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Big data processing in C++ technology: How to use stream processing technology to process big data streams?
Big data processing in C++ technology: How to use stream processing technology to process big data streams?
Jun 01, 2024 pm 10:34 PM
Stream processing technology is used for big data processing. Stream processing is a technology that processes data streams in real time. In C, Apache Kafka can be used for stream processing. Stream processing provides real-time data processing, scalability, and fault tolerance. This example uses Apache Kafka to read data from a Kafka topic and calculate the average.
Big data processing in C technology: using stream processing technology to process big data streams
Stream processing is a kind of unbounded processing Data streaming technology enables developers to process and analyze data instantly as it is generated. In C, we can use stream processing frameworks such as Apache Kafka to achieve this functionality.
Advantages of Stream Processing Framework
- Real-time data processing: Process data immediately without storage and batch processing
- Scalability: Easy Scaling to handle large data streams
- Fault tolerance: Ensure data will not be lost through fault tolerance mechanism
Practical case: Using Apache Kafka for stream processing
Let us use Apache Kafka to create a C stream processing application that will read data from a Kafka topic and calculate the average value in the data stream.
// 頭文件
#include <kafka/apache_kafka.h>
#include <thread>
#include <atomic>
// 定義原子平均值計數(shù)器
std::atomic<double> avg_count(0.0);
// 流處理消費者線程
void consume_thread(const std::string& topic, rd_kafka_t* rk) {
// 創(chuàng)建消費者組
rd_kafka_consumer_group_t* consumer_group =
rd_kafka_consumer_group_join(rk, topic.c_str(),
rd_kafka_topic_partition_list_new(1), NULL);
while (true) {
// 訂閱主題
rd_kafka_message_t* message;
rd_kafka_resp_err_t consumer_err =
rd_kafka_consumer_group_poll(consumer_group, 10000, &message);
if (consumer_err == RD_KAFKA_RESP_ERR__PARTITION_EOF) {
rd_kafka_consumer_group_unjoin(consumer_group);
rd_kafka_consumer_group_destroy(consumer_group);
return;
} else if (consumer_err != RD_KAFKA_RESP_ERR_NO_ERROR) {
std::cerr << "Consumer error: " << rd_kafka_err2str(consumer_err) << "\n";
continue;
}
// 提取并處理數(shù)據(jù)
if (message) {
// 提取值
const char* message_str = static_cast<const char*>(message->payload);
int value = std::atoi(message_str);
// 更新原子平均值計數(shù)器
avg_count += (static_cast<double>(value) - avg_count) /
(avg_count.fetch_add(1) + 1);
if (avg_count >= 1e6) {
std::cout << "Average: " << avg_count << "\n";
}
}
// 提交偏移量
rd_kafka_message_destroy(message);
}
}
int main() {
// 初始化 Kafka 實例
rd_kafka_t* rk = rd_kafka_new(RD_KAFKA_CONSUMER, NULL, NULL, NULL);
if (!rk) {
std::cerr << "Failed to initialize Kafka instance\n";
return 1;
}
// 配置 Kafka 實例
char error_str[512];
if (rd_kafka_conf_set(rk, "bootstrap.servers", "localhost:9092",
error_str, sizeof(error_str)) != RD_KAFKA_CONF_OK) {
std::cerr << "Failed to set Kafka configuration: " << error_str << "\n";
rd_kafka_destroy(rk);
return 1;
}
// 創(chuàng)建流處理消費者線程
std::thread consumer_thr(consume_thread, "test-topic", rk);
// 等待消費者線程
consumer_thr.join();
// 銷毀 Kafka 實例
rd_kafka_destroy(rk);
return 0;
}Running this code will create a streaming application that reads data from the Kafka topic "test-topic" and calculates a per second average.
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Big data processing in C++ technology: How to use stream processing technology to process big data streams?
Jun 01, 2024 pm 10:34 PM
Stream processing technology is used for big data processing. Stream processing is a technology that processes data streams in real time. In C++, Apache Kafka can be used for stream processing. Stream processing provides real-time data processing, scalability, and fault tolerance. This example uses ApacheKafka to read data from a Kafka topic and calculate the average.
