Profiling slow queries in Go using the database/sql package with MySQL can help you identify performance bottlenecks efficiently. By carefully logging and analyzing the queries that take too long to execute, you can optimize them for better performance. Here’s how you can achieve that.
Go database profiling, MySQL slow query analysis, Go sql package performance, database optimization Go, profiling MySQL queries
This guide provides a comprehensive overview of how to profile slow queries in MySQL using Go's database/sql package, enabling developers to enhance their application's performance.
package main
import (
"database/sql"
"fmt"
"log"
"time"
_ "github.com/go-sql-driver/mysql"
)
func main() {
// Open a connection to the MySQL database
db, err := sql.Open("mysql", "user:password@tcp(localhost:3306)/dbname")
if err != nil {
log.Fatal(err)
}
defer db.Close()
// Enable query profiling
_, err = db.Exec("SET profiling = 1")
if err != nil {
log.Fatal(err)
}
// Example query
start := time.Now()
rows, err := db.Query("SELECT * FROM your_table WHERE some_column = ?", "some_value")
if err != nil {
log.Fatal(err)
}
defer rows.Close()
// Process the rows...
fmt.Printf("Query executed in: %s\n", time.Since(start))
// Get profiling results
var queryID int
var duration time.Duration
err = db.QueryRow("SHOW PROFILES").Scan(&queryID, &duration)
if err != nil {
log.Fatal(err)
}
fmt.Printf("Query ID: %d, Duration: %v\n", queryID, duration)
}
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?