flink统计根据账号每30秒 金额的平均值

标签：row   XML   text   schema   mes   .com   设置   ade   window   

package com.zetyun.streaming.flink;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.watermark.Watermark;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.util.serialization.JSONDeserializationSchema;
import org.apache.flink.util.Collector;

import javax.annotation.Nullable;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Iterator;
import java.util.Properties;

/**
 * Created by jyt on 2018/4/10.
 * 基于账号计算每30秒 金额的平均值
 */
public class EventTimeAverage {

    public static void main(String[] args) throws Exception {
        final ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String topic = parameterTool.get("topic", "accountId-avg");
        Properties properties = parameterTool.getProperties();
        properties.setProperty("bootstrap.servers", "192.168.44.101:9092");
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        ObjectMapper objectMapper = new ObjectMapper();
        SingleOutputStreamOperator<ObjectNode> source = env.addSource(new FlinkKafkaConsumer010(
                topic,
                new JSONDeserializationSchema(),
                properties));
        //设置WaterMarks方式一
        /*SingleOutputStreamOperator<ObjectNode> objectNodeOperator = source.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<ObjectNode>(Time.seconds(15)) {
            @Override
            public long extractTimestamp(ObjectNode element) {
                SimpleDateFormat format = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
                Date eventTime = null;
                try {
                    eventTime = format.parse(element.get("eventTime").asText());
                } catch (ParseException e) {
                    e.printStackTrace();
                }
                return eventTime.getTime();
            }
        });*/
        //设置WaterMarks方式二
        SingleOutputStreamOperator<ObjectNode> objectNodeOperator = source.assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<ObjectNode>() {
            public long currentMaxTimestamp = 0L;
            public static final long maxOutOfOrderness = 10000L;//最大允许的乱序时间是10s
            Watermark a = null;
            SimpleDateFormat format = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");


            @Nullable
            @Override
            public Watermark getCurrentWatermark() {
                a = new Watermark(currentMaxTimestamp - maxOutOfOrderness);
                return a;
            }

            @Override
            public long extractTimestamp(ObjectNode jsonNodes, long l) {
                String time = jsonNodes.get("eventTime").asText();
                long timestamp = 0;
                try {
                    timestamp = format.parse(time).getTime();
                } catch (ParseException e) {
                    e.printStackTrace();
                }
                currentMaxTimestamp = Math.max(timestamp, currentMaxTimestamp);
                return timestamp;
            }
        });
        KeyedStream<Tuple3<String, Double, String>, Tuple> keyBy = objectNodeOperator.map(new MapFunction<ObjectNode, Tuple3<String, Double, String>>() {
            @Override
            public Tuple3<String, Double, String> map(ObjectNode jsonNodes) throws Exception {
                System.out.println(jsonNodes.get("accountId").asText() + "==map====" + jsonNodes.get("amount").asDouble() + "===map===" + jsonNodes.get("eventTime").asText());
                return new Tuple3<String, Double, String>(jsonNodes.get("accountId").asText(), jsonNodes.get("amount").asDouble(), jsonNodes.get("eventTime").asText());
            }
        }).keyBy(0);


        SingleOutputStreamOperator<Object> apply = keyBy.window(TumblingEventTimeWindows.of(Time.seconds(30))).apply(new WindowFunction<Tuple3<String,Double,String>, Object, Tuple, TimeWindow>() {
            @Override
            public void apply(Tuple tuple, TimeWindow timeWindow, Iterable<Tuple3<String, Double, String>> iterable, Collector<Object> collector) throws Exception {
                Iterator<Tuple3<String, Double, String>> iterator = iterable.iterator();
                int count =0;
                double num = 0.0;
                ///Tuple2<String, Double> result = null;
                Tuple3<String, Double, String> next = null;
                String accountId = null ;
                while (iterator.hasNext()) {
                    next = iterator.next();
                    System.out.println(next);
                    accountId=next.f0;
                    num += next.f1;
                    count++;
                }
                if (next != null) {

                    collector.collect(new Tuple2<String, Double>(accountId,num/count));
                }
            }
        });


        apply.print();
        //apply.addSink(new FlinkKafkaProducer010<String>("192.168.44.101:9092","wiki-result",new SimpleStringSchema()));
        env.execute("AverageDemo");
    }

}

flink统计根据账号每30秒 金额的平均值

标签：row   XML   text   schema   mes   .com   设置   ade   window   

原文地址：https://www.cnblogs.com/jiang-it/p/8930897.html