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

Other Questions RapidAPI
1

I’m building a dataset of YouTube comments using the YouTube Data API through RapidAPI. My current setup works fine for single videos, but I need to process multiple video IDs efficiently.

import requests
import json

def fetch_comments_batch(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(f"Comments for video {vid}: {result.text}")

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

Right now I have to manually change the videoId parameter for each video I want to analyze. I have hundreds of video IDs stored in a list and want to automate this process. Is there a way to pass multiple video IDs in a single API call, or do I need to loop through them individually? What’s the best approach to handle rate limits when processing large batches?

2

The YouTube Data API won’t let you grab multiple video IDs in one commentThreads request, so you’re stuck looping through them one by one. But you can make this way more efficient with proper error handling and pagination. I’ve dealt with similar datasets - wrap your requests in try-except blocks so one broken video doesn’t kill your whole batch. Store results as you go instead of holding everything in memory. For rate limiting, I use 0.1 seconds between requests. Keeps you under quota and runs pretty fast. Also remember to handle nextPageToken if you need more than 100 comments per video.

3

I’ve processed tons of large video datasets - you’ll need to loop through each video ID one by one since the API doesn’t do bulk comment requests. Here’s what works: use exponential backoff for rate limits (start small, increase the delay when you hit 429 errors). Run 3-5 concurrent threads with a shared rate limiter to speed things up without breaking quotas. Some videos have disabled comments or are private, so log which IDs fail and why. Save your progress to a database or file - trust me, you’ll want to resume when things inevitably break midway through.

4

yeah, i feel you. batching requests could help, but youtube’s limits can be tough. a lil delay between requests might save you from hitting blocks. hope this helps you build your dataset smoothly!