Speed Up Your Inserts: Multi-Row INSERT vs. Multiple Single INSERTs in MySQL/MariaDB

• Reduced Overhead: Sending a single INSERT statement with multiple rows requires less network traffic compared to sending many individual INSERT statements. This is because the database server only needs to parse and execute the query once, minimizing communication overhead.
• Batching: Database engines like MySQL and MariaDB often employ a technique called batching, where they group multiple INSERT operations together for more efficient processing. A single multi-row INSERT naturally aligns with this approach, leading to better performance.
• Transactions (if applicable): If you're performing the INSERTs within a transaction (a block of operations treated as a unit), using a single multi-row INSERT can further improve efficiency. This is because there's only one transaction to commit, reducing overhead compared to committing each single-row INSERT.

Here's a breakdown of the benefits:

• Faster Parsing: The database only needs to analyze the INSERT syntax once for a multi-row INSERT, saving time compared to parsing each single INSERT.
• Fewer Round Trips: Less network communication occurs as you're sending a single larger INSERT statement.
• Batching Optimization: The database can potentially batch the INSERT operation more effectively with multiple rows in one statement.
• Reduced Transaction Overhead (if applicable): One commit operation for the entire multi-row INSERT is more efficient than committing each single INSERT.

Consider these factors when deciding:

• Number of Rows: For a small number of rows (e.g., 10 or fewer), the performance difference might be negligible, and using either approach might be fine.
• Database Load: If your database is already under heavy load, the efficiency gains from a multi-row INSERT can be even more significant.
• Transaction Usage: If you're using transactions, a multi-row INSERT within a transaction can be advantageous.

Best Practices:

• For large datasets, use a multi-row INSERT whenever possible.
• If you're unsure, benchmark both approaches with your specific data size and database load to determine the optimal choice.
• Consider using tools or libraries specifically designed for bulk data loading if you're dealing with very large datasets. These tools often provide even better performance than traditional INSERT statements.

-- MySQL/MariaDB
INSERT INTO your_table (column1, column2) VALUES (value1, value2);
INSERT INTO your_table (column1, column2) VALUES (value3, value4);
INSERT INTO your_table (column1, column2) VALUES (value5, value6);

Single Multi-Row INSERT:

-- MySQL/MariaDB
INSERT INTO your_table (column1, column2)
VALUES (value1, value2),
       (value3, value4),
       (value5, value6);

Explanation:

This method allows you to insert data by selecting it from another table. It's useful when you need to copy or transform data from an existing table.

-- MySQL/MariaDB
INSERT INTO target_table (column1, column2)
SELECT column1, column2
FROM source_table;

• Replace column1 and column2 with the actual columns you want to insert (they must also exist in the source table).

LOAD DATA LOCAL INFILE:

This method is particularly suited for bulk loading large datasets from a local file (CSV, text) into a MySQL table. It's very efficient for massive data imports.

-- MySQL (not supported in MariaDB)
LOAD DATA LOCAL INFILE 'your_data.csv'
INTO TABLE your_table
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
(column1, column2, ...);

• Specify the delimiters used in your data file (e.g., , for comma-separated values, \n for newlines).
• List the column names in the order they appear in the data file.

Prepared Statements with Loops:

For situations where you have dynamic data coming from an external source (like user input), you can use prepared statements with loops. This approach offers better security compared to directly embedding user input in the query.

# Example using Python with MySQL Connector/Python

import mysql.connector

mydb = mysql.connector.connect(
    host="localhost",
    user="yourusername",
    password="yourpassword",
    database="yourdatabase"
)

mycursor = mydb.cursor()

sql = "INSERT INTO your_table (column1, column2) VALUES (%s, %s)"

data = [("value1", "value2"), ("value3", "value4"), ("value5", "value6")]

for row in data:
    mycursor.execute(sql, row)

mydb.commit()

mycursor.close()
mydb.close()

• Replace connection details, table names, and column names with your actual values.
• This code iterates through a list of data tuples and executes a prepared statement with each row, preventing SQL injection vulnerabilities.

Choosing the Right Method:

• For standard insertions of a small to medium number of rows, the single multi-row INSERT is generally the best choice.
• For copying data from another table, INSERT ... SELECT becomes useful.
• For extremely large datasets residing on the server's filesystem (MySQL only), LOAD DATA LOCAL INFILE excels in bulk loading.
• If you have dynamic data from an external source, prepared statements with loops provide a secure way to insert multiple rows.