How to speed up data collection loops when building music streaming database

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1

I’m working on building a music database using a streaming API but the performance is really bad. I have to loop through many genres, and each one takes a long time to process.

music_data = {
    "playlist_ids": [],
    "status_logs": []
}

for genre_id in music_genres_df['genre_ids']:
    genre_playlists = fetch_genre_playlists(genre_id, max_results=50, start=0)
    playlist_items = genre_playlists['data']['results']
    status_msg = genre_playlists['status']
    
    # Convert to numpy array and merge with existing data
    new_ids = np.array([playlist['id'] for playlist in playlist_items])
    music_data["playlist_ids"] = np.concatenate((music_data["playlist_ids"], new_ids))
    music_data["status_logs"].extend([status_msg] * len(playlist_items))

The issue is my genre list has over 50 categories and I’m processing 50 playlists for each category. Each playlist contains around 70 tracks. Just gathering the playlist data takes over 30 seconds. What are some effective strategies to make these loops more efficient?

2

The bottleneck’s definitely those sequential API calls, not the data processing. I’ve scraped tons of music datasets and async requests always make the biggest difference. Switch to asyncio and aiohttp - you’ll cut collection time dramatically since you won’t wait for each call to finish before starting the next. Process multiple genres at once instead of one by one. Those repeated np.concatenate operations are killing you too. Each one creates a new array, which gets expensive fast with large datasets. Pre-allocate your arrays if you know roughly how big they’ll be, or just use Python lists during collection and convert to numpy once at the end. Also check if your API does batch requests - most streaming APIs let you grab multiple genres or playlists in one call, which cuts down HTTP requests big time.