分享一下kafka streams的学习资料

可以参考以下资料：
1. Kafka Streams Documentation: https://kafka.apache.org/documentation/streams/
2. Kafka Streams in Action book: https://www.manning.com/books/kafka-streams-in-action
3. Kafka Streams Tutorial: https://www.baeldung.com/kafka-streams
4. Kafka Streams Course on Udemy: https://www.udemy.com/course/kafka-streams/
希望对你有所帮助。

相关问题

c++ Kafka Streams处理实时行情示例

以下是一个使用 Kafka Streams 处理实时行情的示例代码，其中我们使用了 Kafka 的 C++ 客户端库 RdKafka：

#include <iostream>
#include <string>
#include <vector>

#include <librdkafka/rdkafkacpp.h>

using std::string;
using std::vector;
using std::cout;
using std::endl;

class ExampleDeliveryReportCb : public RdKafka::DeliveryReportCb {
 public:
  void dr_cb(RdKafka::Message& message) {
    if (message.err()) {
      std::cerr << "Failed to deliver message: " << message.errstr() << std::endl;
    } else {
      std::cout << "Message delivered to " << message.topic_name() << " [" << message.partition() << "]" << std::endl;
    }
  }
};

class ExampleEventCb : public RdKafka::EventCb {
 public:
  void event_cb(RdKafka::Event& event) {
    switch (event.type()) {
      case RdKafka::Event::EVENT_ERROR:
        std::cerr << "ERROR (" << RdKafka::err2str(event.err()) << "): " << event.str() << std::endl;
        if (event.err() == RdKafka::ERR__ALL_BROKERS_DOWN) {
          exit(1);
        }
        break;

      case RdKafka::Event::EVENT_STATS:
        std::cerr << "\"STATS\": " << event.str() << std::endl;
        break;

      case RdKafka::Event::EVENT_LOG:
        std::cerr << "LOG-" << event.severity() << "-" << event.fac().c_str() << ": " << event.str().c_str() << std::endl;
        break;

      default:
        std::cerr << "EVENT " << event.type() << " (" << RdKafka::err2str(event.err()) << "): " << event.str() << std::endl;
        break;
    }
  }
};

int main(int argc, char* argv[]) {
  if (argc != 4) {
    std::cerr << "Usage: " << argv[0] << " <broker> <input-topic> <output-topic>" << std::endl;
    return 1;
  }

  string brokers = argv[1];
  string input_topic = argv[2];
  string output_topic = argv[3];

  RdKafka::Conf *conf = RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL);
  conf->set("metadata.broker.list", brokers, errstr);
  conf->set("event_cb", &example_event_cb, errstr);

  ExampleDeliveryReportCb ex_dr_cb;
  conf->set("dr_cb", &ex_dr_cb, errstr);

  RdKafka::Producer *producer = RdKafka::Producer::create(conf, errstr);
  if (!producer) {
    std::cerr << "Failed to create producer: " << errstr << std::endl;
    return 1;
  }

  RdKafka::Conf *conf_consumer = RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL);
  conf_consumer->set("metadata.broker.list", brokers, errstr);
  conf_consumer->set("event_cb", &example_event_cb, errstr);

  RdKafka::Consumer *consumer = RdKafka::Consumer::create(conf_consumer, errstr);
  if (!consumer) {
    std::cerr << "Failed to create consumer: " << errstr << std::endl;
    return 1;
  }

  vector<string> topics;
  topics.push_back(input_topic);
  RdKafka::ErrorCode err = consumer->subscribe(topics);
  if (err) {
    std::cerr << "Failed to subscribe to " << input_topic << ": " << RdKafka::err2str(err) << std::endl;
    return 1;
  }

  while (true) {
    RdKafka::Message *msg = consumer->consume(1000);
    if (msg->err() == RdKafka::ERR_NO_ERROR) {
      string data((const char*)msg->payload(), msg->len());

      // TODO: process data

      RdKafka::ErrorCode resp = producer->produce(output_topic, -1, RdKafka::Producer::RK_MSG_COPY, const_cast<char *>(data.c_str()), data.size(), NULL, NULL);
      if (resp != RdKafka::ERR_NO_ERROR) {
        std::cerr << "Failed to produce message: " << RdKafka::err2str(resp) << std::endl;
      }
      producer->poll(0);
    } else if (msg->err() != RdKafka::ERR__TIMED_OUT) {
      std::cerr << "Failed to consume message: " << RdKafka::err2str(msg->err()) << std::endl;
    }
    delete msg;
  }

  delete consumer;
  delete producer;
  return 0;
}

在上面的示例代码中，我们创建了一个生产者和一个消费者，分别用于从输入主题中消费数据并将处理后的数据写入输出主题中。你可以根据实际需求修改上面的示例代码，并添加各种流式处理操作符来实现实时行情处理。

springboot怎么集成kafka streams实现消息按时间戳消费

您好，关于您的问题，我可以回答。在Spring Boot中，可以使用Spring Kafka Streams来集成Kafka Streams，实现消息按时间戳消费。您可以在Spring Boot应用程序中添加以下依赖项：

<dependency>
    <groupId>org.springframework.kafka</groupId>
    <artifactId>spring-kafka-streams</artifactId>
    <version>2.5.1.RELEASE</version>
</dependency>

然后，您可以使用`@EnableKafkaStreams`注释启用Kafka Streams支持，并使用`StreamsBuilderFactoryBean`创建`KStream`和`KTable`。最后，您可以使用`@StreamListener`注释处理来自Kafka主题的消息。希望这可以帮助您。