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ScanTableSource
  override 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).isBounded
          case _ => 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