java框架在实时数据处理项目中的适用性

import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

public class RealtimeDataProcessing {

    public static void main(String[] args) {
        // 创建流构建器
        StreamsBuilder builder = new StreamsBuilder();

        // 接收实时数据
        KStream<String, String> inputStream = builder.stream("input-topic");

        // 过滤数据
        KStream<String, String> filteredStream = inputStream.filter((key, value) -> value.contains("temperature"));

        // 变换数据
        KStream<String, String> transformedStream = filteredStream.mapValues(value -> value.substring(value.indexOf(":") + 1));

        // 写入数据库
        transformedStream.to("output-topic");

        // 创建 Kafka 流并启动
        KafkaStreams streams = new KafkaStreams(builder.build(), PropertiesUtil.getKafkaProperties());
        streams.start();
    }
}

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

public class RealtimeFraudDetection {

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

        // 接收实时交易数据
        DataStream<Transaction> transactions = env.addSource(...);

        // 提取特征和分数
        DataStream<Tuple2<String, Double>> features = transactions.map(new MapFunction<Transaction, Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> map(Transaction value) {
                // ... 提取特征和计算分数
            }
        });

        // 根据用户分组并求和
        DataStream<Tuple2<String, Double>> aggregated = features.keyBy(0).timeWindow(Time.seconds(60)).reduce(new ReduceFunction<Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> reduce(Tuple2<String, Double> value1, Tuple2<String, Double> value2) {
                return new Tuple2<>(value1.f0, value1.f1 + value2.f1);
            }
        });

        // 检测异常
        aggregated.filter(t -> t.f1 > fraudThreshold);

        // ... 生成警报或采取其他行动
    }
}

import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.topology.TopologyBuilder;
import backtype.storm.tuple.Fields;
import org.apache.storm.kafka.KafkaSpout;
import org.apache.storm.kafka.SpoutConfig;
import org.apache.storm.kafka.StringScheme;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.utils.Utils;

public class RealtimeLogAnalysis {

    public static void main(String[] args) {
        // 创建拓扑
        TopologyBuilder builder = new TopologyBuilder();

        // Kafka 数据源
        SpoutConfig spoutConfig = new SpoutConfig(KafkaProperties.ZOOKEEPER_URL, KafkaProperties.TOPIC, "/my_topic", UUID.randomUUID().toString());
        KafkaSpout kafkaSpout = new KafkaSpout(spoutConfig, new StringScheme());
        builder.setSpout("kafka-spout", kafkaSpout);

        // 分析日志数据的 Bolt
        builder.setBolt("log-parser-bolt", new BaseRichBolt() {
            @Override
            public void execute(Tuple input) {
                // ... 解析日志数据和提取有用信息
            }
        }).shuffleGrouping("kafka-spout");

        // ... 其他处理 Bolt 和拓扑配置

        // 配置 Storm
        Config config = new Config();
        config.setDebug(true);

        // 本地提交和运行拓扑
        LocalCluster cluster = new LocalCluster();
        cluster.submitTopology("log-analysis", config, builder.createTopology());
    }
}