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Apache SeaTunnel Flink引擎执行流程源码分析

news 来源：原创 2025/6/24 8:17:10

1. 任务启动入口

2. 任务执行命令类：FlinkTaskExecuteCommand

3. FlinkExecution的创建与初始化

3.1 核心组件初始化

3.2 关键对象说明

4. 任务执行：FlinkExecution.execute()

5. Source处理流程

5.1 插件初始化

5.2 数据流生成

6. Transform处理流程

6.1 插件初始化

6.2 转换执行

7. Sink处理流程

7.1 插件初始化

7.2 数据输出

执行流程全景图

关键设计总结

本文基于SeaTunnel 2.3.x源码分析Flink引擎执行流程，以seatunnel-examples/seatunnel-flink-connector-v2-example/src/main/java/org/apache/seatunnel/example/flink/v2/SeaTunnelApiExample.java为入口，完整解析Flink引擎的执行流程。

1. 任务启动入口

启动类核心代码：

// 1. 初始化Flink启动命令参数
FlinkCommandArgs flinkCommandArgs = new FlinkCommandArgs();

// 2. 执行SeaTunnel.run()回调Flink执行命令
SeaTunnel.run(flinkCommandArgs.buildCommand());

buildCommand()返回FlinkTaskExecuteCommand实例
SeaTunnel.run()最终调用FlinkTaskExecuteCommand.execute()

2. 任务执行命令类：FlinkTaskExecuteCommand

核心执行流程：

public void execute() {// 1. 解析配置文件生成Config对象Config config = ConfigBuilder.of(configFile);// 2. 创建FlinkExecution实例FlinkExecution seaTunnelTaskExecution = new FlinkExecution(config);// 3. 执行任务seaTunnelTaskExecution.execute();
}

3. FlinkExecution的创建与初始化

3.1 核心组件初始化

public FlinkExecution(Config config) {// 创建三大处理器this.sourcePluginExecuteProcessor = new SourceExecuteProcessor(jarPaths, config.getConfigList(Constants.SOURCE), jobContext);this.transformPluginExecuteProcessor = new TransformExecuteProcessor(jarPaths, TypesafeConfigUtils.getConfigList(config, Constants.TRANSFORM, Collections.emptyList()),jobContext);this.sinkPluginExecuteProcessor = new SinkExecuteProcessor(jarPaths, config.getConfigList(Constants.SINK), jobContext);// 初始化Flink执行环境this.flinkRuntimeEnvironment = FlinkRuntimeEnvironment.getInstance(this.registerPlugin(config, jarPaths));// 为处理器注入运行时环境this.sourcePluginExecuteProcessor.setRuntimeEnvironment(flinkRuntimeEnvironment);this.transformPluginExecuteProcessor.setRuntimeEnvironment(flinkRuntimeEnvironment);this.sinkPluginExecuteProcessor.setRuntimeEnvironment(flinkRuntimeEnvironment);
}

3.2 关键对象说明

组件	类型	功能
`sourcePluginExecuteProcessor`	`SourceExecuteProcessor`	处理数据源接入
`transformPluginExecuteProcessor`	`TransformExecuteProcessor`	处理数据转换逻辑
`sinkPluginExecuteProcessor`	`SinkExecuteProcessor`	处理数据输出
`flinkRuntimeEnvironment`	`FlinkRuntimeEnvironment`	封装Flink StreamExecutionEnvironment

4. 任务执行：FlinkExecution.execute()

DAG构建流程：

public void execute() {// 初始化数据流集合List<DataStream<Row>> dataStreams = new ArrayList<>();// 按顺序执行三大组件dataStreams = sourcePluginExecuteProcessor.execute(dataStreams);dataStreams = transformPluginExecuteProcessor.execute(dataStreams);sinkPluginExecuteProcessor.execute(dataStreams);
}

5. Source处理流程

5.1 插件初始化

调用链：

SourceExecuteProcessor()→ super(jarPaths, pluginConfigs, jobContext) // 调用父类构造器→ this.plugins = initializePlugins(jarPaths, pluginConfigs)

插件加载核心逻辑：

protected List<SeaTunnelSource> initializePlugins(...) {SeaTunnelSourcePluginDiscovery discovery = new SeaTunnelSourcePluginDiscovery();List<SeaTunnelSource> sources = new ArrayList<>();for (Config sourceConfig : pluginConfigs) {// 1. 识别插件类型PluginIdentifier identifier = PluginIdentifier.of(ENGINE_TYPE, PLUGIN_TYPE, sourceConfig.getString(PLUGIN_NAME));// 2. 加载依赖JARjars.addAll(discovery.getPluginJarPaths(Lists.newArrayList(identifier)));// 3. 创建插件实例SeaTunnelSource source = discovery.createPluginInstance(identifier);// 4. 初始化插件source.prepare(sourceConfig);source.setJobContext(jobContext);// 5. 批处理模式校验if (jobContext.getJobMode() == JobMode.BATCH && source.getBoundedness() == Boundedness.UNBOUNDED) {throw new UnsupportedOperationException("Unbounded source not support batch mode");}sources.add(source);}jarPaths.addAll(jars);return sources;
}

5.2 数据流生成

执行入口：

public List<DataStream<Row>> execute(List<DataStream<Row>> upstreamDataStreams) {StreamExecutionEnvironment env = flinkRuntimeEnvironment.getStreamExecutionEnvironment();List<DataStream<Row>> sources = new ArrayList<>();for (int i = 0; i < plugins.size(); i++) {SeaTunnelSource internalSource = plugins.get(i);// 1. 创建SourceFunctionBaseSeaTunnelSourceFunction sourceFunction;if (internalSource instanceof SupportCoordinate) {sourceFunction = new SeaTunnelCoordinatedSource(internalSource);} else {sourceFunction = new SeaTunnelParallelSource(internalSource); // Flink默认路径}// 2. 创建DataStreamSourceDataStreamSource<Row> sourceStream = addSource(env,sourceFunction,"SeaTunnel " + internalSource.getClass().getSimpleName(),internalSource.getBoundedness() == Boundedness.BOUNDED);// 3. 设置并行度if (pluginConfig.hasPath(CommonOptions.PARALLELISM.key())) {sourceStream.setParallelism(pluginConfig.getInt(CommonOptions.PARALLELISM.key()));}// 4. 注册结果表registerResultTable(pluginConfig, sourceStream);sources.add(sourceStream);}return sources;
}

结果表注册逻辑：

void registerResultTable(Config config, DataStream<Row> dataStream) {if (config.hasPath(RESULT_TABLE_NAME)) {String tableName = config.getString(RESULT_TABLE_NAME);StreamTableEnvironment tableEnv = getStreamTableEnvironment();if (!TableUtil.tableExists(tableEnv, tableName)) {if (config.hasPath("field_name")) {// 带字段名的注册方式tableEnv.registerDataStream(tableName, dataStream, config.getString("field_name"));} else {// 默认注册方式tableEnv.registerDataStream(tableName, dataStream);}}}
}

6. Transform处理流程

6.1 插件初始化

关键校验逻辑：

public void prepare(Config pluginConfig) {// 必须包含source_table_name和result_table_nameif (!pluginConfig.hasPath(SOURCE_TABLE_NAME) || !pluginConfig.hasPath(RESULT_TABLE_NAME)) {throw new IllegalArgumentException("Missing required table name config");}// 输入输出表名不能相同if (Objects.equals(pluginConfig.getString(SOURCE_TABLE_NAME),pluginConfig.getString(RESULT_TABLE_NAME)) {throw new IllegalArgumentException("Source and result table names must be different");}this.inputTableName = pluginConfig.getString(SOURCE_TABLE_NAME);this.outputTableName = pluginConfig.getString(RESULT_TABLE_NAME);// 调用具体Transform的配置初始化setConfig(pluginConfig);
}

6.2 转换执行

核心处理流程：

public List<DataStream<Row>> execute(List<DataStream<Row>> upstreamDataStreams) {DataStream<Row> input = upstreamDataStreams.get(0); // 默认使用第一个上游流for (int i = 0; i < plugins.size(); i++) {SeaTunnelTransform transform = plugins.get(i);Config pluginConfig = pluginConfigs.get(i);// 1. 获取输入流（通过source_table_name查找）DataStream<Row> stream = fromSourceTable(pluginConfig).orElse(input);// 2. 执行转换input = flinkTransform(transform, stream);// 3. 注册结果表registerResultTable(pluginConfig, input);}return Collections.singletonList(input);
}

转换算子实现：

protected DataStream<Row> flinkTransform(SeaTunnelTransform transform, DataStream<Row> stream) {// 类型系统转换SeaTunnelDataType inputType = TypeConverterUtils.convert(stream.getType());transform.setTypeInfo(inputType);// 创建行转换器FlinkRowConverter inputConverter = new FlinkRowConverter(inputType);FlinkRowConverter outputConverter = new FlinkRowConverter(transform.getProducedType());// 通过flatMap实现转换逻辑return stream.flatMap(new FlatMapFunction<Row, Row>() {@Overridepublic void flatMap(Row value, Collector<Row> out) {// 类型转换SeaTunnelRow inRow = inputConverter.reconvert(value);// 执行Transform核心逻辑SeaTunnelRow outRow = (SeaTunnelRow) transform.map(inRow);if (outRow != null) {// 输出类型转换Row result = outputConverter.convert(outRow);out.collect(result);}}}, TypeConverterUtils.convert(transform.getProducedType()));
}

7. Sink处理流程

7.1 插件初始化

特殊处理逻辑：

protected List<SeaTunnelSink> initializePlugins(...) {for (Config sinkConfig : pluginConfigs) {SeaTunnelSink sink = discovery.createPluginInstance(identifier);sink.prepare(sinkConfig);// 数据保存模式处理if (sink instanceof SupportDataSaveMode) {SupportDataSaveMode saveModeSink = (SupportDataSaveMode) sink;saveModeSink.checkOptions(sinkConfig); // 校验配置}}
}

7.2 数据输出

执行流程：

public List<DataStream<Row>> execute(List<DataStream<Row>> upstreamDataStreams) {DataStream<Row> input = upstreamDataStreams.get(0);for (int i = 0; i < plugins.size(); i++) {Config sinkConfig = pluginConfigs.get(i);SeaTunnelSink sink = plugins.get(i);// 1. 获取输入流DataStream<Row> stream = fromSourceTable(sinkConfig).orElse(input);// 2. 设置类型信息sink.setTypeInfo((SeaTunnelRowType) TypeConverterUtils.convert(stream.getType()));// 3. 处理数据保存模式if (sink instanceof SupportDataSaveMode) {SupportDataSaveMode saveModeSink = (SupportDataSaveMode) sink;DataSaveMode saveMode = saveModeSink.getDataSaveMode();saveModeSink.handleSaveMode(saveMode); // 处理保存模式}// 4. 适配Flink Sink APIDataStreamSink<Row> dataStreamSink = stream.sinkTo(SinkV1Adapter.wrap(new FlinkSink<>(sink))).name(sink.getPluginName());// 5. 设置并行度if (sinkConfig.hasPath(CommonOptions.PARALLELISM.key())) {dataStreamSink.setParallelism(sinkConfig.getInt(CommonOptions.PARALLELISM.key()));}}return null; // Sink是终点，无下游数据流
}

FlinkSink适配器伪代码：

class FlinkSink implements SinkFunction<Row> {private final SeaTunnelSink<SeaTunnelRow, ?, ?, ?> sink;public void invoke(Row value) {// 将Flink Row转换为SeaTunnelRowSeaTunnelRow row = converter.convert(value);// 调用SeaTunnel Sink写入逻辑sink.write(row);}
}

执行流程全景图

关键设计总结

插件化架构：
- 通过SPI机制动态加载Source/Transform/Sink插件
- 插件发现机制：SeaTunnel*PluginDiscovery
表名映射机制：
- source_table_name和result_table_name构成DAG链路
- 通过registerDataStream()实现表注册
类型系统转换：
- TypeConverterUtils处理SeaTunnel类型与Flink类型的转换
- FlinkRowConverter实现行数据双向转换
执行环境封装：
- FlinkRuntimeEnvironment统一管理执行环境
- 兼容批流一体：通过JobMode控制执行模式
适配器模式：
- SeaTunnelParallelSource适配SeaTunnelSource到Flink SourceFunction
- FlinkSink适配SeaTunnelSink到Flink SinkFunction