Detailed Comparison: DuckDB vs SQLite3

DuckDB vs SQLite3: A Comparative Analysis

DuckDB and SQLite3 are both lightweight, self-contained database systems. However, they serve different purposes and are optimized for specific use cases. DuckDB excels in analytical queries and data science workflows, while SQLite3 is optimized for simple transactional operations in embedded systems and applications. This article explores their differences, use cases, and performance aspects.

Key Differences Between DuckDB and SQLite3

Syntax and Use Cases

1. SQLite3 Syntax Example

SQLite3 is known for its ease of use in embedded systems.

Code:

-- Create a table
CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    name TEXT,
    age INTEGER
);

-- Insert data into the table
INSERT INTO users (name, age) VALUES ('Alice', 30);

-- Query the table
SELECT * FROM users;

Explanation:

• SQLite3 is suitable for mobile apps, IoT devices, and small-scale applications where storage efficiency is essential.

2. DuckDB Syntax Example

DuckDB is designed for analytical tasks and data exploration.

Code:

-- Create a table
CREATE TABLE sales (
    id INTEGER,
    product TEXT,
    amount DECIMAL,
    date DATE
);

-- Load data from a CSV file
COPY sales FROM 'sales_data.csv' (DELIMITER ',');

-- Run an analytical query
SELECT product, SUM(amount) AS total_sales
FROM sales
GROUP BY product
ORDER BY total_sales DESC;

Explanation:

• DuckDB is ideal for data analysis tasks, handling CSV, Parquet files, and other formats seamlessly.

Performance Comparison

1. Transactional Workloads

SQLite3 is highly efficient for transactional workloads with limited concurrency needs, making it perfect for embedded systems.

2. Analytical Queries

DuckDB performs better for analytical queries due to its columnar storage format and ability to process large datasets efficiently.

3. Data Formats

DuckDB supports multiple data formats like CSV and Parquet, making it flexible for data science workflows. SQLite3, on the other hand, sticks to its .db format.

Code Comparison

Python Integration with SQLite3

Code:

import sqlite3

# Connect to SQLite database
conn = sqlite3.connect('example.db')

# Create a cursor object
cursor = conn.cursor()

# Create a table
cursor.execute('''CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER)''')

# Insert data into the table
cursor.execute('INSERT INTO users (name, age) VALUES (?, ?)', ('Alice', 30))

# Query data
cursor.execute('SELECT * FROM users')
print(cursor.fetchall())

# Close the connection
conn.close()

Python Integration with DuckDB

Code:

import duckdb

# Connect to DuckDB in-memory database
conn = duckdb.connect(database=':memory:')

# Create a table
conn.execute('''CREATE TABLE sales (id INTEGER, product TEXT, amount DECIMAL, date DATE)''')

# Load data from a CSV file
conn.execute("COPY sales FROM 'sales_data.csv' (DELIMITER ',')")

# Run an analytical query
result = conn.execute('SELECT product, SUM(amount) AS total_sales FROM sales GROUP BY product ORDER BY total_sales DESC').fetchall()

# Print results
print(result)

Use Cases

SQLite3

• Embedded databases in mobile and desktop applications.
• Small-scale transactional operations.
• IoT devices.

DuckDB

• Large-scale data analysis.
• Data science workflows using CSV and Parquet files.
• OLAP queries in-memory or on-disk.

Pros and Cons

SQLite3

• Pros: Simple, lightweight, and serverless.
• Cons: Limited concurrency and lacks advanced analytics.

DuckDB

• Pros: Fast analytics, supports various data formats.
• Cons: Not ideal for small-scale, transactional use cases.

Conclusion

Both DuckDB and SQLite3 serve distinct purposes. Choose SQLite3 for lightweight, embedded applications with transactional needs. Opt for DuckDB when dealing with analytical queries and large-scale data workflows.

Practical Guides to SQLite Snippets and Examples.