java框架在大数据实时计算中的优势和不足

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;

public class FlinkKafkaStreaming {
    public static void main(String[] args) throws Exception {
        // 创建流执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 从 Kafka 读取数据流
        DataStream<String> inputStream = env.addSource(new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), ...));

        // 实时统计数据流中的单词数量
        DataStream<Long> wordCounts = inputStream.flatMap(new FlatMapFunction<>() {...})
            .keyBy(w -> w)
            .countWindow(10, 2)
            .sum(1);

        // 打印结果
        wordCounts.print();

        // 执行流
        env.execute();
    }
}