Parsing Large JSON Responses in Python Using asyncio and aiohttp Results in MemoryError
I'm relatively new to this, so bear with me... I'm stuck on something that should probably be simple. I tried several approaches but none seem to work. I'm working on a Python application that fetches a large JSON dataset from an API using `aiohttp` for asynchronous requests. However, when the response size exceeds a certain threshold (around 10MB), I encounter a `MemoryError`. I suspect it has to do with how I'm processing the JSON data. Here’s the relevant part of my code: ```python import aiohttp import asyncio async def fetch_json(url): async with aiohttp.ClientSession() as session: async with session.get(url) as response: return await response.json() async def main(): url = 'https://example.com/api/large-data' data = await fetch_json(url) # Additional processing here if __name__ == '__main__': asyncio.run(main()) ``` I’ve tried switching to `response.text()` and then parsing it with `json.loads()`, but I face the same scenario. The scenario seems to occur during the JSON decoding phase. My system has 8GB of RAM, and I noticed that the memory usage spikes just before the `MemoryError` is thrown. I also tried increasing the swap space on my machine, but that didn’t help either. Is there a more memory-efficient way to handle large JSON responses in this setup? I’m currently using Python 3.9.1 and aiohttp 3.7.4. Any suggestions would be greatly appreciated! For context: I'm using Python on Debian. Any help would be greatly appreciated! The stack includes Python and several other technologies. Hoping someone can shed some light on this. For reference, this is a production web app. Any feedback is welcome!