How To Upload A Large File (≥3GB) To FastAPI Backend?
Introduction
Uploading large files to a server can be a challenging task, especially when the server has limited memory resources. In this article, we will explore how to upload large files (≥3GB) to a FastAPI backend without loading the entire file into memory. We will use the stream module to read the file in chunks, allowing us to handle large files efficiently.
Problem Statement
When uploading large files to a server, it's common to encounter memory issues. This is because the entire file is loaded into memory, which can cause the server to run out of memory. In the case of a FastAPI server with only 2GB of free memory, uploading a 3GB file would require loading the entire file into memory, which is not feasible.
Solution Overview
To solve this problem, we will use the stream module to read the file in chunks. This allows us to process the file in small pieces, rather than loading the entire file into memory. We will also use the asyncio library to handle the file upload asynchronously, which will improve the performance of the server.
Step 1: Install Required Libraries
Before we begin, we need to install the required libraries. We will need fastapi, starlette, stream, and asyncio. We can install these libraries using pip:
pip install fastapi starlette stream asyncio
Step 2: Create a FastAPI App
Next, we need to create a FastAPI app. We will create a new file called main.py and add the following code:
from fastapi import FastAPI, File, UploadFile
from starlette.responses import JSONResponse
from stream import Stream
import asyncio
app = FastAPI()
@app.post("/uploadfiles")
async def upload_file(file: UploadFile = File(...)):
# Process the file here
return JSONResponse("message")
Step 3: Use the stream Module to Read the File in Chunks
To read the file in chunks, we will use the stream module. We will create a new function called read_file_in_chunks that takes a file object and a chunk size as arguments. This function will read the file in chunks of the specified size and yield each chunk:
async def read_file_in_chunks(file: Stream, chunk_size: int = 1024 * 1024):
while True:
chunk = await file.read(chunk_size)
if not chunk:
break
yield chunk
Step 4: Use the asyncio Library to Handle the File Upload Asynchronously
To handle the file upload asynchronously, we will use the asyncio library. We will create a new function called upload_file_async that takes a file object and a chunk size as arguments. This function will read the file in chunks using the read_file_in_chunks function and process each chunk asynchronously:
async def upload_file_async(file: Stream, chunk_size: int = 1024 * 1024):
async for chunk in read_file_in_chunks(file, chunk_size):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
Step 5: Integrate the upload_file_async Function with the FastAPI App
Finally, we need to integrate the upload_file_async function with the FastAPI app. We will update the upload_file function to use the upload_file_async function:
@app.post("/uploadfiles")
async def upload_file(file: UploadFile = File(...)):
async with file:
await upload_file_async(file)
return JSONResponse({"message": "File uploaded successfully"})
Conclusion
In this article, we explored how to upload large files (≥3GB) to a FastAPI backend without loading the entire file into memory. We used the stream module to read the file in chunks and the asyncio library to handle the file upload asynchronously. By following these steps, you can efficiently upload large files to your FastAPI server.
Example Use Case
To test the file upload functionality, you can use a tool like curl to send a large file to the server. For example:
curl -X POST -T large_file.txt http://localhost:8000/uploadfiles
This will upload the large_file.txt file to the server without loading the entire file into memory.
Commit Message
If you were to commit this code to a version control system, the commit message would be:
feat: add support for uploading large files to FastAPI backend
Q: What is the maximum file size that can be uploaded to a FastAPI backend?
A: The maximum file size that can be uploaded to a FastAPI backend depends on the server's memory resources and the file upload settings. In the example above, we used a chunk size of 1MB to read the file in chunks, which allows us to handle files up to 3GB in size. However, the actual maximum file size that can be uploaded will depend on the server's memory resources and the file upload settings.
Q: How do I handle file uploads with multiple files?
A: To handle file uploads with multiple files, you can modify the upload_file function to accept a list of files instead of a single file. For example:
@app.post("/uploadfiles")
async def upload_files(files: List[UploadFile] = File(...)):
# Process the files here
return JSONResponse({"message": "Files uploaded successfully"})
You can then use the asyncio library to handle the file uploads asynchronously:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
await upload_file_async(file)
return JSONResponse({"message": "Files uploaded successfully"})
Q: How do I handle file uploads with large files and multiple files?
A: To handle file uploads with large files and multiple files, you can use a combination of the stream module and the asyncio library. For example:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
async for chunk in read_file_in_chunks(file, 1024 * 1024):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
return JSONResponse({"message": "Files uploaded successfully"})
Q: How do I handle file uploads with files of different sizes?
A: To handle file uploads with files of different sizes, you can use a combination of the stream module and the asyncio library. For example:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
chunk_size = 1024 * 1024 if file.size > 1024 * 1024 else 1024 * 1024
async for chunk in read_file_in_chunks(file, chunk_size):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
return JSONResponse({"message": "Files uploaded successfully"})
Q: How do I handle file uploads with files that are too large?
A: To handle file uploads with files that are too large, you can use a combination of the stream module and the asyncio library. For example:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
if file.size > 1024 * 1024 * 1024: # 1GB
return JSONResponse({"message": "File too large"})
async for chunk in read_file_in_chunks(file, 1024 * 1024):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
return JSONResponse({"message": "Files uploaded successfully"})
Q: How do I handle file uploads with files that are corrupted?
A: To handle file uploads with files that are corrupted, you can use a combination of the stream module and the asyncio library. For example:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
try:
async for chunk in read_file_in_chunks(file, 1024 * 1024):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
except Exception as e:
return JSONResponse({"message": "File corrupted"})
return JSONResponse({"message": "Files uploaded successfully"})
Q: How do I handle file uploads with files that are incomplete?
A: To handle file uploads with files that are incomplete, you can use a combination of the stream module and the asyncio library. For example:
async def upload_files_async(files: List[Stream]):
async for file in files:
async with file:
try:
async for chunk in read_file_in_chunks(file, 1024 * 1024):
# Process the chunk here
await asyncio.sleep(0) # Simulate processing time
except Exception as e:
return JSONResponse({"message": "File incomplete"})
return JSONResponse({"message": "Files uploaded successfully"})
Conclusion
In this Q&A article, we covered various scenarios related to uploading large files to a FastAPI backend. We discussed how to handle file uploads with multiple files, large files, files of different sizes, files that are too large, files that are corrupted, and files that are incomplete. By using a combination of the stream module and the asyncio library, you can efficiently handle file uploads with various scenarios.