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wj-datacenter-platform
Commit d4287251 authored by duanruiming's avatar duanruiming
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Merge remote-tracking branch 'origin/master'

parents aa67a4c4 ef4813aa
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Showing with 44 additions and 43 deletions
wj-realtime-computing/src/main/java/com/wanji/indicators/task/citybrain/matchresultdata/MatchResultDataMain.java
View file @ d4287251
......@@ -21,7 +21,6 @@ import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.util.CollectionUtils;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.Properties;
......@@ -103,6 +102,7 @@ public class MatchResultDataMain {
List<TargetInfoVO> targetInfoVOList = new ArrayList<>();
String orgCode = frameModel.getOrgCode();
Long globalTimeStamp = frameModel.getGlobalTimeStamp();
for (CarTrackModel participant : participantList) {
TargetInfoVO targetInfoVO = new TargetInfoVO();
......@@ -123,7 +123,7 @@ public class MatchResultDataMain {
targetInfoVO.setLaneNo(Integer.parseInt(s));
}
targetInfoVO.setData_time(new Date().getTime());
targetInfoVO.setData_time(globalTimeStamp);
targetInfoVO.setDeviceNo(orgCode);
targetInfoVO.setId(participant.getId());
targetInfoVO.setCarType(CarTypeConverter.convertWanjiToBrain(participant.getOriginalType()));
......
wj-realtime-computing/src/main/java/com/wanji/indicators/task/citybrainreceive/targetinfo/TargetInfoReceiveMain.java
View file @ d4287251
......@@ -12,14 +12,15 @@ import org.apache.flink.connector.kafka.source.reader.deserializer.KafkaRecordDe
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.streaming.api.functions.windowing.AllWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.functions.windowing.ProcessAllWindowFunction;
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.FlinkKafkaProducer;
import org.apache.flink.util.Collector;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.*;
import java.util.stream.Collectors;
import java.util.stream.StreamSupport;
......@@ -63,27 +64,27 @@ public class TargetInfoReceiveMain {
SingleOutputStreamOperator<TargetInfoReceiveModel> frameModelStream = stream
.map(new TargetInfoReceiveMap())
.name("接收城市大脑轨迹数据-JsonToObject")
// .assignTimestampsAndWatermarks(
// WatermarkStrategy
// .<TargetInfoReceiveModel>forBoundedOutOfOrderness(Duration.ofMillis(50))
// .withTimestampAssigner((element, recordTimestamp)
// -> element.getBody().get(0).getData_time())
// )
.assignTimestampsAndWatermarks(
WatermarkStrategy
.<TargetInfoReceiveModel>forBoundedOutOfOrderness(Duration.ofSeconds(6))
.withTimestampAssigner((element, recordTimestamp)
-> element.getBody().get(0).getData_time())
)
;
SingleOutputStreamOperator<E1FrameMiniVO> resultStream = frameModelStream
// .windowAll(TumblingEventTimeWindows.of(Time.milliseconds(100)))
.windowAll(TumblingProcessingTimeWindows.of(Time.milliseconds(100)))
.apply(new AllWindowFunction<TargetInfoReceiveModel, E1FrameMiniVO, TimeWindow>() {
.windowAll(TumblingEventTimeWindows.of(Time.milliseconds(100)))
// .windowAll(TumblingProcessingTimeWindows.of(Time.milliseconds(100)))
.process(new ProcessAllWindowFunction<TargetInfoReceiveModel, E1FrameMiniVO, TimeWindow>() {
@Override
public void apply(TimeWindow window, Iterable<TargetInfoReceiveModel> values,
Collector<E1FrameMiniVO> out) throws Exception {
public void process(Context context, Iterable<TargetInfoReceiveModel> elements, Collector<E1FrameMiniVO> out) throws Exception {
E1FrameMiniVO e1FrameMiniVO = new E1FrameMiniVO();
TimeWindow window = context.window();
long windowEnd = window.getEnd();
Date date = new Date(windowEnd);
e1FrameMiniVO.setTimeStamp(date);
e1FrameMiniVO.setGlobalTimeStamp(windowEnd);
List<E1FrameParticipantMiniVO> participantMiniVOList = buildE1FrameParticipant(values);
List<E1FrameParticipantMiniVO> participantMiniVOList = buildE1FrameParticipant(elements);
e1FrameMiniVO.setE1FrameParticipant(participantMiniVOList);
e1FrameMiniVO.setOrgCode("");
e1FrameMiniVO.setParticipantNum(participantMiniVOList.size());
......
wj-realtime-computing/src/main/java/com/wanji/indicators/task/trajectory/CarTrajectoryIndexMain.java
View file @ d4287251
......@@ -758,33 +758,33 @@ public class CarTrajectoryIndexMain {
.name("将失衡事件写入mysql");
SingleOutputStreamOperator<LineCongestion> lineCongestion = splitCongestionStream
.keyBy(value -> "constantKey")
.process(new LineCongestionFunction())
.setParallelism(1)
.name("计算干线拥堵事件");
SingleOutputStreamOperator<LineCongestion> lineCongestionDuration = lineCongestion
.keyBy(value -> "constantKey")
.process(new LineCongestionDuration())
.setParallelism(1)
.name("给干线拥堵事件添加开始时间和持续时长");
SingleOutputStreamOperator<String> lineCongestionResult = lineCongestionDuration
.flatMap(new FlatMapFunction<LineCongestion, String>() {
@Override
public void flatMap(LineCongestion result, Collector<String> collector) {
String value = JSONObject.toJSONString(result);
collector.collect(value);
}
})
.setParallelism(1).name("将干线拥堵事件转成json字符串");
lineCongestionResult.addSink(new FlinkKafkaProducer<String>(
kafkaServerAddress,
lineSinkTopic,
new SimpleStringSchema()
)).setParallelism(1).name("将将干线拥堵事件发送至kafka");
// SingleOutputStreamOperator<LineCongestion> lineCongestion = splitCongestionStream
// .keyBy(value -> "constantKey")
// .process(new LineCongestionFunction())
// .setParallelism(1)
// .name("计算干线拥堵事件");
//
// SingleOutputStreamOperator<LineCongestion> lineCongestionDuration = lineCongestion
// .keyBy(value -> "constantKey")
// .process(new LineCongestionDuration())
// .setParallelism(1)
// .name("给干线拥堵事件添加开始时间和持续时长");
//
// SingleOutputStreamOperator<String> lineCongestionResult = lineCongestionDuration
// .flatMap(new FlatMapFunction<LineCongestion, String>() {
// @Override
// public void flatMap(LineCongestion result, Collector<String> collector) {
// String value = JSONObject.toJSONString(result);
// collector.collect(value);
// }
// })
// .setParallelism(1).name("将干线拥堵事件转成json字符串");
//
// lineCongestionResult.addSink(new FlinkKafkaProducer<String>(
// kafkaServerAddress,
// lineSinkTopic,
// new SimpleStringSchema()
// )).setParallelism(1).name("将干线拥堵事件发送至kafka");
env.execute("计算拥堵-溢出-失衡-死锁-相位空放");
} catch (Exception e) {
......
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