TemporalTable
关于时态表的介绍可以看看flink中文社区的这篇文章Flink SQL 如何实现数据流的 Join？还有该篇博文Flink Table & SQL 时态表Temporal Table
append表(追加表)关联时态表数据，进行流join操作（时态表可以减少时态表中保存的状态）
import java.util.Properties
import com.alibaba.fastjson.{JSON, JSONObject}
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.api.common.time.Time
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
import org.apache.flink.table.api.scala.StreamTableEnvironment
import org.apache.flink.table.api.scala._
import org.apache.flink.table.functions.TemporalTableFunction
object FlinkTemporalTable {
def main(args: Array[String]): Unit = {
// 获取流处理执行坏境
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
// 通过流处理执行引擎构建表执行引擎
val tableEnv: StreamTableEnvironment = StreamTableEnvironment.create(env)
val props = new Properties()
props.setProperty("bootstrap.servers", "xxx.xxx.xxx.xxx:9092")
props.setProperty("auto.offset.reset", "latest") // 设置消费起点 earliest,latest
props.setProperty("group.id", "local_consumer")
val foTopic = "order"
val fcdv2Topic = "deal"
// 获取到原始数据
val foOriginalStream: DataStream[String] = env.addSource(new FlinkKafkaConsumer[String](foTopic, new SimpleStringSchema(), props))
// {"order_id":"order_01","order_no":"0001","date_create":"1584497993601","date_update":"1584497993601"}
val foStream: DataStream[(String, String, Long, Long)] = foOriginalStream
// 解析原始数据（json格式）
.map { json =>
val jsonObj: JSONObject = JSON.parseObject(json)
(jsonObj.getString("order_id"), jsonObj.getString("order_no"), jsonObj.getLongValue("date_create"), jsonObj.getLongValue("date_update"))
}
.assignAscendingTimestamps(tp => tp._4) // 设置时间水位线
// {"car_deal_id":"car_01","sale_order_no":"0001","car_attribute_id":"A0001","vin":"a1000","date_create":"1584497993601","date_update":"1584497993601"}
val fcdv2OriginalStream: DataStream[String] = env.addSource(new FlinkKafkaConsumer[String](fcdv2Topic, new SimpleStringSchema(), props))
val fcdv2Stream: DataStream[(String, String, String, String, Long, Long)] = fcdv2OriginalStream
.map { json =>
val jsonObj: JSONObject = JSON.parseObject(json)
(jsonObj.getString("car_deal_id"), jsonObj.getString("sale_order_no"), jsonObj.getString("car_attribute_id"), jsonObj.getString("vin"), jsonObj.getLongValue("date_create"), jsonObj.getLongValue("date_update"))
}
.assignAscendingTimestamps(tp => tp._6) // 设置时间水位线
// 注册成表
tableEnv.registerDataStream("fo", foStream, 'order_id, 'order_no, 'date_create, 'date_update, 'foRowtime.rowtime)
tableEnv.registerDataStream("fcdv2", fcdv2Stream, 'car_deal_id, 'sale_order_no, 'car_attribute_id, 'vin, 'date_create, 'date_update, 'fcdv2Rowtime.rowtime)
// 设置Temporal Table的时间属性和主键
val fcdv2TemporalFunction: TemporalTableFunction = tableEnv.scan("fcdv2").createTemporalTableFunction("fcdv2Rowtime", "sale_order_no");
//注册TableFunction
tableEnv.registerFunction("FCDV2_TEMPORAL_FUNCTION", fcdv2TemporalFunction)
// 运行SQL
val sql =
"""
|SELECT fo.`order_no` AS `order_no`
| , fo.`date_create` AS `fo_date_create`
| , fo.`date_update` AS `fo_date_update`
| , fcdv2.`car_attribute_id` AS `fcdv2_car_attribute_id`
| , fcdv2.`vin` AS `fcdv2_vin`
| , fcdv2.`date_create` AS `fcdv2_date_create`
| , fcdv2.`date_update` AS `fcdv2_date_update`
|FROM fo
| , LATERAL TABLE(FCDV2_TEMPORAL_FUNCTION(fo.foRowtime)) as fcdv2
|WHERE fo.order_no = fcdv2.sale_order_no
|""".stripMargin
val table = tableEnv.sqlQuery(sql)
tableEnv.getConfig.setIdleStateRetentionTime(Time.minutes(1L), Time.minutes(7L)) // 结合使用状态清理
tableEnv.toAppendStream[(String, Long, Long, String, String, Long, Long)](table) // 添加流
.print()
//6、开始执行
tableEnv.execute(FlinkTemporalTable.getClass.getSimpleName)
}
}
input
-- order
{"order_id":"order_01","order_no":"0001","date_create":"1584497993601","date_update":"1584497993601"}
{"order_id":"order_02","order_no":"0002","date_create":"1584497994602","date_update":"1584497994602"}
{"order_id":"order_03","order_no":"0003","date_create":"1584497995603","date_update":"1584497995603"}
{"order_id":"order_04","order_no":"0004","date_create":"1584497997604","date_update":"1584497997604"}
{"order_id":"order_05","order_no":"0005","date_create":"1584497998605","date_update":"1584497998605"}
{"order_id":"order_06","order_no":"0006","date_create":"1584497998606","date_update":"1584497998606"}
{"order_id":"order_07","order_no":"0007","date_create":"1584497998607","date_update":"1584497999899"}
-- deal
{"car_deal_id":"car_01","sale_order_no":"0001","car_attribute_id":"A0001","vin":"a1000","date_create":"1584497993601","date_update":"1584497993601"}
{"car_deal_id":"car_02","sale_order_no":"0002","car_attribute_id":"A0002","vin":"b1010","date_create":"1584497994602","date_update":"1584497994602"}
{"car_deal_id":"car_03","sale_order_no":"0003","car_attribute_id":"A0003","vin":"c1011","date_create":"1584497995603","date_update":"1584497995603"}
{"car_deal_id":"car_04","sale_order_no":"0004","car_attribute_id":"A0004","vin":"d1110","date_create":"1584497997604","date_update":"1584497997604"}
{"car_deal_id":"car_05","sale_order_no":"0005","car_attribute_id":"A0005","vin":"e1111","date_create":"1584497998605","date_update":"1584497998605"}
{"car_deal_id":"car_05","sale_order_no":"0005","car_attribute_id":"A0005","vin":"f000","date_create":"1584497998605","date_update":"1584497999788"}
{"car_deal_id":"car_04","sale_order_no":"0004","car_attribute_id":"A0004","vin":"d0001","date_create":"1584497997604","date_update":"1584497999799"}
output
注意：此处使用的时间语义为event_time，也就是wartmark来驱动表的join。且append表只能够join上时间戳小于等于自己的时态表数据
temporal_table.png
