问题

Sure, here's the translated code:

我们可以测试下面的flink作业吗？如果可以的话，我们该如何做？

@Component
public class RedisController {

    @Value("${flink.consumer.topic.name}")

    private String flinkConsumerTopic;

    /**
     * Method: run()
     * Description: 运行Flink作业的入口点，从Kafka消费消息，从Redis检索患者详情并将它们合并在一起。
     * @throws Exception 如果Flink作业执行失败
     */

    public void run() throws Exception {

        // 设置执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置Kafka属性
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");

        // 创建Kafka消费者
        FlinkKafkaConsumer<ObjectNode> consumer = new FlinkKafkaConsumer<>(flinkConsumerTopic, new JSONKeyValueDeserializationSchema(false), properties);

        // 从Kafka消费者创建数据流
        DataStream<ObjectNode> stream = env.addSource(consumer);

        // 使用窗口大小为10秒和滑动间隔为5秒定义滑动窗口
        DataStream<String> windowedStream = stream
                .windowAll(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(1)))
                .apply(new RichAllWindowFunction<ObjectNode, String, TimeWindow>() {
                    private Socket socket;
                    private OutputStream outputStream;
                    private InputStream inputStream;

                    @Override
                    public void open(Configuration parameters) throws Exception {
                        String serverAddress = "localhost";
                        int serverPort = 8888;

                        // 创建套接字并连接到服务器
                        socket = new Socket(serverAddress, serverPort);
                        outputStream = socket.getOutputStream();
                        inputStream = socket.getInputStream();
                    }

                    @Override
                    public void apply(TimeWindow timeWindow, Iterable<ObjectNode> input, Collector<String> out) throws Exception {
                        // 将输入数据序列化为JSON字符串
                        String inputJson = input.toString();
                        byte[] inputBytes = inputJson.getBytes();

                        // 将输入数据发送到服务器
                        outputStream.write(inputBytes);
                        outputStream.flush();

                        // 测量开始时间
                        long startTime = System.currentTimeMillis();

                        // 从服务器读取响应
                        byte[] responseBytes = new byte[15034];
                        String response = "";

                        int bytesRead = inputStream.read(responseBytes);

                        if (bytesRead != -1) {
                            response = response + new String(responseBytes, 0, bytesRead);
                            System.out.println("服务器响应：" + response);
                        }

                        // 测量结束时间
                        long endTime = System.currentTimeMillis();

                        // 计算响应时间
                        long responseTime = endTime - startTime;
                        System.out.println("响应时间：" + responseTime + " 毫秒");
                        out.collect(response);
                    }
                });

        // 创建Kafka生产者
        FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
                "output_topic", new SimpleStringSchema(), properties);

        // 将窗口流发送到Kafka生产者
        windowedStream.addSink(kafkaProducer);

        // 执行作业
        env.execute("Tumbling Window Example");
    }
}

Please note that the code contains placeholders like ${flink.consumer.topic.name} and specific configurations that you would need to set according to your environment. Additionally, the code seems to involve network communication with a server on localhost, so make sure the server is set up and running as needed.

@Component
public class RedisController {
@Value("${flink.consumer.topic.name}")
private String flinkConsumerTopic;
/**
* Method: run()
* Description: Entry point for running Flink job that consumes messages from Kafka,
* enriches them with patient details retrieved from Redis, and merges them together.
*
* @throws Exception if Flink job fails to execute
*/
public void run() throws Exception {
// Set up the execution environment
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// Set the Kafka properties
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
// Create a Kafka consumer
FlinkKafkaConsumer<ObjectNode> consumer = new FlinkKafkaConsumer<>(flinkConsumerTopic, new JSONKeyValueDeserializationSchema(false), properties);
// Create a data stream from the Kafka consumer
DataStream<ObjectNode> stream = env.addSource(consumer);
// Define the sliding window with a window size of 10 seconds and slide interval of 5 seconds
DataStream<String> windowedStream = stream
.windowAll(SlidingProcessingTimeWindows.of(Time.seconds(10), Time.seconds(1)))
.apply(new RichAllWindowFunction<ObjectNode, String, TimeWindow>() {
private Socket socket;
private OutputStream outputStream;
private InputStream inputStream;
@Override
public void open(Configuration parameters) throws Exception {
String serverAddress = "localhost";
int serverPort = 8888;
// Create a socket and connect to the server
socket = new Socket(serverAddress, serverPort);
outputStream = socket.getOutputStream();
inputStream = socket.getInputStream();
}
@Override
public void apply(TimeWindow timeWindow, Iterable<ObjectNode> input, Collector<String> out) throws Exception {
// Serialize the input data to a JSON string
String inputJson = input.toString();
byte[] inputBytes = inputJson.getBytes();
// Send the input data to the server
outputStream.write(inputBytes);
outputStream.flush();
// Measure the start time
long startTime = System.currentTimeMillis();
// Read the response from the server
byte[] responseBytes = new byte[15034];
String response = "";
int bytesRead = inputStream.read(responseBytes);
if (bytesRead != -1) {
response = response + new String(responseBytes, 0, bytesRead);
System.out.println("Response from server: " + response);
}
// Measure the end time
long endTime = System.currentTimeMillis();
// Calculate the time taken for the response
long responseTime = endTime - startTime;
System.out.println("Response time: " + responseTime + " ms");
out.collect(response);
}
});
// Create a Kafka producer
FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
"output_topic", new SimpleStringSchema(), properties);
// Send the windowed stream to the Kafka producer
windowedStream.addSink(kafkaProducer);
// Execute the job
env.execute("Tumbling Window Example");
}
}

答案1

得分: 1

你可以使用kafka-junit来启动一个嵌入式Kafka主题，这对于测试非常有用。依赖项如下：

<dependency>
    <groupId>net.mguenther.kafka</groupId>
    <artifactId>kafka-junit</artifactId>
    <version>2.8.0</version>
    <scope>test</scope>
</dependency>

你需要根据你的Flink版本设置合适的版本。

注意，你没有提到你的Flink版本（在问题中包含这个信息通常很有用），但是FlinkKafkaProducer已经被弃用了一段时间。你应该使用KafkaSink。

		<dependency>
<groupId>net.mguenther.kafka</groupId>
<artifactId>kafka-junit</artifactId>
<version>2.8.0</version>
<scope>test</scope>
</dependency>