SQLite Cursor.description Missing Data Type Information: Causes and Workarounds

SQLite’s Type Handling and cursor.description Behavior

SQLite’s dynamic typing system and its interaction with Python’s DB API specification create a unique challenge when retrieving column data types via cursor.description. In SQLite, columns have type affinity rather than rigid data types, allowing values of any type to be stored in any column. This contrasts with databases like DuckDB, which enforce strict column typing. The Python sqlite3 module intentionally omits type information in cursor.description tuples to comply with the Python DB API’s requirements while acknowledging SQLite’s type flexibility. Developers expecting standardized type metadata must reconcile SQLite’s "flexible columns" philosophy with traditional relational database expectations.

Why SQLite Doesn’t Expose Data Types in Cursor.description

1. Type Affinity vs. Static Typing:
SQLite uses type affinity, where column definitions (e.g., INTEGER, TEXT) suggest preferred storage classes rather than enforcing them. A column declared as INTEGER can store text values, and vice versa. This makes it impossible to guarantee that a column’s declared type matches its actual contents, rendering traditional data type reporting misleading. DuckDB and other databases enforce strict typing, allowing them to confidently return column types.

2. Python DB API Compliance:
The Python sqlite3 module implements PEP 249, which specifies that cursor.description should return a 7-tuple where only the first item (column name) is guaranteed. The remaining items, including data type, are optional. By returning None for type-related fields, the module avoids misrepresenting SQLite’s type flexibility while maintaining API compatibility.

3. Implementation Boundaries:
The SQLite core library provides limited type metadata through APIs like sqlite3_column_type(), which returns the storage class of a specific value in a result set—not the column’s declared affinity. The Python sqlite3 module doesn’t expose this per-value type information in cursor.description, which is designed to describe columns, not individual values.

Retrieving Column Type Information in SQLite

1. Querying sqlite_master for Declared Affinity:
While cursor.description lacks type data, SQLite stores a column’s declared type affinity in the sqlite_master system table. For a table created with:

CREATE TABLE t (a INTEGER, b TEXT);

the affinity can be retrieved via:

cursor.execute("SELECT sql FROM sqlite_master WHERE name='t'")

This returns the original CREATE TABLE statement, which can be parsed to extract declared types. However, this approach has limitations:

• Doesn’t work for computed columns or temporary tables
• Returns the declared affinity, not the runtime storage class
• Requires parsing SQL schemas manually

2. Using PRAGMA table_info:
The PRAGMA table_info(table_name) command provides structured metadata:

cursor.execute("PRAGMA table_info(t)")
print(cursor.fetchall())

Output:

(0, 'a', 'INTEGER', 0, None, 0),
(1, 'b', 'TEXT', 0, None, 0)

The third field in each tuple contains the declared type. This is more reliable than parsing sqlite_master but still reflects affinity rather than actual value types.

3. Custom Cursor Subclassing:
Developers can extend sqlite3.Cursor to augment description with type affinity:

class TypedCursor(sqlite3.Cursor):
    def _get_description(self):
        # Fetch original description
        desc = super().description
        # Fetch declared types via PRAGMA
        self.execute(f"PRAGMA table_info({self.table_name})")
        type_map = {row[1]: row[2] for row in self.fetchall()}
        # Augment description tuples
        return [
            (col[0], type_map.get(col[0], None), *col[2:]) 
            for col in desc
        ]
    
    @property
    def description(self):
        return self._get_description()

This approach merges PRAGMA-derived type information with the standard description. Note that this requires tracking the table name being queried, which may not be straightforward for complex joins.

4. Runtime Type Inference:
For result sets where actual value types matter more than declared affinity, use sqlite3_column_type() via a custom function:

import sqlite3
from sqlite3 import Connection, Cursor

def get_value_types(cursor: Cursor):
    """Return storage classes for current result set."""
    return [cursor.connection._get_column_type(i) 
            for i in range(len(cursor.description))]

# Monkey-patch the Cursor class
sqlite3.Cursor.value_types = property(get_value_types)

# Usage:
cursor.execute("SELECT a, b FROM t")
print(cursor.value_types)  # e.g., [1, 3] (SQLITE_INTEGER, SQLITE_TEXT)

This requires accessing SQLite’s internal APIs via ctypes or a C extension, as the Python module doesn’t expose sqlite3_column_type() directly. Storage class codes are:

• 1: INTEGER
• 2: FLOAT
• 3: TEXT
• 4: BLOB
• 5: NULL

5. DuckDB-Style Type Reporting:
For projects requiring strict typing, consider using DuckDB’s Python API when:

• Working with analytical workloads
• Needing enforced column types
• Prioritizing type metadata in result sets

Migration example:

import duckdb

# DuckDB provides type info in cursor.description
cursor = duckdb.connect().cursor()
cursor.execute("CREATE TABLE t AS SELECT 1 a, 'a' b")
cursor.execute("SELECT * FROM t")
print(cursor.description)  # Shows 'NUMBER' and 'STRING'

6. Python Enhancement Proposal (PEP):
To change sqlite3‘s behavior, propose a new PEP or file a CPython issue suggesting:

• Optional type affinity reporting via a detect_types flag
• A new description_types cursor attribute
• Integration with SQLite’s typeof() function

Example proposal:

conn = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLUMN_TYPES)
cursor = conn.execute("SELECT a, b FROM t")
print(cursor.description_types)  # Hypothetical new attribute

7. Hybrid Type Caching:
Combine schema inspection and runtime checks for comprehensive metadata:

def get_column_metadata(cursor: sqlite3.Cursor):
    # Get declared affinity
    table_name = parse_table_name(cursor)  # Custom logic
    cursor.execute(f"PRAGMA table_info({table_name})")
    affinities = {row[1]: row[2] for row in cursor.fetchall()}
    
    # Get runtime storage classes
    storage_classes = [
        cursor.connection._get_column_type(i) 
        for i in range(len(cursor.description))
    ]
    
    return [
        (name, affinities.get(name, None), storage_classes[i])
        for i, (name, *_) in enumerate(cursor.description)
    ]

8. Third-Party Wrappers:
Libraries like records or sqlalchemy provide higher-level abstractions:

from sqlalchemy import create_engine

engine = create_engine("sqlite:///:memory:")
result = engine.execute("SELECT * FROM t")
print(result.keys())         # Column names
print(result.cursor.description)  # SQLAlchemy-enhanced metadata

Key Considerations When Working with SQLite Types

• Affinity vs. Storage Class: A column with INTEGER affinity may contain TEXT values. Use typeof() in queries to inspect values:

  SELECT a, typeof(a) FROM t;
  

• Dynamic Typing Implications: Applications assuming column types based on affinity may break when unexpected data types appear.
• Performance Tradeoffs: Runtime type checking (e.g., PRAGMA calls) adds overhead. Cache metadata where possible.
• Cross-Database Compatibility: Code relying on SQLite’s type affinity won’t port directly to strictly-typed databases without schema adjustments.

Developers must choose between SQLite’s flexibility and DuckDB-style strict typing based on their use case. While workarounds exist to approximate column type reporting, they require acknowledging SQLite’s fundamental design philosophy around dynamic typing. For Python-specific solutions, advocating for enhanced type metadata in future sqlite3 module versions remains the most sustainable long-term approach.