【flink training】 打車熱點區域實時統計PopularPlaces

 

http://training.data-artisans.com/是Apache Flink商業公司DataArtisans提供的一個flink學習平台，主要提供了一些業務場景和flink api結合的case。本文摘取其中一個計算出租車上/下客人熱點區域demo進行分析。

一 數據准備

flink-traing的大部分例子是以New York City Taxi & Limousine Commission 提供的一份歷史數據集作為練習數據源，其中最常用一種類型為taxi ride的事件定義為

rideId         : Long      // a unique id for each ride
taxiId         : Long      // a unique id for each taxi
driverId       : Long      // a unique id for each driver
isStart        : Boolean   // TRUE for ride start events, FALSE for ride end events
startTime      : DateTime  // the start time of a ride
endTime        : DateTime  // the end time of a ride,
                           //   "1970-01-01 00:00:00" for start events
startLon       : Float     // the longitude of the ride start location
startLat       : Float     // the latitude of the ride start location
endLon         : Float     // the longitude of the ride end location
endLat         : Float     // the latitude of the ride end location
passengerCnt   : Short     // number of passengers on the ride

下載數據集

wget http://training.data-artisans.com/trainingData/nycTaxiRides.gz

將數據源轉化為flink stream source數據

// get an ExecutionEnvironment
StreamExecutionEnvironment env =
  StreamExecutionEnvironment.getExecutionEnvironment();
// configure event-time processing
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

// get the taxi ride data stream
DataStream<TaxiRide> rides = env.addSource(
  new TaxiRideSource("/path/to/nycTaxiRides.gz", maxDelay, servingSpeed));

 

二 坐標分格

如下圖所示，程序將整個城市坐標由西北向東南划分為大約250X400個單位的單元格

 

三 根據單元格計算坐標值

基礎坐標數據

    // geo boundaries of the area of NYC
    public static double LON_EAST = -73.7;
    public static double LON_WEST = -74.05;
    public static double LAT_NORTH = 41.0;
    public static double LAT_SOUTH = 40.5;

    // area width and height
    public static double LON_WIDTH = 74.05 - 73.7;
    public static double LAT_HEIGHT = 41.0 - 40.5;

    // delta step to create artificial grid overlay of NYC
    public static double DELTA_LON = 0.0014;
    public static double DELTA_LAT = 0.00125;

    // ( |LON_WEST| - |LON_EAST| ) / DELTA_LON
    public static int NUMBER_OF_GRID_X = 250;
    // ( LAT_NORTH - LAT_SOUTH ) / DELTA_LAT
    public static int NUMBER_OF_GRID_Y = 400;

根據經緯度計算單元格唯一id

    public static int mapToGridCell(float lon, float lat) {
        int xIndex = (int)Math.floor((Math.abs(LON_WEST) - Math.abs(lon)) / DELTA_LON);
        int yIndex = (int)Math.floor((LAT_NORTH - lat) / DELTA_LAT);

        return xIndex + (yIndex * NUMBER_OF_GRID_X);
    }

 

四 程序實現

將坐標映射到gridId之后剩下的就是采用窗口統計單位時間內event事件超過一定閾值的grid。

// find popular places
        DataStream<Tuple5<Float, Float, Long, Boolean, Integer>> popularSpots = rides
                // remove all rides which are not within NYC
                .filter(new RideCleansing.NYCFilter())
                // match ride to grid cell and event type (start or end)
                .map(new GridCellMatcher())
                // partition by cell id and event type
                .<KeyedStream<Tuple2<Integer, Boolean>, Tuple2<Integer, Boolean>>>keyBy(0, 1)
                // build sliding window
                .timeWindow(Time.minutes(15), Time.minutes(5))
                // count ride events in window
                .apply(new RideCounter())
                // filter by popularity threshold
                .filter((Tuple4<Integer, Long, Boolean, Integer> count) -> (count.f3 >= popThreshold))
                // map grid cell to coordinates
                .map(new GridToCoordinates());

        // print result on stdout
        popularSpots.print();

上述flink job在統計完熱點區域后又將gridId映射回每個單元格的中心點經緯度，具體實現為：

    /**
     * Maps the grid cell id back to longitude and latitude coordinates.
     */
    public static class GridToCoordinates implements
            MapFunction<Tuple4<Integer, Long, Boolean, Integer>, Tuple5<Float, Float, Long, Boolean, Integer>> {

        @Override
        public Tuple5<Float, Float, Long, Boolean, Integer> map(
                Tuple4<Integer, Long, Boolean, Integer> cellCount) throws Exception {

            return new Tuple5<>(
                    GeoUtils.getGridCellCenterLon(cellCount.f0),
                    GeoUtils.getGridCellCenterLat(cellCount.f0),
                    cellCount.f1,
                    cellCount.f2,
                    cellCount.f3);
        }
    }


    /**
     * Returns the longitude of the center of a grid cell.
     *
     * @param gridCellId The grid cell.
     *
     * @return The longitude value of the cell's center.
     */
    public static float getGridCellCenterLon(int gridCellId) {

        int xIndex = gridCellId % NUMBER_OF_GRID_X;

        return (float)(Math.abs(LON_WEST) - (xIndex * DELTA_LON) - (DELTA_LON / 2)) * -1.0f;
    }

    /**
     * Returns the latitude of the center of a grid cell.
     *
     * @param gridCellId The grid cell.
     *
     * @return The latitude value of the cell's center.
     */
    public static float getGridCellCenterLat(int gridCellId) {

        int xIndex = gridCellId % NUMBER_OF_GRID_X;
        int yIndex = (gridCellId - xIndex) / NUMBER_OF_GRID_X;

        return (float)(LAT_NORTH - (yIndex * DELTA_LAT) - (DELTA_LAT / 2));

    }

 

結論： 綜上所示，通過單元格划分，flink程序可以方便的解決實時統計熱點地理區域這一類問題。

代碼地址:https://github.com/dataArtisans/flink-training-exercises/blob/master/src/main/java/com/dataartisans/flinktraining/exercises/datastream_java/windows/PopularPlaces.java