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这篇文章主要介绍“FlinkSQL中窗口的功能及实例用法”,在日常操作中,相信很多人在FlinkSQL中窗口的功能及实例用法问题上存在疑惑,小编查阅了各式资料,整理出简单好用的操作方法,希望对大家解答”FlinkSQL中窗口的功能及实例用法”的疑惑有所帮助!接下来,请跟着小编一起来学习吧!
前言
时间语义,要配合窗口操作才能发挥作用。最主要的用途,当然就是开窗口、根据时间段做计算了。下面我们就来看看 Table API 和 SQL 中,怎么利用时间字段做窗口操作。在 Table API 和 SQL 中,主要有两种窗口:Group Windows 和 Over Windows
一、分组窗口(Group Windows) 分组窗口(Group Windows)会根据时间或行计数间隔,将行聚合到有限的组(Group)中,并对每个组的数据执行一次聚合函数。 Table API 中的 Group Windows 都是使用.window(w:GroupWindow)子句定义的,并且必须由 as 子句指定一个别名。为了按窗口对表进行分组,窗口的别名必须在 group by 子句中,像常规的分组字段一样引用。例子:
val table = input .window([w: GroupWindow] as 'w) .groupBy('w, 'a) .select('a, 'w.start, 'w.end, 'w.rowtime, 'b.count)
Table API 提供了一组具有特定语义的预定义 Window 类,这些类会被转换为底层DataStream 或 DataSet 的窗口操作。
Table API 支持的窗口定义,和我们熟悉的一样,主要也是三种:滚动(Tumbling)、滑动(Sliding和 会话(Session)。
1.1 滚动窗口
滚动窗口(Tumbling windows)要用 Tumble 类来定义,另外还有三个方法:
over:定义窗口长度
on:用来分组(按时间间隔)或者排序(按行数)的时间字段
as:别名,必须出现在后面的 groupBy 中
实现案例
1.需求
设置滚动窗口为10秒钟统计id出现的次数。
2.数据准备
sensor_1,1547718199,35.8 sensor_6,1547718201,15.4 sensor_7,1547718202,6.7 sensor_10,1547718205,38.1 sensor_1,1547718206,32 sensor_1,1547718208,36.2 sensor_1,1547718210,29.7 sensor_1,1547718213,30.9
3.代码实现
package windows import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.table.api.scala._ import org.apache.flink.table.api.{EnvironmentSettings, Table, Tumble} import org.apache.flink.types.Row /** * @Package Windows * @File :FlinkSQLTumBlingTie.java * @author 大数据老哥 * @date 2020/12/25 21:58 * @version V1.0 * 设置滚动窗口 */ object FlinkSQLTumBlingTie { def main(args: Array[String]): Unit = { val env = StreamExecutionEnvironment.getExecutionEnvironment env.setParallelism(1) env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) val settings = EnvironmentSettings.newInstance() .useBlinkPlanner() .inStreamingMode() .build() val tableEnv = StreamTableEnvironment.create(env, settings) // 读取数据 val inputPath = "./data/sensor.txt" val inputStream = env.readTextFile(inputPath) // 先转换成样例类类型(简单转换操作) val dataStream = inputStream .map(data => { val arr = data.split(",") SensorReading(arr(0), arr(1).toLong, arr(2).toDouble) }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[SensorReading](Time.seconds(1)) { override def extractTimestamp(element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream(dataStream, 'id, 'temperature, 'timestamp.rowtime as 'ts) // 注册表 tableEnv.createTemporaryView("sensor", sensorTable) // table 实现 val resultTable = sensorTable .window(Tumble over 10.seconds on 'ts as 'tw) // 每10秒统计一次,滚动时间窗口 .groupBy('id, 'tw) .select('id, 'id.count, 'tw.end) //sql 实现 val sqlTable = tableEnv.sqlQuery( """ |select |id, |count(id) , |tumble_end(ts,interval '10' second) |from sensor |group by |id, |tumble(ts,interval '10' second) |""".stripMargin) /*** * .window(Tumble over 10.minutes on 'rowtime as 'w) (事件时间字段 rowtime) * .window(Tumble over 10.minutes on 'proctime as 'w)(处理时间字段 proctime) * .window(Tumble over 10.minutes on 'proctime as 'w) (类似于计数窗口,按处理时间排序,10 行一组) */ resultTable.toAppendStream[Row].print("talbe") sqlTable.toRetractStream[Row].print("sqlTable") env.execute("FlinkSQLTumBlingTie") } case class SensorReading(id: String, timestamp: Long, temperature: Double) }
运行结果
1.2 滑动窗口
滑动窗口(Sliding windows)要用 Slide 类来定义,另外还有四个方法:
over:定义窗口长度
every:定义滑动步长
on:用来分组(按时间间隔)或者排序(按行数)的时间字段
as:别名,必须出现在后面的 groupBy 中
实现案例
1.需求描述
设置窗口大小为10秒钟设置滑动距离为5秒钟,统计id的出现的次数。
2.数据准备
sensor_1,1547718199,35.8 sensor_6,1547718201,15.4 sensor_7,1547718202,6.7 sensor_10,1547718205,38.1 sensor_1,1547718206,32 sensor_1,1547718208,36.2 sensor_1,1547718210,29.7 sensor_1,1547718213,30.9
3.实现代码
package windows import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.table.api.{EnvironmentSettings, Slide, Table} import org.apache.flink.table.api.scala._ import org.apache.flink.types.Row import windows.FlinkSQLTumBlingTie.SensorReading /** * @Package windows * @File :FlinkSQLSlideTime.java * @author 大数据老哥 * @date 2020/12/27 22:19 * @version V1.0 * 滑动窗口 */ object FlinkSQLSlideTime { def main(args: Array[String]): Unit = { //构建运行环境 val env = StreamExecutionEnvironment.getExecutionEnvironment env.setParallelism(1) // 设置分区为1 方便后面测试 env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) //事件时间 val settings = EnvironmentSettings.newInstance() .useBlinkPlanner() .inStreamingMode() .build() // 创建表env val tableEnv = StreamTableEnvironment.create(env, settings) // 读取数据 val inputPath = "./data/sensor.txt" val inputStream = env.readTextFile(inputPath) // 先转换成样例类类型(简单转换操作) val dataStream = inputStream .map(data => { val arr = data.split(",") SensorReading(arr(0), arr(1).toLong, arr(2).toDouble) }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[SensorReading](Time.seconds(1)) { override def extractTimestamp(element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream(dataStream, 'id, 'temperature, 'timestamp.rowtime as 'ts) // 注册表 tableEnv.createTemporaryView("sensor", sensorTable) // table API 实现 val tableApi = sensorTable.window(Slide over 10.seconds every 5.seconds on 'ts as 'w) .groupBy('w, 'id) .select('id, 'id.count, 'w.end) val tableSql = tableEnv.sqlQuery( """ |select |id, |count(id), |HOP_END(ts,INTERVAL '10' SECOND, INTERVAL '5' SECOND )as w |from sensor |group by |HOP(ts,INTERVAL '10' SECOND, INTERVAL '5' SECOND),id |""".stripMargin) tableApi.toAppendStream[Row].print("tableApi") tableSql.toAppendStream[Row].print("tableSql") /** .window(Slide over 10.minutes every 5.minutes on 'rowtime as 'w) (事件时间字段 rowtime) .window(Slide over 10.minutes every 5.minutes on 'proctime as 'w) (处理时间字段 proctime) .window(Slide over 10.rows every 5.rows on 'proctime as 'w) (类似于计数窗口,按处理时间排序,10 行一组) **/ env.execute("FlinkSQLSlideTime") } }
4.运行结果
1.3 会话窗口
会话窗口(Session windows)要用 Session 类来定义,另外还有三个方法:
withGap:会话时间间隔
on:用来分组(按时间间隔)或者排序(按行数)的时间字段
as:别名,必须出现在后面的 groupBy 中实现案例
1.需求描述
设置一个session 为10秒钟 统计id的个数
2.准备数据
sensor_1,1547718199,35.8 sensor_6,1547718201,15.4 sensor_7,1547718202,6.7 sensor_10,1547718205,38.1 sensor_1,1547718206,32 sensor_1,1547718208,36.2 sensor_1,1547718210,29.7 sensor_1,1547718213,30.9
3.编写代码
package windows import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.table.api.{EnvironmentSettings, Session, Table} import org.apache.flink.table.api.scala._ import org.apache.flink.types.Row import windows.FlinkSQLTumBlingTie.SensorReading /** * @Package windows * @File :FlinkSqlSessionTime.java * @author 大数据老哥 * @date 2020/12/27 22:52 * @version V1.0 */ object FlinkSqlSessionTime { def main(args: Array[String]): Unit = { //构建运行环境 val env = StreamExecutionEnvironment.getExecutionEnvironment env.setParallelism(1) // 设置分区为1 方便后面测试 env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) //事件时间 val settings = EnvironmentSettings.newInstance() .useBlinkPlanner() .inStreamingMode() .build() // 创建表env val tableEnv = StreamTableEnvironment.create(env, settings) // 读取数据 val inputPath = "./data/sensor.txt" val inputStream = env.readTextFile(inputPath) // 先转换成样例类类型(简单转换操作) val dataStream = inputStream .map(data => { val arr = data.split(",") SensorReading(arr(0), arr(1).toLong, arr(2).toDouble) }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[SensorReading](Time.seconds(1)) { override def extractTimestamp(element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream(dataStream, 'id, 'temperature, 'timestamp.rowtime as 'ts) // 注册表 tableEnv.createTemporaryView("sensor", sensorTable) val tableApi = sensorTable. window(Session withGap 10.seconds on 'ts as 'w) .groupBy('id, 'w) .select('id, 'id.count, 'w.end) val tableSQL = tableEnv.sqlQuery( """ |SELECT |id, |COUNT(id), |SESSION_END(ts, INTERVAL '10' SECOND) AS w |FROM sensor |GROUP BY |id, |SESSION(ts, INTERVAL '10' SECOND) |""".stripMargin) tableApi.toAppendStream[Row].print("tableApi") tableSQL.toAppendStream[Row].print("tableSQL") /** * .window(Session withGap 10.minutes on 'rowtime as 'w) 事件时间字段 rowtime) * .window(Session withGap 10.minutes on 'proctime as 'w) 处理时间字段 proctime) */ env.execute("FlinkSqlSessionTime") } }
4.运行结果
二、 Over Windows
Over window 聚合是标准 SQL 中已有的(Over 子句),可以在查询的 SELECT 子句中定义。Over window 聚合,会针对每个输入行,计算相邻行范围内的聚合。Over windows使用.window(w:overwindows*)子句定义,并在 select()方法中通过别名来引用。例子:
val table = input .window([w: OverWindow] as 'w) .select('a, 'b.sum over 'w, 'c.min over 'w)
Table API 提供了 Over 类,来配置 Over 窗口的属性。可以在事件时间或处理时间,以及指定为时间间隔、或行计数的范围内,定义 Over windows。
无界的 over window 是使用常量指定的。也就是说,时间间隔要指定 UNBOUNDED_RANGE,或者行计数间隔要指定 UNBOUNDED_ROW。而有界的 over window 是用间隔的大小指定的。
2.1 无界的 over window
// 无界的事件时间 over window (时间字段 "rowtime") .window(Over partitionBy 'a orderBy 'rowtime preceding UNBOUNDED_RANGE as 'w) //无界的处理时间 over window (时间字段"proctime") .window(Over partitionBy 'a orderBy 'proctime preceding UNBOUNDED_RANGE as 'w) // 无界的事件时间 Row-count over window (时间字段 "rowtime") .window(Over partitionBy 'a orderBy 'rowtime preceding UNBOUNDED_ROW as 'w) //无界的处理时间 Row-count over window (时间字段 "rowtime") .window(Over partitionBy 'a orderBy 'proctime preceding UNBOUNDED_ROW as 'w)
2.2 有界的 over window
// 有界的事件时间 over window (时间字段 "rowtime",之前 1 分钟) .window(Over partitionBy 'a orderBy 'rowtime preceding 1.minutes as 'w) // 有界的处理时间 over window (时间字段 "rowtime",之前 1 分钟) .window(Over partitionBy 'a orderBy 'proctime preceding 1.minutes as 'w) // 有界的事件时间 Row-count over window (时间字段 "rowtime",之前 10 行) .window(Over partitionBy 'a orderBy 'rowtime preceding 10.rows as 'w) // 有界的处理时间 Row-count over window (时间字段 "rowtime",之前 10 行) .window(Over partitionBy 'a orderBy 'proctime preceding 10.rows as 'w)
2.3 代码练习
我们可以综合学习过的内容,用一段完整的代码实现一个具体的需求。例如,统计每个sensor每条数据,与之前两行数据的平均温度。
数据准备
sensor_1,1547718199,35.8 sensor_6,1547718201,15.4 sensor_7,1547718202,6.7 sensor_10,1547718205,38.1 sensor_1,1547718206,32 sensor_1,1547718208,36.2 sensor_1,1547718210,29.7 sensor_1,1547718213,30.9
代码分析:
package windows import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.table.api.{EnvironmentSettings, Over, Tumble} import org.apache.flink.table.api.scala._ import org.apache.flink.types.Row /** * @Package windows * @File :FlinkSqlTumBlingOverTime.java * @author 大数据老哥 * @date 2020/12/28 21:45 * @version V1.0 */ object FlinkSqlTumBlingOverTime { def main(args: Array[String]): Unit = { // 构建运行环境 val env = StreamExecutionEnvironment.getExecutionEnvironment env.setParallelism(1) // 设置并行度为1方便后面进行测试 env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) // 设置事件时间 val settings = EnvironmentSettings.newInstance() .useBlinkPlanner() .inStreamingMode() .build() //构建table Env val tableEnv = StreamTableEnvironment.create(env, settings) // 读取数据 val inputPath = "./data/sensor.txt" val inputStream = env.readTextFile(inputPath) // 先转换成样例类类型(简单转换操作) // 解析数据 封装成样例类 val dataStream = inputStream .map(data => { val arr = data.split(",") SensorReading(arr(0), arr(1).toLong, arr(2).toDouble) }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[SensorReading](Time.seconds(1)) { override def extractTimestamp(element: SensorReading): Long = element.timestamp * 1000L }) // 将数据注册成一张临时表 val dataTable = tableEnv.fromDataStream(dataStream,'id, 'temperature, 'timestamp.rowtime as 'ts) tableEnv.createTemporaryView("sensor",dataTable) var tableRes= dataTable.window( Over partitionBy 'id orderBy 'ts preceding 2.rows as 'ow) .select('id,'ts,'id.count over 'ow, 'temperature.avg over 'ow) var tableSql= tableEnv.sqlQuery( """ |select |id, |ts, |count(id) over ow, |avg(temperature) over ow |from sensor |window ow as( | partition by id | order by ts | rows between 2 preceding and current row |) |""".stripMargin) tableRes.toAppendStream[Row].print("tableRes") tableSql.toAppendStream[Row].print("tableSql") env.execute("FlinkSqlTumBlingOverTime") } case class SensorReading(id: String, timestamp: Long, temperature: Double) }
运行结果
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