WencongLiu commented on code in PR #23362: URL: https://github.com/apache/flink/pull/23362#discussion_r1369707442
########## docs/content/docs/dev/datastream/how_to_migrate_from_dataset_to_datastream.md: ########## @@ -0,0 +1,660 @@ +--- +title: "How To Migrate From DataSet to DataStream" +weight: 302 +type: docs +bookToc: false +aliases: + - /dev/how_to_migrate_from_dataset_to_datastream.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. DataSet operators can be implemented by the DataStream API. However, it's important to note that +different operators have varying costs in the implementation, and they can be categorized into three types: + +1. The first type of operators are quite similar to DataStream in terms of API usage. They can be easily implemented without much +complexity. +2. The second type of operators, on the other hand, have completely different names and API usage in DataStream. This can make the +job code more complex. +3. Lastly, the third type of operators not only have different names and API usage in DataStream, but they also involve additional +computation and shuffle costs. + +The subsequent sections will first introduce how to set the execution environment and provide detailed explanations on how to implement +each type of DataSet operators using the DataStream API, highlighting the specific considerations and challenges associated with each type. + + +## Setting the execution environment + +To execute a DataSet pipeline by DataStream API, we should first start by moving from ExecutionEnvironment to 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 + +### Same API Usage + +In the first type of operators, the usage of the API in DataStream is almost identical to that in DataSet. This means that +implementing these operators using the DataStream API is relatively straightforward and does not require significant modifications +or complexity in the 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 +DataSet<String> input1 = // [...] +DataSet<String> input2 = // [...] Review Comment: I have modified all sample codes to use the "dataSet/dataStream" variables. ########## docs/content/docs/dev/datastream/how_to_migrate_from_dataset_to_datastream.md: ########## @@ -0,0 +1,660 @@ +--- +title: "How To Migrate From DataSet to DataStream" +weight: 302 +type: docs +bookToc: false +aliases: + - /dev/how_to_migrate_from_dataset_to_datastream.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. DataSet operators can be implemented by the DataStream API. However, it's important to note that +different operators have varying costs in the implementation, and they can be categorized into three types: + +1. The first type of operators are quite similar to DataStream in terms of API usage. They can be easily implemented without much +complexity. +2. The second type of operators, on the other hand, have completely different names and API usage in DataStream. This can make the +job code more complex. +3. Lastly, the third type of operators not only have different names and API usage in DataStream, but they also involve additional +computation and shuffle costs. + +The subsequent sections will first introduce how to set the execution environment and provide detailed explanations on how to implement +each type of DataSet operators using the DataStream API, highlighting the specific considerations and challenges associated with each type. + + +## Setting the execution environment + +To execute a DataSet pipeline by DataStream API, we should first start by moving from ExecutionEnvironment to 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 + +### Same API Usage + +In the first type of operators, the usage of the API in DataStream is almost identical to that in DataSet. This means that +implementing these operators using the DataStream API is relatively straightforward and does not require significant modifications +or complexity in the 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 +DataSet<String> input1 = // [...] +DataSet<String> input2 = // [...] +DataSet<String> output = input1.union(input2); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<String> input1 = // [...] +DataStream<String> input2 = // [...] +DataStream<String> output = input1.union(input2); +``` +{{< /tab >}} +{{< /tabs>}} + + +#### Rebalance + +{{< tabs rebalancefunc >}} +{{< tab "DataSet">}} +```java +DataSet<String> input = // [...] +DataSet<String> output = input.rebalance(); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<String> input = // [...] +DataStream<String> output = input.rebalance(); +``` +{{< /tab >}} +{{< /tabs>}} + +#### Reduce on Grouped DataSet + +{{< tabs reducegroupfunc >}} +{{< tab "DataSet">}} +```java +DataSet<Tuple2<String, Integer>> input = // [...] +DataSet<Tuple2<String, Integer>> output = input + .groupBy(value -> value.f0) + .reduce(new ReduceFunction(){ + // implement user-defined reduce logic + }); +``` +{{< /tab >}} +{{< tab "DataStream">}} +```java +DataStream<Tuple2<String, Integer>> input = // [...] + DataStream<Tuple2<String, Integer>> output = input + .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>> input = // [...] +DataSet<Tuple2<String, Integer>> output = input + .groupBy(value -> value.f0) + // compute sum of the second field + // .aggregate(SUM, 1); + // compute min of the second field + // .aggregate(MIN, 1); + // compute max of the second field + // .aggregate(MAX, 1); Review Comment: I've separated each aggregate API into a single code block. -- This is an automated message from the Apache Git Service. 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