Flink 定时加载外部文件数据并广播

场景：来自kafka的流数据，需要和外部文件数据进行对比，外部文件每天更新，所以需要在flink流处理中定时读取外部流，并广播到下游。本文介绍怎么在flink流处理中执行定时任务。

广播作用：

广播状态（Broadcast State）的引入是为了支持一些来自一个流的数据需要广播到所有下游任务的情况，它存储在本地，用于处理其他流上的所有传入元素。例如，广播状态可以作为一种自然匹配出现，您可以想象一个低吞吐量流，其中包含一组规则，我们希望对来自另一个流的所有元素进行评估。——尼小摩

1、实时流：

基于flink1.9.2，必须使用FlinkKafkaConsumer

FlinkKafkaConsumer ssConsumer = new FlinkKafkaConsumer(READ_TOPIC, new SimpleStringSchema(), properties);

2、文件流：

DataStreamSource fileStreamSource = env.addSource(new MyRishSourceFileReader());

 

3、自定义Source：

自定义的Source，继承RichSourceFunction，重写函数。在open函数中读取文件，存入ConcurrentHashMap中，在run函数中ctx.collect()出去，然后在BroadcastProcessFunction中的processBroadcastElement函数里接收。

import com.alibaba.fastjson.JSONObject;
import com.maxmind.geoip2.DatabaseReader;
import com.qianxin.ida.dto.DeviceUserBaseLineDto;
import com.qianxin.ida.dto.GpsBaseLineDto;
import com.qianxin.ida.dto.TimeBaseLineDto;
import com.qianxin.ida.dto.UserDeviceBaseLineDto;
import com.qianxin.ida.enrich.BuildBaseLineDto;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.List;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;

public class MyRishSourceFileReader extends RichSourceFunction {
    public static DatabaseReader reader;
    private List timeBaseLineDtos;
    public final static ConcurrentHashMap map = new ConcurrentHashMap<>();
    private static final Logger logger = LoggerFactory.getLogger(MyRishSourceFileReader.class);

    @Override
    public void open(Configuration configuration) {
        try {
            //启动时读取首次
            query();
            reader = TransUtil.getDatabaseReader();
            //线程定时任务，每隔23小时，执行一次
            ScheduledExecutorService service = Executors.newScheduledThreadPool(5);
            service.scheduleWithFixedDelay(() -> {
                try {
                    query();
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }, 10L, 23L, TimeUnit.HOURS);

        } catch (Exception e) {
            logger.error("读取文件失败", e);
        }
    }

    public void query() {
        logger.info("当前读取基线文件的时间：" + LocalDateTime.now().format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")));
        timeBaseLineDtos = BuildBaseLineDto.getTimeBaseLine();
        map.put("timeBaseLineDtos", timeBaseLineDtos);
    }

    @Override
    public void run(SourceContext ctx) {
        try {
            JSONObject out = new JSONObject();
            JSONObject configJsonFile = JSONObject.parseObject(JsonFileReaderUtil.readJsonData(PropertyReaderUtil.getStrValue("config.json.path")));
            out.put("configJsonFile", configJsonFile);
            out.put("timeBaseLineDtos", map.get("timeBaseLineDtos"));
            ctx.collect(out);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    @Override
    public void cancel() {
    }
 
}

将文件流广播，connect实时流ssConsumer,自定义广播流函数。

4、广播：

需要自己实现两个方法：processBroadcastElement()负责处理广播流中的传入元素，processElement()负责处理非广播流中的传入元素。从ReadOnlyContext中取到SourceContext的map，实时流数据和广播流数据汇聚，进行业务逻辑处理，最后out输出，进行sink等操作。

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.qianxin.ida.dto.DeviceUserBaseLineDto;
import com.qianxin.ida.dto.GpsBaseLineDto;
import com.qianxin.ida.dto.TimeBaseLineDto;
import com.qianxin.ida.dto.UserDeviceBaseLineDto;
import com.qianxin.ida.utils.TransUtil;
import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.shaded.netty4.io.netty.util.internal.StringUtil;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.math.BigDecimal;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class MyBroadcastProcessFunction extends BroadcastProcessFunction {

    private static final Logger logger = LoggerFactory.getLogger(MyBroadcastProcessFunction.class);
    private MapStateDescriptor ruleStateDescriptor;
    private String eventType;

    public MyBroadcastProcessFunction(MapStateDescriptor ruleStateDescriptor, String eventType) {
        this.ruleStateDescriptor = ruleStateDescriptor;
        this.eventType = eventType;
    }

    //这里处理广播流的数据
    @Override
    public void processBroadcastElement(JSONObject jsonObject, Context ctx, Collector collector) throws Exception {
        BroadcastState broadcastState = ctx.getBroadcastState(ruleStateDescriptor);
        broadcastState.put("broadcast", jsonObject);
    }

    //这里处理数据流的数据
    @Override
    public void processElement(String value, ReadOnlyContext ctx, Collector out) {
        double probability = 0;
        JSONObject currentStreamData = JSON.parseObject(value);
        if (currentStreamData != null) {
            try {
                Iterator> iterator = ctx.getBroadcastState(ruleStateDescriptor).immutableEntries().iterator();
                while (iterator.hasNext()) {
                    String outStr = "";
                    Object object = iterator.next().getValue();
                    JSONObject jsonObject = (JSONObject) JSON.toJSON(object);
                    JSONObject configJsonFile = (JSONObject) JSON.toJSON(jsonObject.get("configJsonFile"));
                    List timeBaseLineDto = (List) jsonObject.get("timeBaseLineDtos");
                    if ("1".equals(eventType)) {
                        //业务逻辑函数
                        outStr = doTimeOutierEvent(timeBaseLineDto, currentStreamData, configJsonFile);
                    } 
                    if (!StringUtil.isNullOrEmpty(outStr)) {
                        out.collect(outStr);
                    }
                }
            } catch (Exception e) {
                logger.error("处理广播流和数据流数据出错：", e);
            }
        }
    }
}

5、连接两个流：

将实时流和广播流连接，非广播流上调用connect()

    BroadcastStream timeBroadcast = fileStreamSource.setParallelism(1).broadcast(ruleStateDesc);
    DataStream timeStream = env.addSource(ssConsumer)
            .connect(timeBroadcast).process(new MyBroadcastProcessFunction(ruleStateDesc,"1"));

5、Sink：

timeStream.addSink(FlinkKafkaProducerCustom.create(WRITE_TOPIC, properties)).name("flink-kafka-timeStream");