FastAPI UploadFile is slow compared to Flask

Question

I have created an endpoint, as shown below:

@app.post("/report/upload")
def create_upload_files(files: UploadFile = File(...)):
        try:
            with open(files.filename,'wb+') as wf:
                wf.write(file.file.read())
                wf.close()
        except Exception as e:
            return {"error": e.__str__()}

It is launched with uvicorn:

../venv/bin/uvicorn test_upload:app --host=0.0.0.0 --port=5000 --reload

I am performing some tests of uploading a file of around 100 MB using Python requests, and takes around 128 seconds:

f = open(sys.argv[1],"rb").read()
hex_convert = binascii.hexlify(f)
items = {"files": hex_convert.decode()}
start = time.time()
r = requests.post("http://192.168.0.90:5000/report/upload",files=items)
end = time.time() - start
print(end)

I tested the same upload script with an API endpoint using Flask and takes around 0.5 seconds:

from flask import Flask, render_template, request
app = Flask(__name__)


@app.route('/uploader', methods = ['GET', 'POST'])
def upload_file():
   if request.method == 'POST':
      f = request.files['file']
      f.save(f.filename)
      return 'file uploaded successfully'

if __name__ == '__main__':
    app.run(host="192.168.0.90",port=9000)

Is there anything I am doing wrong?

Answer 1

You can define the endpoint with async , and since allUploadFile methods are async methods, you need to await them. You can write the file(s) using synchronous writing, as shown in this answer , or ( better ) using asynchronous writing with aiofiles , as shown below:

Upload Single File

app.py

from fastapi import File, UploadFile
import aiofiles

@app.post("/upload")
async def upload(file: UploadFile = File(...)):
    try:
        contents = await file.read()
        async with aiofiles.open(file.filename, 'wb') as f:
            await f.write(contents)
    except Exception:
        return {"message": "There was an error uploading the file"}
    finally:
        await file.close()

    return {"message": f"Successfuly uploaded {file.filename}"}

or async in the chunked manner, to avoid loading the entire file into memory . Though, this one takes much longer to complete (depending on the chunk size you choose).

from fastapi import File, UploadFile
import aiofiles

@app.post("/upload")
async def upload(file: UploadFile = File(...)):
    try:
        async with aiofiles.open(file.filename, 'wb') as f:
            while contents := await file.read(1024): # async read chunk
                await f.write(contents)
    except Exception:
        return {"message": "There was an error uploading the file"}
    finally:
        await file.close()

    return {"message": f"Successfuly uploaded {file.filename}"}

test.py

import requests

url = 'http://127.0.0.1:8000/upload'
file = {'file': open('images/1.png', 'rb')}
resp = requests.post(url=url, files=file) 
print(resp.json())

Upload Multiple Files

app.py

from fastapi import File, UploadFile
import aiofiles

@app.post("/upload")
async def upload(files: List[UploadFile] = File(...)):
    for file in files:
        try:
            contents = await file.read()
            async with aiofiles.open(file.filename, 'wb') as f:
                await f.write(contents)
        except Exception:
            return {"message": "There was an error uploading the file(s)"}
        finally:
            await file.close()

    return {"message": f"Successfuly uploaded {[file.filename for file in files]}"}  

test.py

import requests

url = 'http://127.0.0.1:8000/upload'
files = [('files', open('images/1.png', 'rb')), ('files', open('images/2.png', 'rb'))]
resp = requests.post(url=url, files=files) 
print(resp.json())

Update

Digging into the source code, it seems that the latest versions of Starlette (which FastAPI uses underneath) use a SpooledTemporaryFile (for UploadFile data structure) with max_size attribute set to 1 MB (1024 * 1024 bytes) - see here - in contrast to older versions where max_size was set to the default value, ie, 0 bytes, such as the one here .

The above means, in the past, data used to be fully loaded into memory no matter the size of file (which could lead to issues when a file couldn't fit into RAM), whereas, in the latest version, data is spooled in memory until the file size exceeds max_size (ie, 1 MB), at which point the contents are written to disk; more specifically, to the OS's temporary directory (Note: this also means that the maximum size of file you can upload is bound by the storage available to the system's temporary directory. . If enough storage (for your needs) is available on your system, there's nothing to worry about; otherwise, please have a look at this answer on how to change the default temporary directory). Thus, the process of writing the file multiple times - that is, initially loading the data into RAM, then, if the data exceeds 1 MB in size, writing the file to temporary directory, then reading the file from temporary directory (using file.read() ) and finally, writing the file to a permanent directory - is what makes uploading file slow compared to using Flask framework, as OP noted in their question (though, the difference in time is not that big, but just a few seconds, depending on the size of file).

Solution

The solution (if one needs to upload files quite larger than 1 MB and uploading time is important to them) would be to access the request body as a stream. As per Starlette documentation , if you access .stream() , then the byte chunks are provided without storing the entire body to memory (and later to temporary directory, if the body contains file data that exceeds 1 MB). Example is given below, where time of uploading is recorded on client side, and which ends up being the same as when using Flask framework with the example given in OP's question.

app.py

from fastapi import Request
import aiofiles

@app.post('/upload')
async def upload(request: Request):
    try:
        filename = request.headers['filename']
        async with aiofiles.open(filename, 'wb') as f:
            async for chunk in request.stream():
                await f.write(chunk)
    except Exception:
        return {"message": "There was an error uploading the file"}
     
    return {"message": f"Successfuly uploaded {filename}"}

In case your application does not require saving the file to disk, and all you need is the file to be loaded directly into memory, you can just use the below (make sure your RAM has enough space available to accommodate the accumulated data):

from fastapi import Request

@app.post('/upload')
async def upload(request: Request):
    body = b''
    try:
        filename = request.headers['filename']
        async for chunk in request.stream():
            body += chunk
    except Exception:
        return {"message": "There was an error uploading the file"}
    
    #print(body.decode())
    return {"message": f"Successfuly uploaded {filename}"}

test.py

import requests
import time

with open("images/1.png", "rb") as f:
    data = f.read()
   
url = 'http://127.0.0.1:8000/upload'
headers = {'filename': '1.png'}

start = time.time()
resp = requests.post(url=url, data=data, headers=headers)
end = time.time() - start

print(f'Elapsed time is {end} seconds.', '\n')
print(resp.json())