解析 flink sql 转化成flink job

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背景

在很多计算引擎里，都会把sql 这种标准语言，转成计算引擎下底层实际的算子，因此理解此转换的流程对于理解整个过程非常重要

流程

flink实例

public class BatchExample {public static void main(String[] args) {// 设置执行环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();EnvironmentSettings settings = EnvironmentSettings.newInstance().inBatchMode().build();StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);// 创建一个内置示例源表String sourceDDL = "CREATE TABLE users (\n" +"    id INT,\n" +"    name STRING,\n" +"    age INT\n" +") WITH (\n" +"    'connector' = 'filesystem',\n" +"    'path' = 'file:///Users/leishuiyu/IdeaProjects/SpringFlink/data.csv',\n" +"    'format' = 'csv'\n" +");";tableEnv.executeSql(sourceDDL);Table table = tableEnv.sqlQuery("select * from users limit 1 ");String explanation = tableEnv.explainSql("select * from users limit 1 ");System.out.println(explanation);table.execute().print();}
}

输出结果

== Abstract Syntax Tree ==
LogicalSort(fetch=[1])
+- LogicalProject(id=[$0], name=[$1], age=[$2])+- LogicalTableScan(table=[[default_catalog, default_database, users]])== Optimized Physical Plan ==
Limit(offset=[0], fetch=[1], global=[true])
+- Exchange(distribution=[single])+- Limit(offset=[0], fetch=[1], global=[false])+- TableSourceScan(table=[[default_catalog, default_database, users, limit=[1]]], fields=[id, name, age])== Optimized Execution Plan ==
Limit(offset=[0], fetch=[1], global=[true])
+- Exchange(distribution=[single])+- Limit(offset=[0], fetch=[1], global=[false])+- TableSourceScan(table=[[default_catalog, default_database, users, limit=[1]]], fields=[id, name, age])

实现细节

主要是三个地方，在优化那一步，就把原生的relnode 转化成了自定义的relnode,自定义的relnode 就可以带物理转化的内容了，比如上面的LogicalTableScan 转成BatchPhysicalTableSourceScan 这个relnode

定义的规则

class BatchPhysicalTableSourceScanRule(config: Config) extends ConverterRule(config) {/** Rule must only match if TableScan targets a bounded [[ScanTableSource]] *///规则只匹配有界的ScanTableSourceoverride def matches(call: RelOptRuleCall): Boolean = {val scan: TableScan = call.rel(0).asInstanceOf[TableScan]val tableSourceTable = scan.getTable.unwrap(classOf[TableSourceTable])tableSourceTable match {case tst: TableSourceTable =>tst.tableSource match {case sts: ScanTableSource =>sts.getScanRuntimeProvider(ScanRuntimeProviderContext.INSTANCE).isBoundedcase _ => false}case _ => false}}def convert(rel: RelNode): RelNode = {val scan = rel.asInstanceOf[FlinkLogicalTableSourceScan]val newTrait = rel.getTraitSet.replace(FlinkConventions.BATCH_PHYSICAL)//在这里转成自定义的relnode new BatchPhysicalTableSourceScan(rel.getCluster,newTrait,scan.getHints,scan.getTable.asInstanceOf[TableSourceTable])}
}

定义的物理算子

也是一个relnode，实现类BatchPhysicalTableSourceScan

class BatchPhysicalTableSourceScan(cluster: RelOptCluster,traitSet: RelTraitSet,hints: util.List[RelHint],tableSourceTable: TableSourceTable)extends CommonPhysicalTableSourceScan(cluster, traitSet, hints, tableSourceTable)with BatchPhysicalRel {
//主要是这个方法，转成 flink exec算子override def translateToExecNode(): ExecNode[_] = {val tableSourceSpec = new DynamicTableSourceSpec(tableSourceTable.contextResolvedTable,util.Arrays.asList(tableSourceTable.abilitySpecs: _*))tableSourceSpec.setTableSource(tableSourceTable.tableSource)new BatchExecTableSourceScan(unwrapTableConfig(this),tableSourceSpec,FlinkTypeFactory.toLogicalRowType(getRowType),getRelDetailedDescription)}
}

定义的flink exec node

BatchExecTableSourceScan 类

 /// 主要是这个方法，看下下面的实现就比较熟悉了public Transformation<RowData> createInputFormatTransformation(StreamExecutionEnvironment env,InputFormat<RowData, ?> inputFormat,InternalTypeInfo<RowData> outputTypeInfo,String operatorName) {// env.createInput will use ContinuousFileReaderOperator, but it do not support multiple// paths. If read partitioned source, after partition pruning, we need let InputFormat// to read multiple partitions which are multiple paths.// We can use InputFormatSourceFunction directly to support InputFormat.final InputFormatSourceFunction<RowData> function =new InputFormatSourceFunction<>(inputFormat, outputTypeInfo);return env.addSource(function, operatorName, outputTypeInfo).getTransformation();}

这里的转换是多种方式，一种是现成的比如source 这种，还有的是函数这种，要通过代码生成的方法实现。flink代码生成

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