SQLite BLOB Data Type: Storing Binary Data in SQLite

Have you ever needed to store binary data like images, audio files, or multimedia in an SQLite database? If so, you’ll be happy to know that SQLite has a data type specifically for this purpose – SQLite BLOB.

SQLite BLOB stands for Binary Large OBject and is perfect for storing large amounts of binary data in your SQLite database.

SQLite BLOBs (Binary Large OBjects) provide specialized storage for large binary data like files, images, and videos. BLOB usage trades off disk space for convenience. Create BLOB columns, insert data through hex encoding or files, and query/retrieve as byte arrays.

In this comprehensive guide, you’ll learn all about using BLOBs in SQLite through easy-to-understand examples. We’ll cover:

• What BLOBs are and when to use them
• Creating BLOB columns in SQLite tables
• Inserting binary data into BLOB columns
• Querying and retrieving BLOB data
• Working with BLOBs in Python and other programming languages
• Advantages and limitations of using BLOBs in SQLite

So, if you’re ready to learn the ins and outs of storing binary data in SQLite, let’s get started!

An Introduction to SQLite BLOBs

Before diving into the details, let’s first understand SQLite BLOBs. A BLOB is a data type in SQLite used to store arbitrary binary data.

Some key things to know about BLOBs:

• SQLite BLOB stands for Binary Large OBject
• BLOBs can store up to 2 terabytes of data
• SQLite does not interpret the data stored in BLOBs
• BLOBs are stored exactly as input, as a “chunk” of binary data

Common uses of BLOBs include storing:

• Images
• Audio/video files
• PDF documents
• Zip archives
• Serialized objects
• HTTP request/response bodies

Basically, any binary data can be stored efficiently in an SQLite BLOB.

The advantage of using BLOBs over just storing binary data in a text field is that BLOBs are treated specially by SQLite – they aren’t subject to any character encoding, don’t require escaping, and allow for streamlined storage and retrieval of binary data.

Now, let’s look at how actually to work with SQLite BLOBs!

Creating a Table with a SQLite BLOB Column

To store binary data in SQLite, first, you need to create a table with one or more BLOB columns.

Here is an example of creating a simple “images” table with an ID column and a BLOB column to hold the image data:

CREATE TABLE images (
  id INTEGER PRIMARY KEY, 
  image BLOB
);

Read: SQLite Create Table

We specify that the image column has a data type of BLOB which tells SQLite to expect binary data.

That’s it! We now have a table ready to store binary image data in the image column.

You can also optionally specify a size for the SQLite BLOB column, like:

image BLOB(10MB)

This sets the maximum size for blobs in that column. But in most cases, this is unnecessary as the default maximum BLOB size is 2TB.

Inserting Data into a SQLite BLOB Column

Now let’s look at how actually to insert binary data into our images table.

There are a couple of approaches:

1. Hex Encoding

You can insert binary data by providing a hex-encoded string.

For example:

INSERT INTO images (id, image)
VALUES
  (1, X'89504E470D0A1A0A0000000D494844520000002D00000028080600000006734D4F20000000017352474200AECE1CE90000000467414D410000B18F0BFC61050000000970485973000012740000127401DE661F780000000774494D4507E4091101F3276D7EB8F');

Here we are inserting a simple 28×28 pixel image as hex-encoded bytes.

The X' ' prefix tells SQLite to interpret the following data as hex-encoded binary.

2. Reading from a File

You can also insert BLOB data by reading from a file on disk.

For example, assuming we had a file image.png we want to insert:

INSERT INTO images (id, image) 
VALUES (2, readfile('image.png')); 

This will read the binary contents of image.png and insert it into the BLOB column.

The readfile() function reads file contents as a BLOB.

Querying and Retrieving SQLite BLOB Data

Once binary data is in a BLOB column, you’ll want to be able to query and retrieve it later.

Querying SQLite BLOB Columns

You can query BLOB columns just like any other column:

SELECT id, image FROM images WHERE id = 1;

This will return the id and image BLOB data for the row with id 1.

One difference with BLOB columns is you generally don’t want to SELECT * or select the BLOB data when you don’t need it. Binary data like images can be large, so you should try to avoid pulling that data into memory if you don’t need it.

Retrieving and Working with BLOB Data

To do something useful with the binary data returned, you need to read the BLOB content.

Let’s say you wanted to retrieve the image for id 1 and save it as a PNG file:

import sqlite3

conn = sqlite3.connect('database.db')

cursor = conn.execute('SELECT image FROM images WHERE id = 1')
blob_data = cursor.fetchone()[0] 

with open('image.png', 'wb') as file:
  file.write(blob_data)

conn.close()

Here we:

1. Execute a query to get the BLOB data
2. Retrieve the binary data into the variable blob_data
3. Write that data into a PNG file

And that’s it! We’ve essentially retrieved the BLOB from SQLite and converted it back into an image file.

This same pattern can be used to retrieve any type of binary data from a BLOB column – like audio files, PDFs, etc.

BLOB Storage in SQLite

Now that we’ve covered the basics of working with BLOBs in SQLite, let’s talk a little bit about how they are stored under the hood.

In SQLite, BLOB data is stored directly in the database file alongside normal row data. But there are some important considerations about how this works:

• BLOBs cannot be indexed – they are stored as raw binary data, which cannot be indexed like text or other types of data.
• BLOBs do not compress well – the data is stored verbatim, so compression achieved in the database file will be minimal.
• BLOBs increase database file size – all that binary data has to go somewhere, so expect your database file size to increase proportional to the size of BLOB data stored.
• BLOBs can cause fragmentation – like large objects in file systems, BLOBs can lead to fragmentation in the database file.

In practice this means:

• Storing lots of BLOBs, especially large ones, can impact performance and increase file size.
• You generally want to avoid pulling BLOB data into memory unless needed.
• Take care to benchmark and optimize BLOB usage if storing large amounts.
• Use shorter columns names, like img instead of image to reduce overhead.

So while BLOBs are great for binary data, they have tradeoffs to be aware of when designing your database schema.

Using SQLite BLOBs in Different Languages

So far all the examples have been for SQLite itself, using SQL queries to work with BLOBs.

But many applications use SQLite through a programming language like Python, Java, C#, etc.

The good news is that in most languages, working with BLOB columns is straightforward:

• Fetching BLOB data – this is done using normal querying of the BLOB column, which returns a byte array.
• Inserting BLOB data – this is done by passing a byte array into parameters bound to a BLOB column.
• Converting BLOB to/from Files – you can write the byte array to a file and vice versa.

Here is some sample Python code showing how this works:

# Insert blob data from file 
with open("image.png", "rb") as f:
  blob_data = f.read()
  
cursor.execute("INSERT INTO images (id, image) VALUES (?, ?)", 
  (1, blob_data))
  
# Query and retrieve blob data
cursor.execute("SELECT image FROM images WHERE id = 1")  
blob_data = cursor.fetchone()[0]

# Save blob as file
with open("output.png", "wb") as f:
  f.write(blob_data)

As you can see, Python provides simple APIs for working with binary data as byte arrays that map nicely to SQLite’s BLOB data.

The same is true for most other languages – just treat the BLOB data as a byte array and you can easily store and retrieve binary data.

Wrapping Up

To summarize, here are some key things we learned about working with binary data using the SQLite BLOB data type:

• BLOBs are designed for storing large binary data like files, images, and multimedia.
• Create BLOB columns by specifying BLOB as the data type.
• Insert binary data as hex-encoded strings or by reading from files.
• Query BLOB columns like normal but avoid pulling unneeded data.
• Retrieve BLOB data into byte arrays and write to files.
• Be aware of size and performance implications of storing BLOBs.
• Most languages make it easy to work with BLOBs through byte arrays.

Storing binary data in SQLite using the BLOB data type is simple and effective. Just be mindful of the size and performance tradeoffs.

I hope this guide gave you a good foundation for working with binary data in SQLite. Let me know if you have any other questions!