package myflink;

public class HotItems {

  public static void main(String[] args) throws Exception {
    
  }
}

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 为了打印到控制台的结果不乱序，我们配置全局的并发为1，这里改变并发对结果正确性没有影响
env.setParallelism(1);

/** 用户行为数据结构 **/
public static class UserBehavior {
  public long userId;         // 用户ID
  public long itemId;         // 商品ID
  public int categoryId;      // 商品类目ID
  public String behavior;     // 用户行为, 包括("pv", "buy", "cart", "fav")
  public long timestamp;      // 行为发生的时间戳，单位秒
}

// UserBehavior.csv 的本地文件路径
URL fileUrl = HotItems2.class.getClassLoader().getResource("UserBehavior.csv");
Path filePath = Path.fromLocalFile(new File(fileUrl.toURI()));
// 抽取 UserBehavior 的 TypeInformation，是一个 PojoTypeInfo
PojoTypeInfo<UserBehavior> pojoType = (PojoTypeInfo<UserBehavior>) TypeExtractor.createTypeInfo(UserBehavior.class);
// 由于 Java 反射抽取出的字段顺序是不确定的，需要显式指定下文件中字段的顺序
String[] fieldOrder = new String[]{"userId", "itemId", "categoryId", "behavior", "timestamp"};
// 创建 PojoCsvInputFormat
PojoCsvInputFormat<UserBehavior> csvInput = new PojoCsvInputFormat<>(filePath, pojoType, fieldOrder);

DataStream<UserBehavior> dataSource = env.createInput(csvInput, pojoType);

DataStream<UserBehavior> timedData = dataSource
    .assignTimestampsAndWatermarks(new AscendingTimestampExtractor<UserBehavior>() {
      @Override
      public long extractAscendingTimestamp(UserBehavior userBehavior) {
        // 原始数据单位秒，将其转成毫秒
        return userBehavior.timestamp * 1000;
      }
    });

DataStream<UserBehavior> pvData = timedData
    .filter(new FilterFunction<UserBehavior>() {
      @Override
      public boolean filter(UserBehavior userBehavior) throws Exception {
        // 过滤出只有点击的数据
        return userBehavior.behavior.equals("pv");
      }
    });

DataStream<ItemViewCount> windowedData = pvData
    .keyBy("itemId")
    .timeWindow(Time.minutes(60), Time.minutes(5))
    .aggregate(new CountAgg(), new WindowResultFunction());

/** COUNT 统计的聚合函数实现，每出现一条记录加一 */
public static class CountAgg implements AggregateFunction<UserBehavior, Long, Long> {

  @Override
  public Long createAccumulator() {
    return 0L;
  }

  @Override
  public Long add(UserBehavior userBehavior, Long acc) {
    return acc + 1;
  }

  @Override
  public Long getResult(Long acc) {
    return acc;
  }

  @Override
  public Long merge(Long acc1, Long acc2) {
    return acc1 + acc2;
  }
}

/** 用于输出窗口的结果 */
public static class WindowResultFunction implements WindowFunction<Long, ItemViewCount, Tuple, TimeWindow> {

  @Override
  public void apply(
      Tuple key,  // 窗口的主键，即 itemId
      TimeWindow window,  // 窗口
      Iterable<Long> aggregateResult, // 聚合函数的结果，即 count 值
      Collector<ItemViewCount> collector  // 输出类型为 ItemViewCount
  ) throws Exception {
    Long itemId = ((Tuple1<Long>) key).f0;
    Long count = aggregateResult.iterator().next();
    collector.collect(ItemViewCount.of(itemId, window.getEnd(), count));
  }
}

/** 商品点击量(窗口操作的输出类型) */
public static class ItemViewCount {
  public long itemId;     // 商品ID
  public long windowEnd;  // 窗口结束时间戳
  public long viewCount;  // 商品的点击量

  public static ItemViewCount of(long itemId, long windowEnd, long viewCount) {
    ItemViewCount result = new ItemViewCount();
    result.itemId = itemId;
    result.windowEnd = windowEnd;
    result.viewCount = viewCount;
    return result;
  }
}

DataStream<String> topItems = windowedData
    .keyBy("windowEnd")
    .process(new TopNHotItems(3));  // 求点击量前3名的商品

/** 求某个窗口中前 N 名的热门点击商品，key 为窗口时间戳，输出为 TopN 的结果字符串 */
public static class TopNHotItems extends KeyedProcessFunction<Tuple, ItemViewCount, String> {

  private final int topSize;

  public TopNHotItems(int topSize) {
    this.topSize = topSize;
  }

  // 用于存储商品与点击数的状态，待收齐同一个窗口的数据后，再触发 TopN 计算
  private ListState<ItemViewCount> itemState;

  @Override
  public void open(Configuration parameters) throws Exception {
    super.open(parameters);
    // 状态的注册
    ListStateDescriptor<ItemViewCount> itemsStateDesc = new ListStateDescriptor<>(
        "itemState-state",
        ItemViewCount.class);
    itemState = getRuntimeContext().getListState(itemsStateDesc);
  }

  @Override
  public void processElement(
      ItemViewCount input,
      Context context,
      Collector<String> collector) throws Exception {

    // 每条数据都保存到状态中
    itemState.add(input);
    // 注册 windowEnd+1 的 EventTime Timer, 当触发时，说明收齐了属于windowEnd窗口的所有商品数据
    context.timerService().registerEventTimeTimer(input.windowEnd + 1);
  }

  @Override
  public void onTimer(
      long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {
    // 获取收到的所有商品点击量
    List<ItemViewCount> allItems = new ArrayList<>();
    for (ItemViewCount item : itemState.get()) {
      allItems.add(item);
    }
    // 提前清除状态中的数据，释放空间
    itemState.clear();
    // 按照点击量从大到小排序
    allItems.sort(new Comparator<ItemViewCount>() {
      @Override
      public int compare(ItemViewCount o1, ItemViewCount o2) {
        return (int) (o2.viewCount - o1.viewCount);
      }
    });
    // 将排名信息格式化成 String, 便于打印
    StringBuilder result = new StringBuilder();
    result.append("====================================\n");
    result.append("时间: ").append(new Timestamp(timestamp-1)).append("\n");
    for (int i=0;i<topSize;i++) {
      ItemViewCount currentItem = allItems.get(i);
      // No1:  商品ID=12224  浏览量=2413
      result.append("No").append(i).append(":")
            .append("  商品ID=").append(currentItem.itemId)
            .append("  浏览量=").append(currentItem.viewCount)
            .append("\n");
    }
    result.append("====================================\n\n");

    out.collect(result.toString());
  }
}