How to fetch YouTube comments for multiple video IDs using YouTube Data API v3 through RapidAPI

Other Questions RapidAPI
1

I’m building a comment analysis tool and need to extract comments from multiple YouTube videos at once. Right now, my code only handles one video at a time, but I have hundreds of video IDs to process.

import requests
import json

def fetch_comments_bulk(video_list):
    api_endpoint = "https://youtube-v31.p.rapidapi.com/commentThreads"
    
    request_headers = {
        'x-rapidapi-key': "your-api-key-here",
        'x-rapidapi-host': "youtube-v31.p.rapidapi.com"
    }
    
    for vid in video_list:
        params = {
            "maxResults": "100",
            "videoId": vid,
            "part": "snippet"
        }
        
        result = requests.get(api_endpoint, headers=request_headers, params=params)
        print(result.json())

video_ids = ["video1", "video2", "video3"]
fetch_comments_bulk(video_ids)

The current approach requires me to manually specify each video ID separately. Is there a way to pass an array of video IDs to the API call so it can process them all in one request? Or do I need to loop through each ID individually? What’s the most efficient method to handle bulk comment extraction without hitting rate limits?

2

Your approach is fundamentally correct since the API requires individual requests per video ID. The main optimization you should focus on is implementing proper error handling and retry logic rather than trying to batch the requests themselves.

I’ve dealt with similar bulk processing scenarios and found that adding request pooling with threading can significantly speed things up without violating rate limits. You can process 3-5 videos concurrently using ThreadPoolExecutor while maintaining the per-request delay. Just make sure to implement proper exception handling for each thread.

Another crucial improvement is adding data validation before making API calls. Some video IDs might be invalid or have comments disabled, which will waste your quota allowance. I recommend checking video metadata first using the videos endpoint to filter out problematic IDs.

For quota management, consider implementing a simple counter that tracks your daily usage. The commentThreads endpoint consumes quota units quickly, so monitoring usage helps prevent unexpected shutdowns mid-process. Store your progress in a database or file so you can resume from where you left off if you hit limits.

3

Unfortunately, the YouTube Data API v3 doesn’t support batch requests for comment retrieval - you’ll need to process each video ID individually as you’re already doing. However, your current implementation has some critical issues that will cause problems at scale.

First, you’re not handling pagination properly. Comments come in pages, and you’ll miss most of them if you only fetch the first 100. You need to implement nextPageToken handling to get complete comment sets.

Second, rate limiting is going to be your biggest challenge. I learned this the hard way when processing large datasets. Implement exponential backoff and respect the quotaUnits - each commentThreads request costs 1 unit, but you only get 10,000 units per day by default. Add sleep intervals between requests (I use 0.5-1 second delays) and proper error handling for 403/429 responses.

Also consider storing results incrementally rather than printing directly, since you’ll likely hit interruptions during long batch processes. I usually save to JSON files after every 50-100 videos to avoid losing progress.

4

honestly just add a simple time.sleep(1) between each request and you’ll be fine. i process thousands of vids this way and never had issues with rate limiting. the real trick is catching when videos have comments disabled - you’ll get a 403 error that wastes quota if you dont handle it properly.