Flink-1.12 - 之如何构建一个简单的TopN应用

本文主要是介绍Flink-1.12 - 之如何构建一个简单的TopN应用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

Flink-1.12 - 之如何构建一个简单的TopN应用

本文主要介绍通过Flink-1.12如何构建一个简单的TopN应用，这里介绍

DataStream API构建
Flink SQL构建

1 maven依赖如下

    <!--当前版本的控制~~--><properties><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target><flink.system.version>1.12.2</flink.system.version><scala.version>2.12</scala.version></properties><dependencies><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.system.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-table-planner-blink --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-blink_${scala.version}</artifactId><version>${flink.system.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-java --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_${scala.version}</artifactId><version>${flink.system.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_${scala.version}</artifactId><version>${flink.system.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-scala --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-scala_${scala.version}</artifactId><version>${flink.system.version}</version></dependency>

2 使用DataStream API构建

package com.shufang.stream;import com.shufang.bean.Orders;
import com.shufang.bean.WindowOrderCount;
import com.shufang.func.MyOrderSourceFunction;
import com.shufang.util.MyUtil;
import org.apache.commons.compress.utils.Lists;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.MapState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.Date;
import java.util.Map;public class WindowAggrFunction_TopN_Optimize {public static void main(String[] args) throws Exception {//1 获取执行环境，1.12.0之后默认时间语义是EventTime，但是可以在EventTime mode下明确指定使用processingTimeStreamExecutionEnvironment env = MyUtil.getStreamEnv();env.setParallelism(10);// TODO 正则匹配，不以.css|.js|.png|.ico结束，通常可以用来过滤String regexpPattern = "^((?!\\.(css|js|png|ico)$).)*&";//2 从数据源获取数据，SingleOutputStreamOperator<Orders> orderDtlStream = env.addSource(new MyOrderSourceFunction()).assignTimestampsAndWatermarks(WatermarkStrategy.<Orders>forBoundedOutOfOrderness(Duration.ofSeconds(20)).withTimestampAssigner((order, timestamp) -> order.getTimestamp()));orderDtlStream.print("detail");//3 主要是统计最近10s钟内不同货币的交易次数，每5s钟更新一次结果输出,找出热门的交易货币，以及排名SingleOutputStreamOperator<WindowOrderCount> aggregateStream = orderDtlStream.keyBy(order -> order.getCurrency()).window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5))).allowedLateness(Time.minutes(1))  // 当到了窗口的endTime,窗口会输出一个计算结果，但是窗口不会关闭，迟到的数据在一分钟进来都会参与计算并更新结果状态.aggregate(new MyOrderAggr(), new MyAllWindowFunction());orderDtlStream.print("agg");//3.1 要求出每个时间窗口的TopN，我们需要按照窗口分组，按照counts进行排序//windowEnd,key,countSingleOutputStreamOperator<String> top5Stream = aggregateStream.keyBy(wc -> wc.getWindowEnd()).process(new MyHotTopN(5));//4 进行输出top5Stream.print();env.execute("should specify a name");}/*** 定义一个processFunction，每来一次数据就存储State中，最终等到ontimer()的时候触发排序计算操作*/static class MyHotTopN extends KeyedProcessFunction<Long, WindowOrderCount, String> {// 定义一个控制TopN 的N的属性private Integer topSize;private SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");// 定义一个MapState，用来保存每个窗口中的所有的<currency,counts>，最终使用onTimer()触发输出topNMapState<String, Long> mapState;public MyHotTopN(Integer topSize) {this.topSize = topSize;}// 初始化mapState状态@Overridepublic void open(Configuration parameters) throws Exception {mapState = getRuntimeContext().getMapState(new MapStateDescriptor<String, Long>("mapState", String.class, Long.class));}// 每来一条数据我们处理一次@Overridepublic void processElement(WindowOrderCount value, Context ctx, Collector<String> out) throws Exception {//1 将信息放入到mapState中mapState.put(value.getCurrency(), value.getCounts());//2 注册定时器1,等到每个窗口的endTime + 1，触发窗口的输出操作ctx.timerService().registerEventTimeTimer(value.getWindowEnd() + 1);//3 注册一个定时器，在窗口关闭之后清空该窗口的mapStatectx.timerService().registerEventTimeTimer(value.getWindowEnd() + 60 * 1000);}// 定时器内管理的生命周期的操作@Overridepublic void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {//1 在process的时候为每个窗口注册了2个定时器，此时先判断是清空状态的定时器，还是输出窗口TopN的定时器if (timestamp == ctx.getCurrentKey() + 60 * 1000) {// 如果走进来，此时应该触发的定时器是清空的定时器，那么清空窗口的状态，并退出mapState.clear();return;}//2 如果走到这里，说明是输出结果的定时器,那么就进行topN的排序并输出结果String windowEndString = sdf.format(new Date(timestamp - 1));//3 拿到map中的所有的数据，进行排序ArrayList<Map.Entry<String, Long>> topNs = Lists.newArrayList(mapState.iterator());topNs.sort(new Comparator<Map.Entry<String, Long>>() {@Overridepublic int compare(Map.Entry<String, Long> o1, Map.Entry<String, Long> o2) {if (o1.getValue() < o2.getValue())return 1;else if (o1.getValue() > o2.getValue())return -1;elsereturn 0;}});//4 最终按照@topsize取topN，为了方便打印好看，以String类型遍历输出StringBuilder sb = new StringBuilder("=====================================");sb.append("窗口结束时间为：").append(windowEndString).append("\n");for (int i = 0; i < Math.min(topNs.size(), topSize); i++) {sb.append("当前的货币为：").append(topNs.get(i).getKey()).append(" || ");sb.append("当前的货币的在该时间段的交易次数为：").append(topNs.get(i).getValue()).append(" || ");sb.append("当前的交以次数排名为：").append(i + 1).append("\n");}sb.append("=====================================");out.collect(sb.toString());}}/*** 实现一个增量聚合的窗口函数 agg function ，该类型的窗口函数可以改变输出的类型* reduce不能改变输出的类型，输入输出的类型必须保持一致* Type parameters:* <IN> – 输出的event类型* <ACC> – 累加器的类型，每来一条数据更新一次累加器的状态 ，The type of the accumulator (intermediate aggregate state).* <OUT> – 最终聚合的结果类型 ，The type of the aggregated result*/static class MyOrderAggr implements AggregateFunction<Orders, Long, Long> {// 初始化累加器@Overridepublic Long createAccumulator() {return 0L;}// 累加器的计算逻辑，来一个event => + 1@Overridepublic Long add(Orders orders, Long acc) {return acc + 1;}// 获取累加器的值@Overridepublic Long getResult(Long acc) {return acc;}// 不同的累加器的merge操作@Overridepublic Long merge(Long aLong, Long acc1) {return aLong + acc1;}}/*** 定义一个全窗口函数，用来接收agg function的输出的 value类型，* Type parameters:* <IN> – 从AggFunction的输出类型作为输入类型 The type of the input value.* <OUT> – 最终的输出类型，可以随意定义 The type of the output value.* <KEY> – keyedStream的key的类型 ，The type of the key.* <W> – 这个应用所在的窗口的类型 ，The type of Window that this window function can be applied on.*/static class MyAllWindowFunction implements WindowFunction<Long, WindowOrderCount, String, TimeWindow> {@Overridepublic void apply(String key, TimeWindow window, Iterable<Long> input, Collector<WindowOrderCount> out) throws Exception {Long count = input.iterator().next(); //从累加器获取的统计累加值long windowEnd = window.getEnd(); //窗口的标识：这里是窗口的endTime//最终返回我们需要的类型WindowOrderCount(windowEnd,currency,counts)out.collect(new WindowOrderCount(windowEnd, key, count));}}
}

3 通过Flink SQL构建

package com.shufang.stream;import com.shufang.bean.Orders;
import com.shufang.func.MyOrderSourceFunction;
import com.shufang.util.MyUtil;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Slide;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;import java.time.Duration;import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;public class TableSQLAPi_TopN {public static void main(String[] args) throws Exception {//1 获取执行环境，1.12.0之后默认时间语义是EventTime，但是可以在EventTime mode下明确指定使用processingTimeStreamExecutionEnvironment env = MyUtil.getStreamEnv();StreamTableEnvironment tableEnv = MyUtil.getBlinkStreamTableEnv();env.setParallelism(1);/* 这是order中的字段,这是一个pojo类* public Long timestamp;* public Long amount;* public String currency;*///2 从数据源获取数据:StreamSingleOutputStreamOperator<Orders> orderDtlStream = env.addSource(new MyOrderSourceFunction()).assignTimestampsAndWatermarks(WatermarkStrategy.<Orders>forBoundedOutOfOrderness(Duration.ofSeconds(20)).withTimestampAssigner((order, timestamp) -> order.getTimestamp()));//3 由于没有外部的数据源，我们假装从stream中获取数据，这个Expression虽然好看，但是难用啊Table orders = tableEnv.fromDataStream(orderDtlStream,$("currency"), $("amount"), $("timestamp").rowtime().as("ts"));orders.printSchema();//4 获取到我们想要的统计表Table windowOrderCounts = orders.window(Slide.over(lit(10).seconds()).every(lit(5).seconds()).on($("ts")).as("sw")).groupBy($("sw"), $("currency")).select($("currency"),$("sw").end().as("windowEnd"),$("amount").count().as("counts"));//5 根据counts排序,TableAPi不支持rank、row_number以及dense_rank 排序，所以还需要使用SQL来处理//createTemporaryView("windowOrderCounts",windowOrderCounts)并不是StreamTableEnv的方法//所以我们需要调用：tableEnv.createTemporaryView("windowOrderCounts",stream)来注册表// TODO can't use this !!! tableEnv.createTemporaryView("windowOrderCounts",windowOrderCounts);//6 将windowOrderCounts转成流，并进行表的注册：windowOrderCountsDataStream<Row> stream = tableEnv.toAppendStream(windowOrderCounts, Row.class);tableEnv.createTemporaryView("windowOrderCounts", stream);/*** root*  |-- currency: STRING*  |-- windowEnd: TIMESTAMP(3)*  |-- counts: BIGINT*///7 使用Over窗口实现排序取TopNString sql = "" +"SELECT  \n" +"   windowEnd, \n" +"   currency, \n" +"   counts,  \n" +"   rn  \n" +"FROM ( \n" +"   SELECT  \n" +"\t  *, \n" +"\t  ROW_NUMBER() OVER w AS rn   \n" +"   FROM windowOrderCounts  \n" +"   WINDOW w AS (PARTITION BY windowEnd ORDER BY counts DESC)  \n" +") tmp_table  \n" +"WHERE rn <= 5";Table topN = tableEnv.sqlQuery(sql);DataStream<Tuple2<Boolean, Row>> topNStream = tableEnv.toRetractStream(topN, Row.class);topNStream.print("final top5");env.execute("sql top5");}
}