Flink 零基础实战教程：如何计算实时热门商品

$cd my-flink-project/src/main/resources
$curl https://raw.githubusercontent.com/wuchong/my-flink-project/master/src/main/resources/UserBehavior.csv > UserBehavior.csv

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);

env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

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());
 }
}

topItems.print();
env.execute("Hot Items Job");