SQLite Data Types: A Close Look at Storage Classes

SQLite uses a flexible type system based on storage classes and type affinity. Unlike traditional RDBMSs that enforce strict typing, SQLite allows more dynamic type handling. Understanding this model is important for ensuring data consistency and avoiding subtle bugs in application logic.

In this article, I will explore the SQLite type system and show you how to work with common data types in SQLite. If you are new to SQL, consider starting with our Introduction to SQL course to build a strong foundation. Also, I find the SQL Basics Cheat Sheet, which you can download, is a helpful reference because it has all the most common SQL functions.

Core Concepts of SQLite’s Type System

To work with SQLite, it is important to grasp how it stores and interprets data internally. I will explain below how a SQLite-type system designs reliable and efficient applications.

Storage classes explained

SQLite uses five storage classes to represent values internally:

• NULL represents a missing or undefined value.

• INTEGER represents a signed integer stored in 1 to 8 bytes, depending on the magnitude.

• REAL describes a floating-point number stored as an 8-byte IEEE-754 value.

• TEXT stores text strings encoded in UTF-8, UTF-16BE, or UTF-16LE.

• BLOB stores binary data exactly as input, without any transformation.

SQLite Data Types. Image Source: OpenAI

These storage classes differ from the declared data types in a table schema. A column may be declared as VARCHAR(50) or BOOLEAN, but the actual value is stored using one of the five storage classes based on its content. This separation gives SQLite its dynamic and forgiving nature, but it also requires developers to be mindful of data validation and type consistency in their application code.

Type affinity and column behavior

Type affinity in SQLite is a recommended type assigned to a column based on its declared data type. SQLite uses the following rules to determine affinity from the declared type’s text.

• Columns declared as VARCHAR, CHAR, or TEXT get TEXT affinity.

• Types like INT and INTEGER get INTEGER affinity.

• Declarations like DECIMAL, NUMERIC, or BOOLEAN get NUMERIC affinity.

• Types like REAL, FLOAT, or DOUBLE get REAL affinity.

• The affinity defaults to BLOB if no recognized type is declared.

For example, a column declared as VARCHAR(100) will have TEXT affinity, meaning SQLite will try to convert values to text when stored. This affinity guides how SQLite coerces and stores data, but does not restrict storing other types in that column.

Manifest typing and its implications

SQLite uses manifest typing, meaning the data type is associated with the individual value, not the column container. This allows multiple types of values to coexist in a single column. For example, a column declared as INTEGER can hold TEXT or BLOB values.

Manifest typing allows developers to insert diverse data types without altering the schema. However, its trade-off is that it can lead to inconsistent data, unpredictable sorting, and more reliance on application logic to enforce type rules.

I recommend taking our Database Design course to learn database management including organizing data in different data types and managing databases.

Working with Common Data Types in SQLite

Now let us look at how to handle common data types in SQLite, such as text, numbers, booleans, dates, and binary data.

Text, numbers, and booleans

SQLite commonly used data declarations include TEXT, INTEGER, REAL, NUMERIC, and BOOLEAN, but stores values based on type affinity, not strict types. The following are use cases for each data type in a real-world schema.

• TEXT stores strings, suitable for names, emails, URLs, and general textual data.

• INTEGER is ideal for whole numbers such as counts, IDs, and codes, offering efficient storage and sorting.

• REAL stores floating-point numbers for measurements, currency, or non-integer values.

• The NUMERIC affinity covers values declared as DECIMAL, BOOLEAN, or similar. Depending on the value, SQLite attempts to store these as INTEGER or REAL.

• BOOLEAN is not a native storage class but is simulated by storing 0 (FALSE) or 1 (TRUE) as INTEGER values behind the scenes.

I recommend using INTEGER for booleans with explicit application-side constraints, while NUMERIC is appropriate for values requiring flexible number parsing.

Dates, times, and temporal strategies

SQLite does not have a native DATE or DATETIME type, but supports the following formats for storing temporal data:

• TEXT: Stores dates/times as ISO8601 strings like YYYY-MM-DD HH:MM:SS. This format is human-readable and compatible with many tools, but can be slower for comparisons.

• REAL: Stores dates as Julian day numbers (floating-point), useful for date arithmetic and range queries, but less readable.

• INTEGER: Stores Unix timestamps, seconds since 1970-01-01. It is efficient for storage and fast numeric comparisons, but not human-readable.

When working with temporal data, you can use these functions: date(), time(), datetime(), julianday(), and strftime() for parsing, formatting, and converting between temporal formats.

Binary and numeric edge cases

SQLite also supports binary data via the BLOB storage class. BLOBs are useful for storing images, files, or any non-textual data. When working with BLOB data types, always use parameterized queries or bindings to insert BLOBs. You can also store large binaries externally and reference them with paths or hashes.

While SQLite can store integers up to 8 bytes (-2^63 to 2^63-1), values beyond this range may be silently converted to REAL data type. This may make the values lose precision. SQLite lacks strict enforcement for decimal precision, so always use external libraries or application logic for financial data when you need precision.

Enforcing structure with STRICT tables

Unlike the traditional flexible typing system, SQLite introduced STRICT tables in version 3.37.0. A STRICT table provides a stricter typing mode that enforces exact type matching for column values. When you declare a table as STRICT, every value inserted must match the column’s declared type, and any mismatch results in an error. 

The STRICT mode improves data consistency and validation. It also helps catch type-related errors early during development and makes SQLite's behavior more predictable, closer to traditional RDBMSs.

Before using STRICT as a developer, you should note that it is only available in SQLite 3.37.0 and above. Also, you cannot mix STRICT and manifest typing in the same table.

Performance and Design Considerations

SQLite has a flexible type system with performance implications. SQLite uses variable-length encoding for INTEGER and REAL values, meaning smaller numbers use fewer bytes. This helps minimize database size, especially for large datasets. 

Type affinity also influences how values are stored and compared. Inconsistent types in a column can reduce the effectiveness of indexes, since SQLite may perform extra type conversions during lookups, slowing down queries.

Consider the following strategies to design efficient schemas and queries.

• Define columns with clear and consistent affinities to minimize type coercion overhead.

• Consider explicit casting in queries when type conversion is needed to ensure consistent behavior.

• Use INTEGER affinity for numeric IDs and counters, REAL for floating-point data, and TEXT for strings.

• Avoid mixing data types in the same column to prevent sorting and comparison quirks.

• Normalize schemas to avoid redundant type coercion or data duplication.

• Use EXPLAIN QUERY PLAN to analyze performance and fine-tune queries.

SQLite Data Type Examples with Schema Patterns

The following table schemas illustrate the thoughtful use of SQLite’s data types, type affinity, and constraints.

Users table with boolean simulation and constraints

In the example below:

• is_active simulates a boolean using INTEGER with a CHECK constraint.

• TEXT data type ensures consistency for string values while INTEGER is for boolean-like logic.

 -- Create users table with auto-incrementing rowid and enforce uniqueness
CREATE TABLE users (
    id INTEGER PRIMARY KEY, 
    username TEXT NOT NULL UNIQUE, 
    email TEXT NOT NULL, 
    is_active INTEGER NOT NULL DEFAULT 1 CHECK (is_active IN (0, 1))  -- Simulates boolean
);

Orders table using temporal data and numeric precision

In the query below, total uses NUMERIC for currency-like values to handle precision at the application level. order_date is stored as TEXT in ISO-8601 format, ensuring readability and compatibility with date functions.

-- Create orders table with row identifier and foreign key in practice
-- ISO date format "YYYY-MM-DD"
CREATE TABLE orders (
    order_id INTEGER PRIMARY KEY,  
    customer_id INTEGER NOT NULL, 
    total NUMERIC NOT NULL, 
    order_date TEXT NOT NULL CHECK (length(order_date) = 10)  );

Files table with binary and type checks

In the query below, content uses BLOB to store raw binary data. uploaded_at stores time as an INTEGER for space and performance efficiency.

-- Create files table with raw binary data and Unix timestamp (epoch seconds)
CREATE TABLE files (
    file_id INTEGER PRIMARY KEY, 
    name TEXT NOT NULL, 
    content BLOB NOT NULL,  
    uploaded_at INTEGER NOT NULL 
);

You can verify the type at runtime with the following query:

SELECT typeof(uploaded_at), typeof(content) FROM files;

I recommend taking our Introduction to Relational Databases in SQL course to learn more about data structures and how to create tables in databases.

Comparing SQLite to Other Databases

SQLite’s type system is more flexible than traditional RDBMSs like MySQL or PostgreSQL. The table below compares SQLite to these databases.

Conclusion

SQLite has a flexible typing system that offers different advantages when used thoughtfully. Developers can design schemas that balance adaptability with reliability by understanding storage classes, type affinity, and manifest typing. Features like STRICT tables, consistent type usage, and normalization help enforce structure where needed.

As a next step, I recommend taking our Exploratory Data Analysis in SQL and Data Manipulation in SQL courses to learn how to analyze data of different data types using advanced SQL queries.