(MAX, 1); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +// compute sum of the second field +dataStream.keyBy(value -> value.f0).sum(1); +// compute min of the second field +dataStream.keyBy(value -> value.f0).min(1); +// compute max of the second field +dataStream.keyBy(value -> value.f0).max(1); +``` +{{< /tab >}} +{{< /tabs>}} + + +### Category 2 + +For category 2, these DataSet APIs can be implemented by DataStream APIs with different semantic but same calculation behavior. +The developers need to adapt their code to accommodate these variations, which introduces additional complexity. + +#### Project + +{{< tabs projectfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple3<Integer, Double, String>> dataSet = // [...] +dataSet.project(2,0); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple3<Integer, Double, String>> dataStream = // [...] +dataStream.map(value -> Tuple2.of(value.f2, value.f0)); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Distinct + +{{< tabs distinctfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +dataSet.distinct(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +DataStream<Integer> output = dataStream + .keyBy(value -> value) + .reduce((value1, value2) -> value1); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Hash-Partition + +{{< tabs hashpartitionfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> dataSet = // [...] +dataSet.partitionByHash(value -> value.f0); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> dataStream = // [...] +// partition by the hashcode of key +dataStream.partitionCustom((key, numSubpartition) -> key.hashCode() % numSubpartition, value -> value.f0); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Reduce on Full DataSet + +If developers want to compute data of full datastream, GlobalWindow could be used to collect all records of datastream. +However, a special trigger is also required to trigger the computation of GlobalWindow at the end of its inputs. Here is an example +code snippet of the trigger. + +```java +public class EOFTrigger extends Trigger<Object, GlobalWindow> { + + private boolean hasRegistered; + + @Override + public TriggerResult onElement( + Object element, long timestamp, GlobalWindow window, TriggerContext ctx) { + if (!hasRegistered) { + ctx.registerEventTimeTimer(Long.MAX_VALUE); + hasRegistered = true; + } + return TriggerResult.CONTINUE; + } + + @Override + public TriggerResult onEventTime(long time, GlobalWindow window, TriggerContext ctx) { + return TriggerResult.FIRE; + } + + @Override + public TriggerResult onProcessingTime(long time, GlobalWindow window, TriggerContext ctx) { + return TriggerResult.CONTINUE; + } + + @Override + public void clear(GlobalWindow window, TriggerContext ctx) throws Exception {} +} +``` +Then the reduce operation on full datastream could be performed by EOFTrigger. + +{{< tabs reducefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<String> dataSet = // [...] +dataSet.reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<String> dataStream = // [...] +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### Aggregate on Full DataSet + +The aggregate on full datastream could also be performed by EOFTrigger. + +{{< tabs aggregatefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<Integer, Integer>> dataSet = // [...] +// compute sum of the second field +dataSet.aggregate(SUM, 1); +// compute min of the second field +dataSet.aggregate(MIN, 1); +// compute max of the second field +dataSet.aggregate(MAX, 1); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<Integer, Integer>> dataStream = // [...] +// compute sum of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).sum(1); +// compute min of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).min(1); +// compute max of the second field +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()).max(1); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### GroupReduce on Full DataSet + +The grpup reduce on full datastream could also be performed by EOFTrigger. + +{{< tabs groupreducefullfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +dataSet.reduceGroup(new GroupReduceFunction(){ + // implement user-defined group reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +// assign a same timestamp to all records +assignSameTimestamp(dataStream); +dataStream.windowAll(GlobalWindows.create()).trigger(new EOFTrigger()) + .apply(new WindowFunction(){ + // implement user-defined group reduce logic + }); +``` +{{< /tab >}} +{{< /tabs>}} + +### Category 3 + +For category 3, these DataSet APIs can be implemented by DataStream APIs with different semantic and different calculation behavior. Additional +calculation steps will be added. + +To collect records from each key, the DataStream assigns a same timestamp to all records and uses a fixed-length time window to gather them. +However, this introduces additional computational costs due to timestamp processing. + +Here is an example code snippet. The function assignSameTimestamp is used to explain the detailed behavior and will be utilized in the +subsequent sections: +```java +// assign a same timestamp to all records +void assignSameTimestamp(DataStream<Tuple2<String, Integer>> dataStream) { + dataStream.assignTimestampsAndWatermarks( + WatermarkStrategy.<Tuple2<String, Integer>>forMonotonousTimestamps() + .withTimestampAssigner((event, timestamp) -> 0)); +} +``` + +To collect records from each subtask, every record needs to be assigned a unique subtask ID and grouped accordingly within the window. +This additional step of assigning the subtask ID and performing a groupby operation introduces shuffle costs. Here is an example code +snippet showing how to assign a subtask ID to each record. The function assignSubtaskID is used to explain the detailed behavior and will +be utilized in the subsequent sections: +```java +// assign subtask ID to all records +DataStream<Tuple2<String, Integer>> assignSubtaskID(DataStream<Integer> dataStream) { + return dataStream.map(new RichMapFunction<Integer, Tuple2<String, Integer>>() { + @Override + public Tuple2<String, Integer> map(Integer value) { + return Tuple2.of(String.valueOf(getRuntimeContext().getIndexOfThisSubtask()), value); + } + }); +} +``` + +#### MapPartition/SortPartition + +{{< tabs mapsortpartitionfunc>}} +{{< tab "DataSet">}} +```java +DataSet<Integer> dataSet = // [...] +// MapPartition +dataSet.mapPartition(new MapPartitionFunction(){ + // implement user-defined map partition logic + }); +// SortPartition +dataSet.sortPartition(0, Order.ASCENDING); +dataSet.sortPartition(0, Order.DESCENDING) +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Integer> dataStream = // [...] +// assign subtask ID to all records +DataStream<Tuple2<String, Integer>> dataStream1 = assignSubtaskID(dataStream); +// assign a same timestamp to all records +assignSameTimestamp(dataStream1); +dataStream1.keyBy(value -> value.f0) + .window(TumblingEventTimeWindows.of(Time.seconds(1))) Review Comment: Good idea.😄 I've migrate all examples that utilize the record timestamp to `EOFTrigger`. -- This is an automated message from the Apache Git Service. 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