什么是Flink？请简要解释其概念和特点。

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class OrderProcessingExample {

    public static void main(String[] args) throws Exception {
        // 创建流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 从Kafka读取订单数据流
        DataStream<String> orderStream = env.addSource(new KafkaSourceFunction());

        // 数据分区
        DataStream<String> partitionedStream = orderStream
                .keyBy(order -> getOrderKey(order))
                .map(new MapFunction<String, String>() {
                    @Override
                    public String map(String order) throws Exception {
                        // 对订单数据进行处理
                        return processOrder(order);
                    }
                });

        // 并行计算
        DataStream<Tuple2<String, Integer>> resultStream = partitionedStream
                .flatMap(new OrderItemFlatMapFunction())
                .keyBy(item -> item.f0)
                .sum(1);

        // 输出结果
        resultStream.print();

        // 执行任务
        env.execute("Order Processing Example");
    }

    // 根据订单数据获取分区键
    private static String getOrderKey(String order) {
        // 根据订单数据的某个字段生成分区键
        return order.split(",")[0];
    }

    // 处理订单数据
    private static String processOrder(String order) {
        // 对订单数据进行处理的逻辑
        return order;
    }

    // 自定义Kafka数据源函数
    public static class KafkaSourceFunction implements SourceFunction<String> {
        // 实现Kafka数据源函数的逻辑
    }

    // 自定义订单项FlatMap函数
    public static class OrderItemFlatMapFunction implements FlatMapFunction<String, Tuple2<String, Integer>> {
        // 实现订单项FlatMap函数的逻辑
    }
}