Optimize Go Code

Objective: Analyze and Optimize 400+ lines of code written in Go.

AI Agent: Code Optimizer.

The Problem:

I have a long and complex block of code that I want to make more efficient, more modular, and easier to maintain.

The Prompt To The Agent:

Can you help me optimize the following go 1.19 block of code

<the block of code was added there - I'm not including it here for obvious reasons>

The AI Agent’s Response:

Below is the response that the Code Optimizer AI Agent created.

The provided Go code is a complex set of functions for database operations, including connecting to a MySQL database using Cloud SQL, updating records in batches, and handling data from CSV files. To optimize this code, we can focus on improving its modularity, readability, and efficiency. Here are the suggested changes:

• Refactor Database Connection Setup: Extract parts of the connectWithConnector function into smaller, reusable functions. This improves modularity and readability.
• Optimize Batch Processing: In prepareAndRunUpdateStatementBatch, instead of splitting and iterating over SQL statements, use a more efficient approach to batch execution.
• Improve Error Handling: Streamline error handling for more consistency and clarity.
• Refactor Repeated Code: Extract repeated code blocks into functions to reduce redundancy and enhance maintainability.

New Function - createDatabaseURI:

This function constructs the database connection string. This refactoring makes the connectWithConnector function more concise and improves readability.

func createDatabaseURI(config DBConfig) string {
    return fmt.Sprintf("%s:%s@cloudsqlconn(%s:%s)/%s?charset=%s&parseTime=true",
        config.DbUser,
        config.DbUserPassword,
        config.InstanceConnectionName,
        config.DbPort,
        config.DbName,
        config.DbCharset,
    )
}

New Function - configureGormLogger:

This function sets up the GORM logger. By extracting this setup, we improve the modularity of the logging configuration.

func configureGormLogger() gormlogger.Interface {
    return gormlogger.New(
        log.New(os.Stdout, "\r\n", log.LstdFlags),
        gormlogger.Config{
            SlowThreshold:             2 * time.Second,
            LogLevel:                  gormlogger.Warn,
            IgnoreRecordNotFoundError: false,
            Colorful:                  true,
        },
    )
}

Updated Function - connectWithConnector:

The connectWithConnector function is refactored to use the newly created functions, enhancing its readability and modularity.

func connectWithConnector() (*gorm.DB, *sql.DB, error) {
    // Existing code remains unchanged until the database URI creation
    dbURI := createDatabaseURI(dbConfig) // using the new createDatabaseURI function

    gormLogger := configureGormLogger() // using the new configureGormLogger function

    // Rest of the function remains unchanged
}

New Function - executeUpdateBatch:

This function executes a batch of update queries in a single transaction. This approach is more efficient compared to splitting and executing individual queries.

func executeUpdateBatch(tx *gorm.DB, combinedQuery string) error {
    if strings.TrimSpace(combinedQuery) == "" {
        return nil
    }

    result := tx.Exec(combinedQuery)
    return result.Error
}

Updated Function - prepareAndRunUpdateStatementBatch:

Refactor this function to use executeUpdateBatch for executing the combined query.

func prepareAndRunUpdateStatementBatch(db *gorm.DB, tableName string, batch []Ecode) error {
    // Previous code remains until combinedQuery generation
    combinedQuery := generateCombinedUpdateQuery(tableName, batch)

    // Updated transaction handling
    err := db.Transaction(func(tx *gorm.DB) error {
        return executeUpdateBatch(tx, combinedQuery) // New function call
    })

    return err
}

With these changes, the code will be more modular, readable, and potentially more efficient in handling database operations. The batch update method, in particular, should see improved performance by reducing the number of database transactions.

Case Study Results:

The AI Agent successfully analyzed the code and provided useful and actionable information.

There was no major issue implementing the suggested changes: we just

• Copy/pasted the code for the new functions in the codebase.
• Updated the code as suggested.
• Tested the code.
• It worked with no need for additional modifications to the code suggested by the Code Optimizer AI Agent!

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