代码

package com.zjc.flow_analysis.hotitems_analysis

import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.scala._
import org.apache.flink.table.api.{EnvironmentSettings, Slide}
import org.apache.flink.table.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer

import java.sql.Timestamp
import java.util.Properties

object HotItemsSQL {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)

    val properties = new Properties()
    properties.setProperty("bootstrap.servers", "hadoop103:9092")
    properties.setProperty("group.id", "consumer-group")
    properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    properties.setProperty("auto.offset.reset", "latest")

    val inputStream = env.addSource(new FlinkKafkaConsumer[String]("hotItems", new SimpleStringSchema(), properties))
    val dataStream = inputStream.map(data => {
      val arrayData = data.split(",")
      UserBehavior(arrayData(0).toLong, arrayData(1).toLong, arrayData(2).toLong, arrayData(3).toString,arrayData(4).toLong)
    }).assignAscendingTimestamps(_.timestamp * 1000L)

    val settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build()
    val tableEnv = StreamTableEnvironment.create(env, settings)
    // 将dataStream转为表
    tableEnv.createTemporaryView("dataTable",dataStream, 'itemId, 'behavior, 'timestamp.rowtime as 'ts)
    val resultTalbe = tableEnv.sqlQuery(
      """
        |select *
        |from (
        | select *,
        |  row_number() over(partition by windowEnd order by cnt desc) as row_num
        |  from (
        |  select itemId, count(itemId) as cnt,
        |         hop_end(ts, interval '5' minute, interval '1' hour) as windowEnd
        |  from dataTable
        |  where behavior='pv'
        |  group by itemId, hop(ts, interval '5' minute, interval '1' hour)
        |  )
        |)
        |where row_num <= 5
        |""".stripMargin
    )
    resultTalbe.toRetractStream[(Long, Long,Timestamp, Long)].print("result")
    env.execute("商品热门统计(sql版实现)")

  }
}

输出，部分截图：

注意：sql中用单引号，如behavior=‘pv’，如果用双引号sql解析会有问题。
HOP(time_attr, interval, interval)定义一个滑动窗口，第一个参数是时间字段，第二个参数是窗口滑动步长，第三个是窗口长度。
HOP_END(time_attr, interval, interval)定义一个滑动窗口，第一个参数是时间字段，第二个参数是窗口滑动步长，第三个是窗口长度，返回的窗口右边界时间戳。
官网解释下图：