【源码分析】一个flink job的sql到底是如何执行的(一):flink sql底层是如何调用connector实现物理执行计划的

发布时间:2024年01月12日

我们以一条sql为例分析下flink sql与connector是如何配合执行的,本文我们先分析

  1. sql->sqlnode->validate->operation:是如何找到对应的connector实例的
  2. relnode->execGraph:是如何组装node为Graph,在哪找到connector实例的

之后的文章将会继续分析:

  1. translateToPlanInternal是如何串联connector其他方法的
  2. runtime时是如何调用connector执行方法的

另外:对于connector的spi机制、connector并行度的设置后续也都可以分析。

一. 一条flink sql

CREATE TABLE tjy_test1_ss  
(  
`id` int,  
`name` string,  
age string,  
`proc_time` AS `proctime`()  
) WITH (  
'password' = '11111111',  
'timestamp-format.standard' = 'SQL',  
'connector' = 'binlog-x',  
'port' = '3306',  
'cat' = 'insert',  
'host' = 'localhost',  
-- 'connection-charset' = 'utf-8',  
'url' = 'jdbc:mysql://localhost:3306/360test',  
'table' = 'test003',  
'username' = 'root',  
'timestamp'='1702881040000'  
);

CREATE TABLE tjy_fortest1  
(  
`id` int,  
`name` string,  
`face` string,  
PRIMARY KEY (id) NOT ENFORCED  
) WITH (  
'password' = '123456',  
'connector' = 'mysql-x',  
'sink.buffer-flush.interval' = '10000',  
'sink.buffer-flush.max-rows' = '1024',  
'sink.all-replace' = 'true',  
'table-name' = 'tjy_fortest1',  
'sink.parallelism' = '1',  
'url' = 'jdbc:mysql://xxx:3306/middle_test?useunicode=true&characterEncoding=utf8&useSSL=false&useCursorFetch=true',  
'username' = 'middle_test'  
);  
CREATE TABLE zzzzz_star01  
(  
`qq` int,  
`ww` string  
) WITH (  
'connector' = 'print'  
);

insert into zzzzz_star01 select t1.`id` as `qq` ,t2.`name` as `ww` from tjy_test1_ss t1 left join tjy_fortest1 FOR SYSTEM_TIME AS OF `t1`.`proc_time` t2 on t1.name=t2.name;

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二. 源码流程图示

1. flink connector的实现逻辑

flink connector 在代码层面从一个阶段到其他阶段的翻译过程。
在这里插入图片描述

阶段解释
元数据管理

执行CREATE TABLE时,会在目标catalog种更新元数据,比如上图黄色的create table语句。语句中with下的参数不会被校验和解释。DDL语句将会被解释成CatalogTable实例。

生成逻辑计划

接下来,当规划和优化一个job下的sql时,CatalogTable实例将会根据sql的语法具体解析为:当读select 语句时,会解析成DynamicTableSource;当读到insert into 语句时,解析成DynamicTableSink。

DynamicTableSource\SinkFactory将会提供具体的解析方法将CatalogTable解析为DynamicTableSource\Sink。具体的,比如说with下的port,Factory会从CatalogTable中校验port是否是连接器支持的参数,然后获取值,并创建参数实例,以便往下个阶段传输。

默认情况下,通过java的SPI机制发现工厂实例,工厂实例要定义一个能被校验的工厂标识符,例如上图:‘connector’ = ‘custom’。

DynamicTableSource\Sink 在运行时将会实际的读写数据。

运行时

当逻辑计划生成时,planner将会获取连接器的实现。运行时核心接口是,InputFormat和SourceFunction,按另一个抽象级别分组为ScanRuntimeProvider、 LookupRuntimeProvider和的子类SinkRuntimeProvider

小结:

  1. 对于一个create table语句会被更新到catalogTable实例中,语句中with下的参数不会被校验和解析。
  2. create table不会生成DynamicTable,而是select语句会生成DynamicTableSource,代表从物理表中拉取数据,insert into语句生成DynamicTableSink,代表写入数据到物理表。
  3. 在Planning期间,通过with下的connector参数获取对应的工厂,然后通过工厂校验、解析(with下的)参数值(比如’port’=‘5022’)最后封装成参数实例,并传递给runtime的实例;
  4. Runtime期间,根据参数实例定义的数据消费规则,开始真正的从物理表中处理(拉取\写入)数据。

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2. flink sql的转换逻辑

其次梳理下flink sql的整体源码流程
在这里插入图片描述

  1. 解析:通过calicite解析器生成AST即sqlnode
  2. 校验:校验sqlnode
  3. 生成逻辑计划:生成relroot封装成不同的operation(封装的算子细节)
  4. 优化逻辑计划:解析operation内部的relnode进行优化
  5. 生成物理执行计划
  6. 构建streamGraph异步执行

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三、flink sql 调用connector源码分析

1. tEnv.executeSql(stmt)

1.1. 概述

先回顾下tEnv.executeSql(stmt)执行时都发生了什么
在这里插入图片描述

在paser阶段:sql会从sqlNode再到operation的转变

  1. create table 语句:
    create table 语句会被维护到catalogTable实例中,以便insert 或 select 语句。注意语句中with下的参数不会被校验和解析。

  2. insert into语句:
    找into后的表会生成DynamicTableSink实例,代表写入数据到物理表。

  3. select语句:
    select语句代表query,找from后的表生成DynamicTableSource实例,代表从物理表中拉取数据。

ServiceLoader通过catalog元数据利用spi机制发现具体的实现类,然后加载具体的DynamicTable。

生成的DynamicTablexxx实例提供getScanRuntimeProvider、getLookupRuntimeProvider、getSinkRuntimeProvider等方法:用于后续translate阶段构建物理执行计划。

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代码调用链概述

tEnv.executeSql(stmt);
   ->List<Operation> operations = getParser().parse(statement);
   -> ParserImpl.parse:
   创建paser,并执行parse:javacc将sql转换为代码:SqlNodeList。这里略
      ->SqlToOperationConverter.convert(planner, catalogManager,
       parsed.get(0)):转换为operation:逻辑执行计划

parse的大致流程是:sql到sqlnode再到operation。
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1.2. convertValidatedSqlNode

这里我们忽略校验的过程,关注SqlToOperationConverter.convertValidatedSqlNode逻辑:即用于将sqlnode转换为operation。

如下代码主要是匹配不同的sqlnode进行operation的转换,这里我们集中关注,create、select、insert语句的converter逻辑。

private static Optional<Operation> convertValidatedSqlNode(  
        FlinkPlannerImpl flinkPlanner, CatalogManager catalogManager, SqlNode validated) {  
    SqlToOperationConverter converter =  
            new SqlToOperationConverter(flinkPlanner, catalogManager);

	...
	 else if (validated instanceof SqlCreateTable) {  
    ...
    return Optional.of(  
            converter.createTableConverter.convertCreateTable((SqlCreateTable) validated));
    ...
    } else if (validated instanceof RichSqlInsert) {  
    return Optional.of(converter.convertSqlInsert((RichSqlInsert) validated));
    ...
    } else if (validated.getKind().belongsTo(SqlKind.QUERY)) {  
    return Optional.of(converter.convertSqlQuery(validated));

}

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1.2.1. create语句

代码分析

SqlToOperationConverter.convertValidatedSqlNode
return Optional.of(  
        converter.createTableConverter.convertCreateTable((SqlCreateTable) validated));
        
->

Operation convertCreateTable(SqlCreateTable sqlCreateTable) {  
    sqlCreateTable.getTableConstraints().forEach(validateTableConstraint);  
    CatalogTable catalogTable = createCatalogTable(sqlCreateTable);  
  
    UnresolvedIdentifier unresolvedIdentifier =  
            UnresolvedIdentifier.of(sqlCreateTable.fullTableName());  
    ObjectIdentifier identifier = catalogManager.qualifyIdentifier(unresolvedIdentifier);  
  //封装一些属性:
  //identifier:
  //catalogTable实例
    return new CreateTableOperation(  
            identifier,  //`default_catalog`.`default_database`.`tjy_test1_ss`
            catalogTable,  // option
                           // partitionkey
                           // table schema:字段、主键、watermark
            sqlCreateTable.isIfNotExists(),  
            sqlCreateTable.isTemporary());  
}

create的sqlnode转换为operation:主要用于封装一些属性,如下:

  1. identifier:这里是:default_catalog.default_database.tjy_test1_ss
  2. catalogTable实例:option、 partitionkey、 table schema:字段、主键、watermark
    在这里插入图片描述

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1.2.2. insert语句

insert的sqlnode到Operation转换同样是封装了一些参数

 else if (validated instanceof RichSqlInsert) {  
    return Optional.of(converter.convertSqlInsert((RichSqlInsert) validated));

private Operation convertSqlInsert(RichSqlInsert insert) {  
    // Get sink table name.  
	...
    // Get sink table hints.  
	...
    PlannerQueryOperation query =  
            (PlannerQueryOperation)  
                    convertValidatedSqlNodeOrFail(  
                            flinkPlanner, catalogManager, insert.getSource());  
  
    return new SinkModifyOperation(  
            contextResolvedTable,   //表名
            query,                  //query:PlannerQueryOperation
            insert.getStaticPartitionKVs(),  
            insert.isOverwrite(),  
            dynamicOptions);  
}

ing
此例子insert into 后的表源码并未找sinksource的实现,只是将insert 后的select语句进行了实现,debug后发现在translate附近进行的实例初始化。

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1.2.3. query语句

query的语句包含了join,operation操作时生成了join左右两个表的DynamicTableSource实例。

} else if (validated.getKind().belongsTo(SqlKind.QUERY)) {  
    return Optional.of(converter.convertSqlQuery(validated));

...

private PlannerQueryOperation toQueryOperation(FlinkPlannerImpl planner, SqlNode validated) {  
    // transform to a relational tree  
    RelRoot relational = planner.rel(validated);  
    return new PlannerQueryOperation(relational.project());  
}

toQueryOperation时会将query语句转换为关系表达式,而RelRoot relational = planner.rel(validated); 的调用链是:rel->sqlToRelConverter.convertQuery->convertQueryRecursive

接着调用链往下看

...
switch (kind) {  
case SELECT:  
  return RelRoot.of(convertSelect((SqlSelect) query, top), kind);
...

->convertSelect->convertSelectImpl实现了select语句中各个sqlNode的转换,如下代码:


protected void convertSelectImpl(final Blackboard bb, SqlSelect select) {  
	// 转换from
    convertFrom(bb, select.getFrom());
    //转换where  
    convertWhere(bb, select.getWhere());  
  
    final List<SqlNode> orderExprList = new ArrayList<>();  
    final List<RelFieldCollation> collationList = new ArrayList<>();  
    gatherOrderExprs(bb, select, select.getOrderList(), orderExprList, collationList);  
    final RelCollation collation = cluster.traitSet().canonize(RelCollations.of(collationList));  
    //转换聚合
    if (validator.isAggregate(select)) {  
        convertAgg(bb, select, orderExprList);  
    } else {  
        convertSelectList(bb, select, orderExprList);  
    }  
    //转换:distinct
    if (select.isDistinct()) {  
        distinctify(bb, true);  
    }  
    //转换:order
    convertOrder(select, bb, collation, orderExprList, select.getOffset(), select.getFetch());  
    //转换hint
    if (select.hasHints()) {  
        ...
    } else {  
        bb.setRoot(bb.root, true);  
    }  
}

SqlSelect进入convertFrom逻辑进行匹配、迭代,直到遇见IDENTIFIER类型,会创建左右表的数据源实例,如下代码调用过程:

protected void convertFrom(Blackboard bb, SqlNode from, List<String> fieldNames) {  
...
	switch (from.getKind()) {
		case JOIN:  
		    convertJoin(bb, (SqlJoin) from);  
		    return;
	}


private void convertJoin(Blackboard bb, SqlJoin join) {
    final SqlValidatorScope scope = validator.getJoinScope(join);
    final Blackboard fromBlackboard = createBlackboard(scope, null, false);
    SqlNode left = join.getLeft();
    SqlNode right = join.getRight();
    ...
}


# SqlToRelConverter
private void convertIdentifier(。。。){
	...
	tableRel = toRel(table, hints);
	...
}

===>
#CatalogSourceTable.
toRel(){
...
	// create table source  
	final DynamicTableSource tableSource =  
	        createDynamicTableSource(context, catalogTable.getResolvedTable());  
	
	// prepare table source and convert to RelNode  
	return DynamicSourceUtils.convertSourceToRel(  
	        !schemaTable.isStreamingMode(),  
	        context.getTableConfig(),  
	        relBuilder,  
	        schemaTable.getContextResolvedTable(),  
	        schemaTable.getStatistic(),  
	        hints,  
	        tableSource);
}

其中,代码

final DynamicTableSource tableSource =  
	        createDynamicTableSource(context, catalogTable.getResolvedTable());  

通过spi机制(ing)发现具体连接器的实现:DynamicTableSource,创建DynamicTableSource实例,这里是binlog的,jdbc的就不列举了。

# BinlogDynamicTableFactory.
@Override  
public DynamicTableSource createDynamicTableSource(Context context) {  
    final FactoryUtil.TableFactoryHelper helper =  
            FactoryUtil.createTableFactoryHelper(this, context);

....
}

此时观察createDynamicTableSource的context参数就知道context是在paser阶段传递的。

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a. 找到connector的DynamicTableSource

找到数据源实现之后
在这里插入图片描述

接着调用DynamicSourceUtils.convertSourceToRel 将 DynamicTableSource 转换成 RelNode。

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当获取到DynamicTableSource时就有能力获取具体数据源的getScanRuntimeProvider、getLookupRuntimeProvider等方法。

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b. convertSourceToRel ing

继续看DynamicSourceUtils.convertSourceToRel的方法

public static RelNode convertSourceToRel(  
        boolean isBatchMode,  
        ReadableConfig config,  
        FlinkRelBuilder relBuilder,  
        ContextResolvedTable contextResolvedTable,  
        FlinkStatistic statistic,  
        List<RelHint> hints,  
        DynamicTableSource tableSource) {  
    final String tableDebugName = contextResolvedTable.getIdentifier().asSummaryString();  
    final ResolvedCatalogTable resolvedCatalogTable = contextResolvedTable.getResolvedTable();  
  
    // 1. prepare table source  
    prepareDynamicSource(  
            tableDebugName, resolvedCatalogTable, tableSource, isBatchMode, config);  
  
    // 2. push table scan  
    pushTableScan(isBatchMode, relBuilder, contextResolvedTable, statistic, hints, tableSource);  
  
    // 3. push project for non-physical columns  
    final ResolvedSchema schema = contextResolvedTable.getResolvedSchema();  
    if (!schema.getColumns().stream().allMatch(Column::isPhysical)) {  
        pushMetadataProjection(relBuilder, schema);  
        pushGeneratedProjection(relBuilder, schema);  
    }  
  
    // 4. push watermark assigner  
    if (!isBatchMode && !schema.getWatermarkSpecs().isEmpty()) {  
        pushWatermarkAssigner(relBuilder, schema);  
    }  
  
    return relBuilder.build();  
}

创建DynamicSource之前的准备:prepareDynamicSource

// 1. prepare table source  
prepareDynamicSource(  
        tableDebugName, resolvedCatalogTable, tableSource, isBatchMode, config);

//准备给定的DynamicTableSource 。它检查源是否与给定模式兼容并应用初始参数。
public static void prepareDynamicSource(  
        String tableDebugName,  
        ResolvedCatalogTable table,  
        DynamicTableSource source,  
        boolean isBatchMode,  
        ReadableConfig config) {  
    //获取数据源的schema
    final ResolvedSchema schema = table.getResolvedSchema();  
  
    validateAndApplyMetadata(tableDebugName, schema, source);  
  
    if (source instanceof ScanTableSource) {  
        validateScanSource(  
                tableDebugName, schema, (ScanTableSource) source, isBatchMode, config);  
    }  
  
    // lookup table source is validated in LookupJoin node  
}



private static void validateScanSource(  
        String tableDebugName,  
        ResolvedSchema schema,  
        ScanTableSource scanSource,  
        boolean isBatchMode,  
        ReadableConfig config) {  
        
    // getScanRuntimeProvider:用于离线任务时,provider的校验
    final ScanRuntimeProvider provider =  
            scanSource.getScanRuntimeProvider(ScanRuntimeProviderContext.INSTANCE);  
    final ChangelogMode changelogMode = scanSource.getChangelogMode();  
    //校验水印
    validateWatermarks(tableDebugName, schema);  
    
    if (isBatchMode) {  
        validateScanSourceForBatch(tableDebugName, changelogMode, provider);  
    } else {  
        validateScanSourceForStreaming(  
                tableDebugName, schema, scanSource, changelogMode, config);  
    }  
}

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2. statementSet.execute()

执行statementSet.execute(); 时有如下三个阶段

  1. 优化逻辑计划:解析operation内部的relnode进行优化
  2. 生成物理执行计划
  3. 构建streamGraph异步执行

如下代码:主要描述了flink优化逻辑计划、翻译成物理计划、转换为trans DAG的总体逻辑。

// PlannerBase.

override def translate(  
    modifyOperations: util.List[ModifyOperation]): util.List[Transformation[_]] = {  
  beforeTranslation()  
  if (modifyOperations.isEmpty) {  
    return List.empty[Transformation[_]]  
  }  
  
  val relNodes = modifyOperations.map(translateToRel) 
  //1.优化 
  val optimizedRelNodes = optimize(relNodes)  
  //2. 生成物理计划
  val execGraph = translateToExecNodeGraph(optimizedRelNodes, isCompiled = false)  
  //3. 翻译为transformations DAG。
  val transformations = translateToPlan(execGraph)  
  afterTranslation()  
  transformations  
}

再来回顾下二.2的部分流程图
在这里插入图片描述

接下来我们重点关注translateToExecNodeGraph转换为物理执行计划的逻辑

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2.1. translateToExecNodeGraph

translateToExecNodeGraph用于生成物理执行计划,如下代码:


private[flink] def translateToExecNodeGraph(  
    optimizedRelNodes: Seq[RelNode],  
    isCompiled: Boolean): ExecNodeGraph = {  
...
  // convert FlinkPhysicalRel DAG to ExecNodeGraph  
  val generator = new ExecNodeGraphGenerator()  
  //产生execGraph:物理执行计划
  val execGraph =  
generator.generate(optimizedRelNodes.map(_.asInstanceOf[FlinkPhysicalRel]), isCompiled)  
  
  // process the graph  :处理graph
  val context = new ProcessorContext(this)  
  val processors = getExecNodeGraphProcessors  
  processors.foldLeft(execGraph)((graph, processor) => processor.process(graph, context))  
}


//产生execGraph:物理执行计划
private ExecNode<?> generate(FlinkPhysicalRel rel, boolean isCompiled) {  
。。。
    //获取所有的input数据源
    for (RelNode input : rel.getInputs()) {  
        inputNodes.add(generate((FlinkPhysicalRel) input, isCompiled));  
    }  
    // 找到各个逻辑执行计划节点的物理实现类
    execNode = rel.translateToExecNode(isCompiled);  
    // connects the input nodes:链接所有的逻辑执行计划节点,形成DAG? 
    List<ExecEdge> inputEdges = new ArrayList<>(inputNodes.size());  
    for (ExecNode<?> inputNode : inputNodes) {  inputEdges.add(ExecEdge.builder().source(inputNode).target(execNode).build());  
    }  
。。。  
    return execNode;  
}

上述代码,获取逻辑执行计划的所有节点,找到各个逻辑执行计划节点的物理实现类,然后串联成物理执行计划。
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接下来我们看各个节点物理执行计划节点实现类:

如下是物理执行计划转化的基类

FlinkPhysicalRel.
def translateToExecNode(isCompiled: Boolean): ExecNode[_] = {  
  val execNode = translateToExecNode()  
  execNode.setCompiled(isCompiled)  
  execNode  
}

接着看source、sink、lookup的实现类

实现类1:StreamPhysicalTableSourceScan

org.apache.flink.table.planner.plan.nodes.physical.stream

//实现类:StreamPhysicalTableSourceScan
//Stream physical RelNode to read data from an external source defined by a org.apache.flink.table.connector.source.ScanTableSource.

override def translateToExecNode(): ExecNode[_] = {  
  val tableSourceSpec = new DynamicTableSourceSpec(  
    tableSourceTable.contextResolvedTable,  
    util.Arrays.asList(tableSourceTable.abilitySpecs: _*))  
// 这里的tableSource:是在parser阶段已经实现的
  tableSourceSpec.setTableSource(tableSource)  
  
  new StreamExecTableSourceScan(  
    unwrapTableConfig(this),  
    tableSourceSpec,  
    FlinkTypeFactory.toLogicalRowType(getRowType),  
    getRelDetailedDescription)  
}

tableSource:是在parser阶段已经实现的实例,这里以BinlogDynamicTableSource为例
在这里插入图片描述

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实现类2:StreamPhysicalLookupJoin

override def translateToExecNode(): ExecNode[_] = {  
  val (projectionOnTemporalTable, filterOnTemporalTable) = calcOnTemporalTable match {  
    case Some(program) =>  
      val (projection, filter) = FlinkRexUtil.expandRexProgram(program)  
      (JavaScalaConversionUtil.toJava(projection), filter.orNull)  
    case _ =>  
      (null, null)  
  }  
  
  new StreamExecLookupJoin(  
    tableConfig,  
    JoinTypeUtil.getFlinkJoinType(joinType),  
    remainingCondition.orNull,  
    new TemporalTableSourceSpec(temporalTable),  
    allLookupKeys.map(item => (Int.box(item._1), item._2)).asJava,  
    projectionOnTemporalTable,  
    filterOnTemporalTable,  
    lookupKeyContainsPrimaryKey(),  
    upsertMaterialize,  
    asyncOptions.orNull,  
    retryOptions.orNull,  
    inputChangelogMode,  
    InputProperty.DEFAULT,  
    FlinkTypeFactory.toLogicalRowType(getRowType),  
    getRelDetailedDescription)  
}
 /** Stream physical RelNode for [[Calc]]. */ 

class StreamPhysicalCalc(

}

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实现类3:StreamPhysicalSink
/**  
 * Stream physical RelNode to to write data into an external sink defined by a [[DynamicTableSink]].  
 */
 class StreamPhysicalSink(){
...

override def translateToExecNode(): ExecNode[_] = {  
  val inputChangelogMode =  
    ChangelogPlanUtils.getChangelogMode(getInput.asInstanceOf[StreamPhysicalRel]).get  
    
  val tableSinkSpec =  
    new DynamicTableSinkSpec(contextResolvedTable, util.Arrays.asList(abilitySpecs: _*))  
  tableSinkSpec.setTableSink(tableSink)  
  // no need to call getUpsertKeysInKeyGroupRange here because there's no exchange before sink,  
  // and only add exchange in exec sink node.  val inputUpsertKeys = FlinkRelMetadataQuery  
    .reuseOrCreate(cluster.getMetadataQuery)  
    .getUpsertKeys(inputRel)  
  
  new StreamExecSink(  
    unwrapTableConfig(this),  
    tableSinkSpec,  
    inputChangelogMode,  
    InputProperty.DEFAULT,  
    FlinkTypeFactory.toLogicalRowType(getRowType),  
    upsertMaterialize,  
    UpsertKeyUtil.getSmallestKey(inputUpsertKeys),  
    getRelDetailedDescription)  
}
 

tableSinkSpec为具体sink实现类,为PrintTableSinkFactory。

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2.2. translateToPlan

org.apache.flink.table.planner.delegation.PlannerBase
。。。
//Translates an ExecNodeGraph into a Transformation DAG.
val transformations = translateToPlan(execGraph)

PlannerBase有两个实现类:BatchPlanner、StreamPlanner,我们这里是StreamPlanner,如下

//StreamPlanner
override protected def translateToPlan(execGraph: ExecNodeGraph): util.List[Transformation[_]] = {  
  beforeTranslation()  
  val planner = createDummyPlanner()  
  //遍历每一个node转换为transformations
  val transformations = execGraph.getRootNodes.map {  
    case node: StreamExecNode[_] => node.translateToPlan(planner)  
    case _ =>  
      throw new TableException(  
        "Cannot generate DataStream due to an invalid logical plan. " +  
          "This is a bug and should not happen. Please file an issue.")  
  }  
  //
  afterTranslation()  
  transformations ++ planner.extraTransformations  
}

node.translateToPlan: 翻译node为 Transformation。其中node: 一个物理执行节点(FlinkPhysicalRel)

先跳到基类,然后找具体实现

//org.apache.flink.table.planner.plan.nodes.exec.ExecNodeBase
public final Transformation<T> translateToPlan(Planner planner) {  
    if (transformation == null) {  
        transformation =  
                translateToPlanInternal(  
                        (PlannerBase) planner,  
                        ExecNodeConfig.of(  
                                ((PlannerBase) planner).getTableConfig(),  
                                persistedConfig,  
                                isCompiled));  
        if (this instanceof SingleTransformationTranslator) {  
            if (inputsContainSingleton()) {  
                transformation.setParallelism(1);  
                transformation.setMaxParallelism(1);  
            }  
        }  
    }  
    return transformation;  
}

//接口
protected abstract Transformation<T> translateToPlanInternal(  
        PlannerBase planner, ExecNodeConfig config);

这里较为复杂,篇幅问题,暂时不详细分析(ing)。

目前可以确定的是在translateToPlanInternal中source、sink、lookup等操作,都会找到对应连接器的实现。

?

参考:
https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sourcessinks/
flink 1.16.1的相关源码

文章来源:https://blog.csdn.net/hiliang521/article/details/135515711
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