Flink处理函数（3）—— 窗口处理函数

窗口处理函数包括：ProcessWindowFunction 和 ProcessAllWindowFunction

基础用法

stream.keyBy( t -> t.f0 )
 .window( TumblingEventTimeWindows.of(Time.seconds(10)) )
 .process(new MyProcessWindowFunction())

这里的MyProcessWindowFunction就是ProcessWindowFunction的一个实现类；

ProcessWindowFunction是一个典型的全窗口函数，把数据全部收集保存在窗口内，等到触发窗口计算时再统一处理

源码解析

public abstract class ProcessWindowFunction
        extends AbstractRichFunction {

    private static final long serialVersionUID = 1L;

    /**
     * Evaluates the window and outputs none or several elements.
     *
     * @param key The key for which this window is evaluated.
     * @param context The context in which the window is being evaluated.
     * @param elements The elements in the window being evaluated.
     * @param out A collector for emitting elements.
     * @throws Exception The function may throw exceptions to fail the program and trigger recovery.
     */
    public abstract void process(
            KEY key, Context context, Iterable elements, Collector out) throws Exception;

    /**
     * Deletes any state in the {@code Context} when the Window expires (the watermark passes its
     * {@code maxTimestamp} + {@code allowedLateness}).
     *
     * @param context The context to which the window is being evaluated
     * @throws Exception The function may throw exceptions to fail the program and trigger recovery.
     */
    public void clear(Context context) throws Exception {}

    /** The context holding window metadata. */
    public abstract class Context implements java.io.Serializable {
        /** Returns the window that is being evaluated. */
        public abstract W window();

        /** Returns the current processing time. */
        public abstract long currentProcessingTime();

        /** Returns the current event-time watermark. */
        public abstract long currentWatermark();

        /**
         * State accessor for per-key and per-window state.
         *
         * 
NOTE:If you use per-window state you have to ensure that you clean it up by
         * implementing {@link ProcessWindowFunction#clear(Context)}.
         */
        public abstract KeyedStateStore windowState();

        /** State accessor for per-key global state. */
        public abstract KeyedStateStore globalState();

        /**
         * Emits a record to the side output identified by the {@link OutputTag}.
         *
         * @param outputTag the {@code OutputTag} that identifies the side output to emit to.
         * @param value The record to emit.
         */
        public abstract  void output(OutputTag outputTag, X value);
    }
}

类型参数如下：

• IN：input，数据流中窗口任务的输入数据类型
• OUT：output，窗口任务进行计算之后的输出数据类型
• KEY：数据中键 key 的类型
• W：窗口的类型，是 Window 的子类型。一般情况下我们定义时间窗口，W就是 TimeWindow

定义方法如下：

process（窗口处理函数不是逐个处理数据）

• key：窗口做统计计算基于的键，也就是之前 keyBy 用来分区的字段
• context：当前窗口进行计算的上下文
• elements：窗口收集到用来计算的所有数据，这是一个可迭代的集合类型
• out：用来发送数据输出计算结果的收集器，类型为 Collector

可以明显看出，这里的参数不再是一个输入数据，而是窗口中所有数据的集合。而上下文context 所包含的内容也跟其他处理函数有所差别：

①不再提供设置定时器的方法

②由于当前不是只处理一个数据，所以也不再提供.timestamp()方法

③可以通过.window()直接获取到当前的窗口对象

④可以通过.windowState()和.globalState()获取到当前自定义的窗口状态和全局状态

clear()：

进行窗口的清理工作：如果我们自定义了窗口状态，那么必须在.clear()方法中进行显式地清除，避免内存溢出

学习课程链接：【尚硅谷】Flink1.13实战教程（涵盖所有flink-Java知识点）_哔哩哔哩_bilibili