How do I batch sql statements with package database/sql

sql

batch-insert

database-performance

sql-optimization

byNikita Barsukov·Jan 1, 2025

The transaction feature in the database/sql package of Go is your friend for batching SQL statements. The magic starts with invoking db.Begin(). Next, you execute statements through tx.Exec(). Finally, tx.Commit() is your magic wand that applies all changes at once. In the unfortunate case of errors, tx.Rollback() is your safety net. Here is a simple illustration:

tx, _ := db.Begin()  // Let the magic begin!
tx.Exec("INSERT INTO table_name ...")  // Abracadabra! SQL statement 1
tx.Exec("UPDATE table_name ...")  // Hocus Pocus! SQL statement 2
tx.Commit()  // Poof! The magic is complete

Always handle errors after each Exec, and remember, Commit plays the crucial role of guaranteeing atomic execution of all your batched statements.

Optimize for efficiency and scale

When dealing with the big boys (i.e., a large number of rows), certain tricks and tips can help you enhance efficiency:

Prepare once, execute many times: With stmt, err := tx.Prepare(), compile your SQL statement once and within a loop, keep executing it. Efficiency and performance improved in a snap.
Bulk insert: Merging multiple rows into a single INSERT with several value sets reduces network roundtrips. Your network thanks you for this.
Equipoise string concatenation: fmt.Sprintf works well for individual entries, and strings.Join accumulates a batch of SQL value strings.

values := make([]string, 0, len(rows)) // Nothing beats a well-prepared string slice container
args := make([]interface{}, 0, len(rows) * len(columns)) // Making space for argument values

for _, row := range rows {
    placeholders := make([]string, len(row)) // Space...the final frontier
    for i, val := range row {
        args = append(args, val) // Welcome aboard args array! 
        placeholders[i] = fmt.Sprintf("$%d", len(args)) // sprintf. Not the sprintf you wanted, but the sprintf you needed 
    }
    values = append(values, fmt.Sprintf("(%s)", strings.Join(placeholders, ","))) // Assembling the puzzle pieces together
}
queryString := fmt.Sprintf("INSERT INTO table_name (%s) VALUES %s", strings.Join(columns, ","), strings.Join(values, ",")) // The concatenated monster of a SQL statement

_, err := tx.Exec(queryString, args...)
if err != nil {
    // Houston, we have a problem! Make sure to handle this error
}

Remember, performance is key, and preparation is half the victory!

Leverage advanced libraries

There are libraries that come with bells and whistles for batching capabilities:

For PostgreSQL, the pq package spices things up with its COPY command for optimized batch insertion.
The pgx library comes with an assorted box of features for batching statements.
ON CONFLICT DO UPDATE: This combined with batch inserts handles duplicity issues harmoniously. Double trouble, no more!

Ensuring data integrity and effective error handling

When batching operations, focus on error handling and transaction management:

Eagerly call defer tx.Rollback() in the beginning. Ensure successful commit before calling it a day to avoid incomplete batch execution.
Be descriptive with error messages. This aids easier debugging and enhances maintainability.

Handling batch operations at scale

For larger scale transactions, use temporary tables for notable performance gains.

Both batch updates and deletes follow similar patterns. For massive data traffic, turn to temporary tables and COPY operations.

Clean up and best practices

Perform a clean-up act post operations, especially using temporary tables. These tables consume resources and need to be managed.

Format your SQL code for better readability. This improves maintainability and scalability. Remember, well-structured code is loved equally by machines and humans!

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