Comparing FastAPI with DuckDB vs ClickHouse: Performance Showdown

Title:

Comparing FastAPI with DuckDB vs ClickHouse: Performance Showdown

Description:

Explore how to use FastAPI with DuckDB and ClickHouse for CRUD operations. Discover why FastAPI with ClickHouse delivers superior performance in high-load environments.

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DuckDB vs ClickHouse: Overview

DuckDB:

• Use case: Embedded analytics, OLAP tasks on smaller datasets.
• Performance: Optimized for single-node, in-memory computations.
• Strengths: Lightweight, easy to set up for local data analysis.

ClickHouse:

• Use case: Distributed OLAP, real-time analytics on large datasets.
• Performance: Highly optimized for massive parallel processing and disk I/O efficiency.
• Strengths: Handles high-velocity data ingestion and complex queries at scale.

Setting Up FastAPI with DuckDB

1. Install dependencies:

   pip install fastapi uvicorn duckdb

2. Create the FastAPI app with DuckDB:

   from fastapi import FastAPI
   import duckdb

   app = FastAPI()
   conn = duckdb.connect('my_database.db')

   @app.post("/users")
   def create_user(name: str, email: str):
       conn.execute("INSERT INTO users (name, email) VALUES (?, ?)", (name, email))
       return {"message": "User created"}

   @app.get("/users/{user_id}")
   def read_user(user_id: int):
       user = conn.execute("SELECT * FROM users WHERE id = ?", (user_id,)).fetchone()
       return {"user": user}

   @app.put("/users/{user_id}")
   def update_user(user_id: int, name: str, email: str):
       conn.execute("UPDATE users SET name = ?, email = ? WHERE id = ?", (name, email, user_id))
       return {"message": "User updated"}

   @app.delete("/users/{user_id}")
   def delete_user(user_id: int):
       conn.execute("DELETE FROM users WHERE id = ?", (user_id,))
       return {"message": "User deleted"}

3. Run the server:

   uvicorn main:app --reload

Setting Up FastAPI with ClickHouse

1. Install dependencies:

   pip install fastapi uvicorn clickhouse-driver

2. Create the FastAPI app with ClickHouse:

   from fastapi import FastAPI
   from clickhouse_driver import Client

   app = FastAPI()
   client = Client('localhost')

   @app.post("/users")
   def create_user(name: str, email: str):
       client.execute("INSERT INTO users (name, email) VALUES", [(name, email)])
       return {"message": "User created"}

   @app.get("/users/{user_id}")
   def read_user(user_id: int):
       user = client.execute("SELECT * FROM users WHERE id = ?", [user_id])
       return {"user": user[0]}

   @app.put("/users/{user_id}")
   def update_user(user_id: int, name: str, email: str):
       client.execute("ALTER TABLE users UPDATE name = ?, email = ? WHERE id = ?", (name, email, user_id))
       return {"message": "User updated"}

   @app.delete("/users/{user_id}")
   def delete_user(user_id: int):
       client.execute("ALTER TABLE users DELETE WHERE id = ?", [user_id])
       return {"message": "User deleted"}

3. Run the server:

   uvicorn main:app --reload

Why ClickHouse with FastAPI Outperforms DuckDB

• Parallelism: ClickHouse is built for distributed query processing, enabling it to handle large datasets efficiently across multiple nodes, unlike DuckDB which is designed for single-node analytics.
• I/O Efficiency: ClickHouse excels at handling disk-based queries, minimizing I/O bottlenecks, which makes it better suited for real-time analytics with high ingestion rates.
• High Availability: ClickHouse’s replication and sharding mechanisms make it ideal for fault-tolerant, high-throughput environments, offering superior performance in production scenarios compared to DuckDB.

In summary, if you’re working with high-velocity data streams or need horizontal scalability, FastAPI with ClickHouse offers a much faster and more robust solution than using FastAPI with DuckDB, which is better suited for lightweight, local analytics.