I’m working on collecting music data from an API but running into major speed problems. My current approach uses nested loops to fetch playlist information from different music categories. The outer loop goes through category IDs and for each one it makes API calls to get playlist data. Then I store the playlist IDs and response messages in arrays.
music_data = {
"playlist_ids": [],
"api_responses": []
}
for genre_id in genre_dataframe['category_ids']:
category_playlists = fetch_playlists_by_genre(genre_id, count=50, start=0)
playlist_items = category_playlists['data']['results']
response_msg = category_playlists['status']
# Convert IDs to numpy array and combine with existing data
extracted_ids = np.array([playlist['id'] for playlist in playlist_items])
music_data["playlist_ids"] = np.concatenate((music_data["playlist_ids"], extracted_ids))
music_data["api_responses"].extend([response_msg] * len(playlist_items))
The issue is my genre dataframe has over 50 categories and each category returns 50 playlists. Each playlist contains around 70 tracks. Just collecting the basic playlist info takes over 30 seconds. Any suggestions for speeding this up?
API rate limiting is your main problem. Most music APIs throttle requests to prevent abuse, so hitting them one by one for 50+ genres creates delays between calls. I hit the same issue scraping Spotify data. Try batching requests if the API supports it - some endpoints let you grab multiple genres in one call. Also use connection pooling with requests.Session() instead of creating new connections every time. This alone cut my API response times by 40%. Another option: implement exponential backoff when you hit rate limits instead of just waiting. The API might return data faster than you think, but your client could be the bottleneck in how it handles responses.
try using asyncio or multithreading to call APIs at the same time instead of waiting for one to finish. also, ditch np.concatenate in the loop, it’s really slow. collect everything in regular lists then convert them to np.array later. should help a lot!
check if your api handles pagination params correctly. the start=0 and count=50 might not be optimized server-side. try smaller batches like count=20 to see if it speeds things up. also check your network - 30 seconds screams timeout, not just slow processing.
Database design is huge here. I hit the same performance wall building a music rec system - turns out the real bottleneck wasn’t API calls or loops, but how I stored the data.
Don’t accumulate everything in memory with arrays. Stream results straight to a database or file system instead. You’ll kill the memory overhead and process results as they come in. SQLite’s perfect for this - just insert playlist data right after each API call finishes.
Also, do you actually need all that data upfront? I thought I did, but I could grab basic metadata first and lazy-load track details only when users needed them. Cut my collection time from minutes to seconds and made everything way more responsive.
Your concatenation operations are killing performance. Every np.concatenate call creates a brand new array and copies all existing data plus the new stuff. With 50+ categories, this gets exponentially slower.
I hit the same wall building a movie database scraper. Fixed it by preallocating arrays when I knew the final size, or just using regular Python lists for the entire collection process and converting to numpy at the end. No more memory reallocation overhead.
Also throw in some basic caching if you’re hitting the same genre IDs multiple times. Even a simple dictionary cache cuts redundant API calls. Your bottleneck might not just be nested loops - could be the data structure operations inside them too.
Been dealing with this for years at work. The real game changer isn’t fixing your code - it’s automating the whole data pipeline.
I built something similar for our analytics team pulling music data from multiple APIs daily. Instead of wrestling with async code and rate limits, I set up an automated workflow that handles everything.
The workflow runs on schedule and manages concurrent API calls automatically. Handles rate limiting, retries failures, and caches responses to avoid duplicates. No more nested loops killing your processing time.
You can set up parallel branches for each genre, so all 50 categories process simultaneously instead of one by one. The system automatically aggregates results into your final structure.
Best part - when APIs change rate limits or go down, the automation adapts and keeps running. No more babysitting scripts or debugging performance issues.
This cut our music data collection from 45 minutes to under 3 minutes. Plus it runs automatically so fresh data’s always ready.
Check out Latenode for this setup. Way more reliable than trying to optimize nested loops manually: https://latenode.com