https://github.com/ranfysvalle02/ray-yt
https://github.com/ranfysvalle02/ray-yt
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/ranfysvalle02/ray-yt
- Owner: ranfysvalle02
- Created: 2025-01-08T19:42:03.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T19:56:19.000Z (10 months ago)
- Last Synced: 2025-01-22T21:19:01.529Z (9 months ago)
- Language: Python
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ray-yt
---
# Accelerating YouTube Data Processing with Ray: A Dive into Parallelization
In the era of big data, efficiently processing and analyzing large datasets is crucial. Whether you're a data scientist, a developer, or just a tech enthusiast, you've likely encountered situations where traditional sequential processing just doesn't cut it. This is where parallelization comes into play, allowing us to perform multiple operations simultaneously and significantly speed up data processing tasks.
One powerful tool that brings parallel computing to Python is **Ray**, an open-source framework that enables effortless scaling of Python and machine learning applications. In this blog post, we'll explore how we can harness the power of Ray to accelerate the processing of YouTube data, specifically focusing on checking the availability of English transcripts for a batch of videos.
## The Challenge: Processing YouTube Data at Scale
Suppose you're working on a project that involves analyzing YouTube videos related to a specific topic. You might be interested in videos that have English transcripts available so that you can perform natural language processing tasks like sentiment analysis, keyword extraction, or topic modeling.
However, YouTube has an immense amount of content. Even for a specific search query, you might get hundreds or thousands of results. Checking each video individually to see if it has an English transcript can be time-consuming, especially if done sequentially.
## Enter Ray: Parallelization Made Easy
Ray provides a simple way to parallelize and distribute computations in Python without the need to manage complex processes or threads. It abstracts away the underlying complexities, allowing you to focus on your application logic.
By leveraging Ray, we can concurrently check multiple videos for the availability of English transcripts, significantly reducing the total processing time.
## How Ray Enhances the Process
Here's how Ray revolutionizes our approach:
- **Concurrency**: Ray allows us to define remote functions that can be executed in parallel across multiple cores or machines.
- **Scalability**: As our data scales, Ray can distribute the workload seamlessly, ensuring efficient utilization of resources.
- **Simplicity**: With minimal changes to our code, we can achieve parallel execution without diving deep into multi-threading or multi-processing intricacies.
## A High-Level Overview of the Solution
Let's break down the steps involved in our parallelized YouTube data processing:
1. **Searching YouTube Videos**: We start by performing a YouTube search using the `youtubesearchpython` library to get a list of videos related to our query (e.g., "Python web scraping").
2. **Preparing Data Structures**: We collect the video IDs and relevant information like titles and URLs.
3. **Defining the Remote Function**: We use Ray's `@ray.remote` decorator to define a function that checks if a video has an English transcript.
4. **Parallel Execution**: We invoke this remote function concurrently across all videos in our list.
5. **Collecting Results**: We gather the results and filter out the videos that have English transcripts available.
6. **Output**: We display the titles and URLs of videos with English transcripts, ready for further analysis.
## The Power of Parallelization in Action
By parallelizing the transcript availability check, we can perform this task much faster compared to a sequential approach. For example, if checking each video takes approximately one second, processing 50 videos sequentially would take around 50 seconds. With Ray, these checks are performed concurrently, drastically reducing the total time to roughly the duration of the longest single check.
## Why This Matters
- **Efficiency**: Time saved is invaluable, especially when dealing with larger datasets.
- **Resource Utilization**: Parallelization ensures that all available computational resources are used effectively.
- **Scalable Solutions**: As your data grows, the same parallel approach can handle increased workloads without significant code changes.
- **Enhanced Capabilities**: Faster processing opens up possibilities for more complex analyses that were previously time-prohibitive.
## Getting Started with Ray
To use Ray in your projects, follow these simple steps:
1. **Installation**: Install Ray using pip:
```bash
pip install ray
```
2. **Initialization**: Initialize Ray in your script:
```python
import ray
ray.init()
```
3. **Define Remote Functions**: Use the `@ray.remote` decorator to define functions that should run in parallel:
```python
@ray.remote
def parallel_function(args):
# Function logic
return result
```
4. **Invoke Remote Functions**: Call these functions asynchronously:
```python
futures = [parallel_function.remote(arg) for arg in args_list]
```
5. **Gather Results**: Use `ray.get()` to retrieve the results:
```python
results = ray.get(futures)
```
6. **Shutdown**: When done, shut down Ray:
```python
ray.shutdown()
```
## Practical Example: Checking YouTube Transcripts
Here's a conceptual example of how we can apply Ray to check for English transcripts in YouTube videos:
```python
import ray
from youtubesearchpython import VideosSearch
from youtube_transcript_api import YouTubeTranscriptApi
ray.init()
@ray.remote
def has_en_transcript(video_id):
try:
YouTubeTranscriptApi.get_transcript(video_id, languages=['en'])
return True
except:
return False
def fetch_videos_with_en_transcripts(search_query):
videosSearch = VideosSearch(search_query, limit=50)
video_results = videosSearch.result()['result']
video_ids = [video['id'] for video in video_results]
futures = [has_en_transcript.remote(vid) for vid in video_ids]
results = ray.get(futures)
videos_with_transcripts = [
video for video, has_transcript in zip(video_results, results) if has_transcript
]
for video in videos_with_transcripts:
print(f"{video['title']}: {video['link']}")
search_query = "Python web scraping"
fetch_videos_with_en_transcripts(search_query)
ray.shutdown()
```
In this example:
- **Initialization**: We start Ray and prepare our search query.
- **Remote Function**: The `has_en_transcript` function checks for an English transcript and is decorated with `@ray.remote`.
- **Parallel Execution**: We create a list of futures by calling this function for each video ID.
- **Result Collection**: We retrieve the results and filter the videos accordingly.
- **Output**: We print the titles and URLs of videos with English transcripts.
## OUTPUT
```
2025-01-08 14:48:36,752 INFO worker.py:1777 -- Started a local Ray instance. View the dashboard at 127.0.0.1:8265
Found 20 YouTube video(s)
Videos with English transcripts:
- Beginners Guide To Web Scraping with Python - All You Need To Know: https://www.youtube.com/watch?v=QhD015WUMxE
- Web Scraping with Python - Beautiful Soup Crash Course: https://www.youtube.com/watch?v=XVv6mJpFOb0
- Scraping Data from a Real Website | Web Scraping in Python: https://www.youtube.com/watch?v=8dTpNajxaH0
- Scrapy Course – Python Web Scraping for Beginners: https://www.youtube.com/watch?v=mBoX_JCKZTE
- Python Tutorial: Web Scraping with BeautifulSoup and Requests: https://www.youtube.com/watch?v=ng2o98k983k
- Beautiful Soup 4 Tutorial #1 - Web Scraping With Python: https://www.youtube.com/watch?v=gRLHr664tXA
- Python AI Web Scraper Tutorial - Use AI To Scrape ANYTHING: https://www.youtube.com/watch?v=Oo8-nEuDBkk
- Web Scraping with Python - Start HERE: https://www.youtube.com/watch?v=1PCWwK0AsE0
- Ultimate Guide To Web Scraping - Node.js & Python (Puppeteer & Beautiful Soup): https://www.youtube.com/watch?v=XMu46BRPLqA
- Comprehensive Python Beautiful Soup Web Scraping Tutorial! (find/find_all, css select, scrape table): https://www.youtube.com/watch?v=GjKQ6V_ViQE
- Advanced Web Scraping Tutorial! (w/ Python Beautiful Soup Library): https://www.youtube.com/watch?v=DcI_AZqfZVc
- Python Tutorial: Web Scraping with Requests-HTML: https://www.youtube.com/watch?v=a6fIbtFB46g
- BeautifulSoup + Requests | Web Scraping in Python: https://www.youtube.com/watch?v=bargNl2WeN4
- Web Scraping in Python using Beautiful Soup | Writing a Python program to Scrape IMDB website: https://www.youtube.com/watch?v=LCVSmkyB4v8
- Web Scraping With Python 101: https://www.youtube.com/watch?v=CHUxmVVH2AQ
- Web Scraping with Python and BeautifulSoup is THIS easy!: https://www.youtube.com/watch?v=nBzrMw8hkmY
- Web Scraping 101: How To Scrape 99% of Sites: https://www.youtube.com/watch?v=WYp0dmZOHXM
- Amazon Web Scraping Using Python | Data Analyst Portfolio Project: https://www.youtube.com/watch?v=HiOtQMcI5wg
```
## Conclusion
Harnessing the power of parallelization with Ray can greatly enhance your data processing workflows, especially when dealing with large datasets like YouTube video information. By spreading tasks across multiple cores or machines, you can achieve faster results and free up valuable time for deeper analysis or additional tasks.
Whether you're processing video data, running complex computations, or training machine learning models, Ray offers a scalable and efficient solution to meet your parallel computing needs.
---
*Note: While this example focuses on checking for English transcripts in YouTube videos, the principles of parallelization with Ray can be applied to a wide range of data processing tasks. As you explore Ray, you'll discover many opportunities to optimize and accelerate your Python applications.*