Flink| Table API| SQL
Table API与SQL
Table API是流处理和批处理通用的关系型API，Table API可以基于流输入或者批输入来运行而不需要进行任何修改。
Table API是SQL语言的超集并专门为Apache Flink设计的，Table API是Scala 和Java语言集成式的API。与常规SQL语言中将查询指定为字符串不同，
Table API查询是以Java或Scala中的语言嵌入样式来定义的，具有IDE支持如:自动完成和语法检测。
引入pom依赖
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table_2.11</artifactId> <version>1.7.0</version> </dependency>
构造表环境
简单的fliter、select操作
import org.apache.flink.api.scala._ //scala的隐式转换
def main(args: Array[String]): Unit = {
//类似sparkcontext val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP") val dstream: DataStream[String] = env.addSource(myKafkaConsumer)
val startupLogDstream: DataStream[StartupLog] = dstream.map{ jsonString =>JSON.parseObject(jsonString,classOf[StartupLog]) }
//类似sparksession val tableEnv: StreamTableEnvironment = TableEnvironment.getTableEnvironment(env) //将流转换成表 val startupLogTable: Table = tableEnv.fromDataStream(startupLogDstream)
//通过Table API操作，sql中的操作都变成了函数方法来操作了； 如果filter中过滤的字段不在select中则filter要写在select前边; filter("ch = 'appstore'").select("mid,uid,ts") val table: Table = startupLogTable.select("mid,ch").filter("ch ='appstore'") //筛选出mid，ch字段，过滤掉 ch = 'appstore'的信息；
//表不能直接打印，要把它转换成流 //import org.apache.flink.table.api.scala._ 需要加隐式转换 val midchDataStream: DataStream[(String, String)] = table.toAppendStream[(String,String)] midchDataStream.print() env.execute() }
动态表
如果流中的数据类型是case class可以直接根据case class的结构生成table 或者根据字段顺序单独命名
tableEnv.fromDataStream(startupLogDstream)
tableEnv.fromDataStream(startupLogDstream,’mid,’uid .......)
最后的动态表可以转换为流进行输出
table.toAppendStream[(String,String)]
字段
用一个单引放到字段前面 来标识字段名, 如 ‘name , ‘mid ,’amount 等
每10秒统计渠道为appstore的个数
方法一、TableAPI的实现方式：
//每10秒中渠道为appstore的个数
def main(args: Array[String]): Unit = {
//sparkcontext
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
//时间特性改为eventTime
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
val dstream: DataStream[String] = env.addSource(myKafkaConsumer)
val startupLogDstream: DataStream[StartupLog] = dstream.map{ jsonString =>JSON.parseObject(jsonString,classOf[StartupLog]) }
//告知watermark 和 eventTime如何提取
val startupLogWithEventTimeDStream: DataStream[StartupLog] = startupLogDstream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[StartupLog](Time.seconds(0L)) {
override def extractTimestamp(element: StartupLog): Long = {
element.ts
}
}).setParallelism(1)
//SparkSession
val tableEnv: StreamTableEnvironment = TableEnvironment.getTableEnvironment(env)
//把数据流转化成Table
val startupTable: Table = tableEnv.fromDataStream(startupLogWithEventTimeDStream , 'mid,'uid,'appid,'area,'os,'ch,'logType,'vs,'logDate,'logHour,'logHourMinute,'ts.rowtime)
//ts.rowtime是声明ts为表里的时间字段; 每个字段都要跟 类StartupLog中的字段对齐；
//通过table api 进行操作
// 每10秒 统计一次各个渠道的个数 table api 解决
//1 groupby 2 要用 window 3 用eventtime来确定开窗时间
//①这里在表中.window()开窗; 但前提是还要有一个明确的哪个字段是时间字段；
//②.window(Tumble over 10000.millis on 'ts as' tt) 滚动窗口每10s滚动一次,ts起别名为tt，tt必须出现在groupBy中
val resultTable: Table = startupTable.window(Tumble over 10000.millis on 'ts as 'tt).groupBy('ch,'tt ).select( 'ch, 'ch.count)
//把Table转化成数据流
//val appstoreDStream: DataStream[(String, String, Long)] = appstoreTable.toAppendStream[(String,String,Long)]
//使用了group by之后就不能直接用toAppendStream方法了，应该使用toRetractStream
val resultDstream: DataStream[(Boolean, (String, Long))] = resultSQLTable.toRetractStream[(String,Long)]
resultDstream.filter(_._1).print()
//它打印的结果是(true,(tencent,539)) (false,(tencent,539)) //表这个数据已过期了，每来一条count一个 (true,(tencent,540)) //.filter(_._1)是留下为true的数据
env.execute()
}
每10秒统计渠道为appstore的个数
方法二、SQL的实现方式：
见官方文档： https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/table/sql.html
def main(args: Array[String]): Unit = {
//sparkcontext
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
//时间特性改为eventTime
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
val dstream: DataStream[String] = env.addSource(myKafkaConsumer)
val startupLogDstream: DataStream[StartupLog] = dstream.map{ jsonString =>JSON.parseObject(jsonString,classOf[StartupLog]) }
//告知watermark 和 eventTime如何提取
val startupLogWithEventTimeDStream: DataStream[StartupLog] = startupLogDstream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[StartupLog](Time.seconds(0L)) {
override def extractTimestamp(element: StartupLog): Long = {
element.ts
}
}).setParallelism(1)
//SparkSession
val tableEnv: StreamTableEnvironment = TableEnvironment.getTableEnvironment(env)
//把数据流转化成Table
val startupTable: Table = tableEnv.fromDataStream(startupLogWithEventTimeDStream , 'mid,'uid,'appid,'area,'os,'ch,'logType,'vs,'logDate,'logHour,'logHourMinute,'ts.rowtime)
//方法一、通过table api 进行操作
// 每10秒 统计一次各个渠道的个数 table api 解决
//1 groupby 2 要用 window 3 用eventtime来确定开窗时间
//val resultTable: Table = startupTable.window(Tumble over 10000.millis on 'ts as 'tt).groupBy('ch,'tt ).select( 'ch, 'ch.count)
//方法二、通过sql 进行操作
val resultSQLTable : Table = tableEnv.sqlQuery( "select ch ,count(ch) from "+startupTable+" group by ch,Tumble(ts,interval '10' SECOND )")
//把Table转化成数据流
//val appstoreDStream: DataStream[(String, String, Long)] = appstoreTable.toAppendStream[(String,String,Long)]
//使用了group by之后就不能直接用toAppendStream方法了，应该使用toRetractStream
val resultDstream: DataStream[(Boolean, (String, Long))] = resultSQLTable.toRetractStream[(String,Long)]
resultDstream.filter(_._1).print()
//它打印的结果是(true,(tencent,539)) (false,(tencent,539)) //表这个数据已过期了，每来一条count一个 (true,(tencent,540)) //.filter(_._1)是留下为true的数据
env.execute()
}
关于group by
如果使用 groupby table转换为流的时候只能用toRetractDstream
val rDstream: DataStream[(Boolean, (String, Long))] = table.toRetractStream[(String,Long)]
toRetractDstream 得到的第一个boolean型字段标识 true就是最新的数据，false表示过期老数据
val rDstream: DataStream[(Boolean, (String, Long))] = table.toRetractStream[(String,Long)] rDstream.filter(_._1).print()
如果使用的api包括时间窗口，那么时间的字段必须，包含在group by中。
val table: Table = startupLogTable.filter("ch ='appstore'").window(Tumble over 10000.millis on 'ts as 'tt).groupBy('ch ,'tt).select("ch,ch.count ")
关于时间窗口
用到时间窗口，必须提前声明时间字段，如果是processTime直接在创建动态表时进行追加就可以
val startupLogTable: Table = tableEnv.fromDataStream(startupLogWithEtDstream,'mid,'uid,'appid,'area,'os,'ch,'logType,'vs,'logDate,'logHour,'logHourMinute,'ts.rowtime)
如果是EventTime要在创建动态表时声明
val startupLogTable: Table = tableEnv.fromDataStream(startupLogWithEtDstream,'mid,'uid,'appid,'area,'os,'ch,'logType,'vs,'logDate,'logHour,'logHourMinute,'ps.processtime)
滚动窗口可以使用Tumble over 10000.millis on
val table: Table = startupLogTable.filter("ch ='appstore'").window(Tumble over 10000.millis on 'ts as 'tt).groupBy('ch ,'tt).select("ch,ch.count ")
