Efficiently Storing and Retrieving Large Binary Data with MySQL BLOB
Robert Cooper
Robert Cooper Senior Engineer at Basedash
· January 31, 2025
Robert Cooper
Robert Cooper Senior Engineer at Basedash
· January 31, 2025
Storing and retrieving BLOB data (Binary Large Objects) in MySQL caters to applications that manage large binary data like images, audio, or video files. MySQL’s BLOB columns accommodate varying amounts of data up to 4 GB, offering four types: TINYBLOB, BLOB, MEDIUMBLOB, and LONGBLOB, each with different storage capacities.
BLOB data type in MySQL?The BLOB data type stores binary large objects, with four variations differing in size:
TINYBLOB: Holds up to 255 bytesBLOB: Holds up to 65,535 bytes (64 KB)MEDIUMBLOB: Holds up to 16,777,215 bytes (16 MB)LONGBLOB: Holds up to 4,294,967,295 bytes (4 GB)Select the BLOB type based on the maximum size of data you anticipate storing.
BLOB dataCreate a table with a BLOB column to store BLOB data, such as images:
CREATE TABLE images (
id INT AUTO_INCREMENT PRIMARY KEY,
image BLOB
);
To insert BLOB data, use the INSERT statement along with the LOAD_FILE() function, ensuring the file is accessible to the MySQL server and you have the FILE privilege.
INSERT INTO images (image) VALUES (LOAD_FILE('/path/to/your/image.jpg'));
Alternatively, employ a programming language like Python or PHP to read the file contents and insert them into the database.
BLOB dataTo fetch BLOB data, select the BLOB column from your table and process the binary data in your application. For example, to retrieve an image stored in a BLOB column:
SELECT image FROM images WHERE id = 1;
Then, use the binary data from the query result in your application, whether displaying the image on a web page or saving it to a file.
BLOB type that matches your data size needs.BLOBs, as this can slow down load times and increase memory usage.Efficiently managing BLOB data enhances your applications’ ability to process large binary objects seamlessly.
Written by
Senior Engineer at Basedash
Robert Cooper is a senior engineer at Basedash who builds full-stack product systems across SQL data infrastructure, APIs, and frontend architecture. His work focuses on application performance, developer velocity, and reliable self-hosted workflows that make data operations easier for teams at scale.
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