WencongLiu commented on code in PR #23362: URL: https://github.com/apache/flink/pull/23362#discussion_r1360172067
########## docs/content/docs/dev/datastream/dataset_migration.md: ########## @@ -0,0 +1,758 @@ +--- +title: "How To Migrate From DataSet to DataStream" +weight: 302 +type: docs +--- +<!-- +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. + +<table> + <tr> + <th>DataSet</th> + <th>DataStream</th> + </tr> + <tr> + <td> + <code style="white-space: pre-line;">// Create the execution environment + ExecutionEnvironment.getExecutionEnvironment(); + // Create the local execution environment + ExecutionEnvironment.createLocalEnvironment(); + // Create the collection environment + new CollectionEnvironment(); + // Create the remote environment + ExecutionEnvironment.createRemoteEnvironment(host, port, jarFile); + </code> + </td> + <td> + <code style="white-space: pre-line;">// Create the execution environment + StreamExecutionEnvironment.getExecutionEnvironment(); + // Create the local execution environment + StreamExecutionEnvironment.createLocalEnvironment(); + // The collection environment is not supported. + // Create the remote environment + StreamExecutionEnvironment.createRemoteEnvironment(host, port, jarFile); + </code> + </td> + </tr> +</table> + + +As the source of DataSet is always bounded, the execution mode must be set to RuntimeMode.BATCH to make Flink execute in batch mode. + +```java +StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment(); +executionEnvironment.setRuntimeMode(RuntimeExecutionMode.BATCH); +``` + +## Using the streaming sources and sinks + +Sources: The DataStream API uses `DataStreamSource` to read records from external system, while the DataSet API uses the +`DataSource`. + +Sinks: The DataStream API uses the implementations of `SinkFunction` and `Sink` to write records to external system, while the +DataSet API uses the `FileOutputFormat`. + +If you are looking for pre-defined source and sink connectors of DataStream, please check the [Connector Docs]({{< ref "docs/connectors/datastream/overview" >}}) + + +## 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 + +<table> + <tr> + <th>DataSet</th> + <th>DataStream</th> + </tr> + <tr> + <td> + <code style="white-space: pre-line;">dataSet.map(new MapFunction(){ + // implement user-defined map logic +}); Review Comment: Done. All operator names have been placed into the first column of table. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
