分布式处理 - Apache Spark、Flink、Samza、Siddhi、Hazelcast Jet

以下是使用Apache Spark进行分布式处理的示例代码：

from pyspark import SparkContext

# 创建SparkContext对象
sc = SparkContext("local", "DistributedProcessing")

# 创建RDD
data = [1, 2, 3, 4, 5]
rdd = sc.parallelize(data)

# 执行map操作
result = rdd.map(lambda x: x * 2)

# 打印结果
print(result.collect())

# 关闭SparkContext对象
sc.stop()

以下是使用Apache Flink进行分布式处理的示例代码：

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

public class DistributedProcessing {
    public static void main(String[] args) throws Exception {
        // 创建批处理执行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // 创建数据集
        DataSet<Integer> data = env.fromElements(1, 2, 3, 4, 5);

        // 执行map操作
        DataSet<Integer> result = data.map(new MapFunction<Integer, Integer>() {
            @Override
            public Integer map(Integer value) throws Exception {
                return value * 2;
            }
        });

        // 打印结果
        result.print();

        // 执行批处理任务
        env.execute("DistributedProcessing");
    }
}

以下是使用Apache Samza进行分布式处理的示例代码：

import org.apache.samza.application.TaskApplication;
import org.apache.samza.application.descriptors.TaskApplicationDescriptor;
import org.apache.samza.serializers.IntegerSerde;
import org.apache.samza.system.kafka.descriptors.KafkaInputDescriptor;
import org.apache.samza.system.kafka.descriptors.KafkaOutputDescriptor;
import org.apache.samza.system.kafka.descriptors.KafkaSystemDescriptor;

public class DistributedProcessing {
    public static void main(String[] args) {
        // 创建Kafka系统描述符
        KafkaSystemDescriptor kafkaSystemDescriptor = new KafkaSystemDescriptor("kafka");

        // 创建输入描述符
        KafkaInputDescriptor<Integer> inputDescriptor = kafkaSystemDescriptor.getInputDescriptor("input-topic", new IntegerSerde());

        // 创建输出描述符
        KafkaOutputDescriptor<Integer> outputDescriptor = kafkaSystemDescriptor.getOutputDescriptor("output-topic", new IntegerSerde());

        // 创建任务应用程序描述符
        TaskApplicationDescriptor taskApplicationDescriptor = new TaskApplicationDescriptor("DistributedProcessing");

        // 定义输入和输出
        taskApplicationDescriptor.withInputStream(inputDescriptor)
                                .withOutputStream(outputDescriptor)
                                .withTaskFactory(new MyTaskFactory());

        // 创建任务应用程序
        TaskApplication taskApplication = new TaskApplication(taskApplicationDescriptor);

        // 运行任务应用程序
        taskApplication.run();
    }
}

public class MyTaskFactory implements TaskFactory {
    @Override
    public Task createInstance() {
        return new MyTask();
    }
}

public class MyTask implements StreamTask, InitableTask {
    private MessageCollector messageCollector;

    @Override
    public void init(Context context) {
        this.messageCollector = context.getMessageCollector();
    }

    @Override
    public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
        Integer value = (Integer) envelope.getMessage();
        Integer result = value * 2;
        collector.send(new OutgoingMessageEnvelope(new SystemStream("kafka", "output-topic"), result));
    }
}

以下是使用Siddhi进行分布式处理的示例代码：

import org.apache.log4j.Logger;
import org.wso2.siddhi.core.SiddhiAppRuntime;
import org.wso2.siddhi.core.SiddhiManager;
import org.wso2.siddhi.core.event.Event;
import org.wso2.siddhi.core.stream.input.InputHandler;
import org.wso2.siddhi.core.stream.output.StreamCallback;

public class DistributedProcessing {
    public static void main(String[] args) {
        // 创建Siddhi管理器
        SiddhiManager siddhiManager = new SiddhiManager();

        // 定义Siddhi应用程序
        String siddhiApp = "@app:name('DistributedProcessing')" +
                           "define stream inputStream (value int);" +
                           "@info(name = 'query') " +
                           "from inputStream " +
                           "select value * 2 as result " +
                           "insert into outputStream;";

        // 创建Siddhi应用程序运行时
        SiddhiAppRuntime siddhiAppRuntime = siddhiManager.createSiddhiAppRuntime