Real-time advertising recommendation system implemented by SpringBoot and Apache Doris

This topic aims to provide readers with an in-depth interpretation of Apache Doris technology and explore its role and function in various practical application scenarios in combination with the SpringBoot framework. This topic includes ten articles, each of which outlines a specific application field, such as big data analysis, real-time reporting systems, e-commerce data analysis, etc., and deepens readers' comprehensive understanding of Apache Doris technology through analysis of requirements, design of solutions, presentation of practical application examples, and discussion of possible problems.

In the context of the rapid development of science and technology, the importance of advertising recommendation systems has become increasingly prominent, especially real-time advertising recommendation systems. In order to meet this challenge, we chose SpringBoot and Apache Doris as our weapons to design and implement a powerful advertising recommendation system.

Explain the key requirements and challenges of real-time advertising recommendation systems

The key requirements of real-time advertising recommendation systems include real-time, accuracy, and stability. Real-time requires that the advertising recommendation system can respond to user behavior immediately and make corresponding recommendations; accuracy requires that the advertising recommendation is highly accurate and accurately pushes the ads that users are interested in; stability requires that the advertising recommendation system can operate stably under various abnormal circumstances.

We face two major challenges in order to meet these requirements: one is how to process a large amount of real-time data and make real-time advertising recommendations; the other is how to ensure the stability of the system so that it can work normally under various abnormal circumstances.

Describe the solution of SpringBoot and Apache Doris in advertising recommendation system

To address these challenges, we chose SpringBoot and Apache Doris. SpringBoot can quickly create independently running Spring projects, and has many built-in plug-ins and tool classes, which can greatly improve development efficiency. Apache Doris is an open source database for online analytical processing (OLAP). Its powerful real-time query and big data processing capabilities are very suitable for use in real-time advertising recommendation systems.

 // 使用SpringBoot启动一个Web服务@SpringBootApplication public class Application { public static void main(String[] args) { SpringApplication.run(Application.class, args); } }

Then we can create an interface to handle user requests:

 @RestController public class AdRecommendationController { @Autowired private AdRecommendationService adRecommendationService; @PostMapping("/recommend") public List<Ad> recommendAd(@RequestBody User user) { return adRecommendationService.recommendAd(user); } }

The AdRecommendationService class handles the main logic of ad recommendations:

 @Service public class AdRecommendationService { @Autowired private UserRepository userRepository; @Autowired private DorisService dorisService; public List<Ad> recommendAd(User user) { UserBehavior userBehavior = userRepository.findByUserId(user.getId()); return dorisService.queryAds(userBehavior); } }

We use Apache Doris's JDBC connection to query the user's historical behavior and push corresponding ads:

 @Service public class DorisService { private Connection conn; public DorisService() { String dorisUrl = "jdbc:mysql://localhost:9030/yourdb?characterEncoding=utf8&useSSL=true"; String username = "root"; String password = "123456"; conn = DriverManager.getConnection(dorisUrl, username, password); } public List<Ad> queryAds(UserBehavior userBehavior) { String sql = "SELECT * FROM ad WHERE category_id = ? AND user_id = ? ORDER BY click_count DESC LIMIT 10"; PreparedStatement statement = conn.prepareStatement(sql); statement.setInt(1, userBehavior.getCategoryId()); statement.setLong(2, userBehavior.getUserId()); ResultSet rs = statement.executeQuery(); return convertRsToAdList(rs); } }

Demonstrate real-time ad recommendation examples

Taking an online e-commerce system as an example, when a user clicks on a product, we can capture the behavior immediately, and then query the advertisements that the user may be interested in through Doris, and finally push these advertisements to the user through the SpringBoot Web interface.

 // 当用户点击一个商品时，我们捕获到这个行为@GetMapping("/click") public String clickAd(@RequestParam("userId") Long userId, @RequestParam("itemId") Long itemId, @RequestParam("categoryId") Integer categoryId) { UserBehavior userBehavior = new UserBehavior(userId, itemId, categoryId, new Date()); // 保存用户行为userRepository.save(userBehavior); // 查询推荐的广告List<Ad> ads = adRecommendationService.recommendAd(new User(userId)); // 返回推荐的广告return ads.toString(); }

Analyze the problems and improvement strategies that may be encountered in designing such systems

Although our real-time advertising recommendation system can better meet the requirements of real-time, accuracy and stability, there are still some problems. The first is the traffic problem. When the user scale expands rapidly, our service may not be able to bear such a load. The second is the data problem. We may encounter the problem of too much data and low query efficiency.

For traffic problems, we can consider using load balancing technology to share the pressure of large traffic through multiple servers. For data problems, we can use data sharding technology to store and query massive amounts of data in shards to improve query efficiency.

In general, the combination of SpringBoot and Apache Doris provides us with a good solution in the real-time advertising recommendation system, but we still need to continue to learn and explore to meet more complex needs and challenges.