java框架在大数据处理中的最新进展？

import org.apache.spark.streaming.*;
import org.apache.spark.streaming.api.java.*;

public class StreamingExample {
    public static void main(String[] args) {
        // 定义流式上下文
        JavaStreamingContext jsc = new JavaStreamingContext("local[*]", "Streaming Example", Seconds(1));

        // 创建数据输入流
        JavaDStream<String> lines = jsc.socketTextStream("localhost", 9999);

        // 处理流中的数据
        lines.foreachRDD(rdd -> {
            rdd.foreach(line -> System.out.println(line));
        });

        // 启动流式计算
        jsc.start();
        jsc.awaitTermination();
    }
}

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.api.java.utils.ParameterTool.UnrecognizedOptionException;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.windowing.time.Time;

public class FlinkExample {
    public static void main(String[] args) throws Exception {
        // 解析命令行参数
        ParameterTool params = ParameterTool.fromArgs(args);

        // 设置流式执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 创建数据源
        SensorSource source = new SensorSource();

        // 创建数据流
        DataStream<SensorData> stream = env.addSource(source);

        // 处理流中的数据
        stream
                .map(new MapFunction<SensorData, Tuple2<String, Double>>() {
                    @Override
                    public Tuple2<String, Double> map(SensorData sensorData) {
                        return new Tuple2<>(sensorData.getId(), sensorData.getTemperature());
                    }
                })
                .keyBy(0)
                .timeWindow(Time.seconds(10))
                .reduce(
                        (s1, s2) -> new SensorData(
                                s1.getId(),
                                (s1.getTemperature() + s2.getTemperature()) / 2)
                )
                .print();

        // 执行流式计算
        env.execute("Flink Example");
    }

    static class SensorSource implements SourceFunction<SensorData> {
        @Override
        public void run(SourceContext<SensorData> out) throws Exception {
            // 生成随机传感器数据
            while (true) {
                SensorData data = new SensorData("sensor-" + new Random().nextInt(10), new Random().nextDouble() * 100);
                out.collect(data);
                Thread.sleep(100);
            }
        }

        @Override
        public void cancel() {}
    }

    static class SensorData {
        private String id;
        private double temperature;

        public SensorData(String id, double temperature) {
            this.id = id;
            this.temperature = temperature;
        }

        public String getId() {
            return id;
        }

        public double getTemperature() {
            return temperature;
        }
    }
}

import backtype.storm.topology.BasicOutputCollector;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.base.BaseBasicBolt;
import backtype.storm.topology.IBasicBolt;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Tuple;
import backtype.storm.tuple.Values;

public class WebsiteLogBolt extends BaseBasicBolt {
    @Override
    public void execute(Tuple tuple, BasicOutputCollector collector) {
        String logLine = tuple.getString(0);

        // 解析日志行
        WebsiteLog log = WebsiteLog.parse(logLine);

        // 发射解析结果
        collector.emit(new Values(log.getIpAddress(), log.getUrl(), log.getResponseTime()));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("ipAddress", "url", "responseTime"));
    }
}