After seeing SO SO many benchmarks of Node.js vs GO where GO by default can use full CPU cores whereas Node.js is just run without clustering, I decided to do my own full benchmark on my machine with and without clustering Node.js to utilize full CPU cores.

My machine specs:

1] Macos, 16'inch 2019 model, I7 - 6 core 2.6 GH processor, 32GB RAM

The test:

Exposed a simple POST /benchmark API on ALL the servers. The api receives 1.4KB of JSON as a POST body payload, converts the JSON to native JSON object (in GO's case native struct, in Node.js/Bun/Deno's case native JSON object) and responds back with the EXACT same JSON (so it de-serializes the JSON back to a stringified one to be able to send the JSON back to caller).

Benchmark command:

wrk -t8 -c5000 -d120s -s ./post.lua --latency <URL>

Results:

TLDR:

1] With Clustering, the speed difference between Node.js/fastify vs GO (with GIN) is quite close. GO is 1.2x faster than Node.js/fastify with clustering and utilizing all CPU cores under very high load

2] Fastify speed is near identical (or even slightly better) to native Node.js HTTP module with/without clustering

3] GO (with GIN) uses almost 2x the CPU (835%) to achieve 1.2x the speed of Node.js/fastify (432% CPU) (with clustering)

4] GO (with GIN) uses 12x LESS memory compared to Node.js/fastify with clustering to achieve 1.2x more speed.

5] Bun actually is ALMOST 3x faster than Node.js (with/without fastify) on a single core without clustering. (Their claim was NOT WRONG).

6] Deno.js is ALMOST 1.5x faster than Node.js (with/without fastify) on a single core without clustering

7] Bun is ALMOST 2x faster than Deno on a single core without clustering.

8] Express.js has fallen SO SO MUCH behind and utilizes more CPU/ram and is SIGNIFICANTLY slower than everything, its not even comparable.

I hope this would be useful for the community here to have a good idea of how big a difference between Node vs GO vs Bun vs Deno vs Fastify vs Express could be.

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

Documentation: https://fastapi.tiangolo.com

Source Code: https://github.com/tiangolo/fastapi

Key Features

• Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.

• Fast to code: Increase the speed to develop new features.

• Fewer bugs: Reduce a high amount of human (developer) induced errors.

• Intuitive: Great editor support. Completion (also known as auto-complete, autocompletion, IntelliSense) everywhere. Less time debugging.

• Easy: Designed to be easy to use and learn. Less time reading docs.

• Short: Minimize code duplication. Multiple features from each parameter declaration. Less bugs.

• Robust: Get production-ready code. With automatic interactive documentation.

• Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

Installation

$ pip install fastapi

You will also need an ASGI server, for production such as Uvicorn.

$ pip install uvicorn

Example

Create it

• Create a file main.py with:

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

Or use async def...

Check it

Open your browser at http://127.0.0.1:8000/items/5?q=somequery.

You will see the JSON response as:

{"item_id": 5, "q": "somequery"}

You already created an API that:

• Receives HTTP requests in the paths / and /items/{item_id}.

• Both paths take GET operations (also known as HTTP methods).

• The path /items/{item_id} has a path parameter item_id that should be an int.

• The path /items/{item_id} has an optional str query parameter q.

Interactive API docs

Now go to http://127.0.0.1:8000/docs.

You will see the automatic interactive API documentation (provided by Swagger UI):

Alternative API docs

And now, go to http://127.0.0.1:8000/redoc.

You will see the alternative automatic documentation (provided by ReDoc):