I’m building a music database using an API but the performance is really bad. I have to loop through many categories and each category contains multiple playlists. Each playlist also has around 70 tracks to fetch.
music_data = {
"playlist_ids": [],
"status_messages": []
}
for genre_id in music_genres_df['category_ids']:
genre_playlists = fetch_playlists_by_genre(genre_id, max_results=50, start=0)
playlist_items = genre_playlists['playlist_data']['results']
response_msg = genre_playlists['status']
# Convert playlist IDs to numpy array for processing
playlist_ids = np.array([playlist['playlist_id'] for playlist in playlist_items])
music_data["playlist_ids"] = np.concatenate((music_data["playlist_ids"], playlist_ids))
music_data["status_messages"].extend([response_msg] * len(playlist_items))
The main issue is that my genres dataframe has over 50 categories. For each category I’m fetching 50 playlists. Just collecting the playlist data takes more than 30 seconds. How can I make these loops run faster?
Threading was a game-changer for me with similar API performance issues. Your bottleneck isn’t the loop - it’s waiting for each API response one by one. I used ThreadPoolExecutor to hit multiple genre requests at once and got about 70% faster speeds. Start with 5-10 threads max though - most APIs will rate limit you. Cache responses locally too. If you’re running this repeatedly during dev, save successful responses to a file. Saves tons of time. Do you actually need all 50 playlists per genre right away? I grab 10-15 first, then expand later. Better user experience. The numpy concatenation overhead is real, but threading gives you the biggest win for network-bound stuff like this.
Had the same bottleneck scraping music data last year. Your loop’s fine - the real problem is those sequential API calls. You’re hammering 50+ requests one by one, which kills performance. Try request batching if the API supports it. Most music APIs let you grab multiple playlists in one call using comma-separated IDs. Cut my collection time from 45 seconds down to 8 for similar amounts of data. Also check if you’re hitting rate limits for no reason. Different endpoints often have different limits, and playlist metadata usually has higher limits than track details. You might be able to run more concurrent requests. One more thing - that numpy concatenation in your loop creates new arrays every iteration. Just use a regular Python list and convert to numpy at the end. Made a big difference for me with large datasets.
Your issue isn’t the loop - it’s those sequential API calls. I ran into this same problem building a playlist analyzer. Use connection pooling with a requests Session object. It keeps connections alive instead of creating new ones every call. Got me about 30% faster right there. Check if the API supports partial responses too. Most music APIs let you specify which fields you want, so skip the metadata you don’t need. That numpy concatenation is killing you - recreating arrays every iteration is brutal. I switched to collections.deque for appending, then convert to numpy once at the end. Add checkpointing too. Save progress every 10-15 genres so you don’t lose everything if it crashes. These changes dropped my collection time from 30+ seconds to under 10.
asyncio with aiohttp is ur best bet. I switched from requests to async and cut my API collection time by 80%. Fire off all those genre calls at once with asyncio.gather() instead of waiting for each one. Just don’t go crazy with concurrency or you’ll hit rate limits fast.