Flink Table API运用与UDF实现

本文使用Table Api实现word count,自定义UDF实现单词切割。

object TestUDFByWordCount {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build()
    val tableEnv = StreamTableEnvironment.create(env, settings)

    // source
    val stream= env.socketTextStream("flink101", 8888)

    val table = tableEnv.fromDataStream(stream, 'line)

    // 使用table api切割单词，自定义UDF
    val my_func = new MyFlatMapFunction
    // 把一行数据传入UDF进行切割,然后给列重新命名
    val re = table.flatMap(my_func('line)).as('word, 'word_c)
      .groupBy('word)  // 分组聚合
      .select('word, 'word_c.sum as 'cnt)

    tableEnv.toRetractStream[Row](re).filter(_._1 == true).print()

    tableEnv.execute("TestUDFByWordCount")
  }

  // 自定义UDF
  class MyFlatMapFunction extends TableFunction[Row] {
    // 定义函数处理之后的返回类型，输出单词和1
    override def getResultType: TypeInformation[Row] = Types.ROW(Types.STRING, Types.INT)

    // 函数主体
    def eval(str: String): Unit = {
      if (StringUtils.isNotEmpty(str)) str.trim.split(" ").foreach(word => {
        val row = new Row()
        row.setField(0, word)
        row.setField(1,1 )
        collect(row)
      })
    }
  }
}