FlinkSQL源码解析（三）执行流程

1、前言

        前面2部分主要是介绍以下2点：

1. flink sql整体的执行流程大致为：sqlNode --> Operation --> RelNode --> 优化 --> execNode --> Transformation。
2. 以及flink sql源码解析需要使用到的java spi。

         现在具体来看看flink sql 在其内部转换的实现步骤，就是如何去调用连接器，主要是在sqlNode --> RelNode这一步。

        当执行建表语句，主要是进行语法的校验。真正把输入源、输出源连接在一起执行的，还是通过insert语句。即当我们在定义输入表或者输出表，有错误的定义connecter='abc'，也不会在执行建表语句时返回错误，而是在执行插入语句时报错。这里以一份简单的代码为例：

 def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(10)
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)

    val settings = EnvironmentSettings.newInstance()
      .useBlinkPlanner()
      .inStreamingMode()
      .build()

    val tableEnv = StreamTableEnvironment.create(env, settings)
    tableEnv.getConfig.setLocalTimeZone(ofHours(8))
    tableEnv.getConfig().getConfiguration().setBoolean("table.exec.emit.early-fire.enabled", true)
    tableEnv.getConfig().getConfiguration().setString("table.exec.emit.early-fire.delay", "5000ms")

    tableEnv.executeSql(
      """
        |CREATE TABLE input (
        |    userId STRING,
        |    pageId STRING,
        |    sign STRING,
        |    proctime AS PROCTIME(),   -- generates processing-time attribute using computed column
        |    eventTime TIMESTAMP(3),
        |    WATERMARK FOR eventTime AS eventTime - INTERVAL '5' SECOND  -- defines watermark on ts column, marks ts as event-time attribute
        |) WITH (
        |    'connector' = 'kafka',  -- using kafka connector
        |    'topic' = 'flinksource',  -- kafka topic
        |    'scan.startup.mode' = 'latest-offset',  -- reading from the latest
        |    'properties.bootstrap.servers' = 'hadoop1:9092,hadoop2:9092,hadoop3:9092',  -- kafka broker address
        |    'format' = 'json'  -- the data format is json
        |)
        |
      """.stripMargin)

    tableEnv.executeSql(
      """
        |CREATE TABLE output (
        |    userId STRING,
        |    pageId STRING,
        |    cnt BIGINT,
        |    startTime timestamp,
        |    endTime timestamp
        |) WITH (
        |    'connector' = 'print'
        |)
        |
      """.stripMargin)

    tableEnv.executeSql(
      """
        | insert into output
        | select
        | userId,
        | pageId,
        | count(*) as cnt,
        | HOP_START(eventTime, interval '1' HOUR, interval '1' DAY) as startTime,
        | HOP_END(eventTime, interval '1' HOUR, interval '1' DAY) as endTime
        |from (
        | select * from input where sign = 'error'
        |) a group by userId,pageId, hop(eventTime, interval '1' HOUR, interval '1' DAY)
      """.stripMargin)
    env.execute("insertDemo")
  }

2.