How ChatGPT Analyzes and Optimizes Application Performance

Performance analysis

ChatGPT reviews the provided JMeter data, code snippets, and database schema. It notes that the login page achieves a throughput of about 546 QPS, while the successful login page reaches 547 QPS, but the average response time of the successful login is 1477 ms, indicating a backend database query issue. The error rate for successful logins is 10.683%, suggesting instability under high concurrency.

Identified bottlenecks

Database queries in UserRepository.login do not use indexes, leading to full‑table scans as user volume grows.

CPU usage spikes above 90% under load, making CPU a potential bottleneck.

Disk I/O shows high read/write latency during concurrent login operations.

Optimization recommendations

Database indexing : create indexes on username and password, preferably a composite index (username,password).

CREATE INDEX idx_user_login ON user(username,password);

Application code improvements :

Adjust HikariCP pool parameters (e.g., maximumPoolSize ≈ 70‑80% of MySQL max connections, minimumIdle ≈ 10‑20% of concurrent users). Example configuration:

spring.datasource.hikari.maximumPoolSize=80
spring.datasource.hikari.minimumIdle=10

Handle specific SQLExceptions, cache user status after successful validation, and avoid redundant database queries.

Use asynchronous processing for non‑critical tasks such as logging.

Load balancing and scaling : add more instances behind a load balancer and consider a distributed cache like Redis for session storage.

Code review : improve regular‑expression performance in ValidateUser and ensure proper data structures are used.

Monitoring and testing : regularly run EXPLAIN on SQL statements, use JMeter for load testing, and monitor database metrics.

SQL modifications

Replace the original SELECT * FROM user WHERE username=? AND password=? with a count query to reduce data transfer:

SELECT COUNT(*) AS sum FROM user WHERE username=? AND password=?

Update UserRepository.login() accordingly (see code snippet below).

String queryString = "SELECT COUNT(*) as sum FROM user WHERE username=? AND password=?";
try (Connection conn = dataSource.getConnection();
     PreparedStatement stmt = conn.prepareStatement(queryString)) {
    stmt.setString(1, username);
    stmt.setString(2, password);
    try (ResultSet rs = stmt.executeQuery()) {
        if (rs.next()) {
            int result = rs.getInt("sum");
            if (result == 0) {
                logger.error("登录失败");
                return "0";
            } else {
                logger.info("登录成功");
                return "1";
            }
        }
    }
} catch (SQLException e) {
    logger.error("login(User user) 发生数据库错误:" + e.getMessage());
}
return "-1";

HikariCP pool tuning example

config.setMaximumPoolSize(300); // max connections
config.setMinimumIdle(70);      // min idle connections

Retrieving MySQL max connections SHOW VARIABLES LIKE 'max_connections'; The query returns max_connections = 500, informing the pool size decisions.

By applying these steps—adding appropriate indexes, adjusting connection pool settings, caching, refactoring code, and continuously monitoring—the system’s average response time can be reduced and overall throughput increased, leading to a better user experience.