Piyush Garg - Build Your Own Image Kit and Cloudinary Clone
The discussion centers on creating a clone of services like ImageKit or Cloudinary, which optimize images on-the-fly to improve web performance. The video outlines the architecture needed to build such a service from scratch, emphasizing the importance of image optimization for web pages to maintain high performance and ranking. The speaker explains the use of AWS services like S3 for storing original images and CloudFront for caching optimized images. A Lambda function acts as a transformer to apply transformations based on user queries, such as resizing or changing image quality, and stores the transformed images in a separate S3 bucket. This setup reduces the need for repeated transformations and optimizes delivery speed. The video also discusses potential enhancements using AI for image transformations and suggests using EC2 instances for faster processing if needed. The entire system is designed to be cost-effective, leveraging serverless architecture to minimize expenses.
Key Points:
- Build a custom image optimization service using AWS S3, CloudFront, and Lambda.
- Use Lambda functions to apply image transformations based on user queries.
- Store transformed images in a separate S3 bucket to avoid repeated processing.
- Consider using EC2 instances for faster image processing if needed.
- Implement AI for advanced image transformations and create a UI for managing images.
Details:
1. ЁЯОм Introduction to ImageKit Clone
1.1. Introduction to Image Optimization Services
1.2. Understanding Background Processes
2. ЁЯЫа Building from Scratch and Features
2.1. Sponsorship Concerns and Project Introduction
2.2. Understanding ImageKit and Its Importance
2.3. Image Optimization for SaaS and Web Pages
2.4. Image Quality, Sizing, and Loading Efficiency
2.5. On-the-Fly Transformations and Real-Time Application
3. ЁЯУК Pricing and Optimization Challenges
3.1. Challenges with On-the-Fly Image Optimization
3.2. Potential Solutions and Recommendations
4. ЁЯФД System Design and Architecture
- The system features a magical server that processes image transformations on-the-fly, allowing users to attach filters and query parameters to images.
- For example, users can change the image dimensions to width=400 and height=400 and set quality to 50, transforming a 45MB image to a smaller, optimized version.
- Images are cached at the edge for fast delivery, similar to services like Vercel, which optimize images on-the-fly and cache them for better performance.
- The solution was inspired by building a white-labeled platform, Tichys, which uses many images that need optimization based on device size.
- Initially, the service relied on Vercel for image optimization, but due to cost, they developed their own server-based solution which runs at zero cost, aided by CDN caching.
- CDN caching, including CloudFront caching, ensures the system remains fast, minimizing the impact of cold starts.
- The video demonstrates building a custom image kit or cloud clone, which can also be converted to Amazon EC2, illustrating the solution using a sample web page built with Next.js, a popular framework.
5. ЁЯЪА Implementing Lambda and Caching Strategies
- Images are initially stored in Amazon S3, providing a cost-effective and scalable solution for original image storage.
- Users upload images to S3 using pre-signed URLs, ensuring secure and controlled access without exposing S3 credentials.
- CloudFront CDN is employed as a caching layer to optimize image delivery, reduce latency, and improve user experience by serving cached images rapidly.
- A separate S3 bucket is designated for transformed images, accommodating various image sizes or formats to meet different user needs.
- A Lambda function is utilized as a transformer to automatically process and optimize images, ensuring they are in the desired size or format before delivery.
- The Lambda function interacts seamlessly with S3 and CloudFront, enhancing the efficiency of image processing and delivery.
6. ЁЯФз Managing Transformations and Efficiency
6.1. Caching and Image Optimization
6.2. Two-Origin System for Efficiency
6.3. Storing and Retrieving Transformations
7. ЁЯУВ Code Implementation and Permissions
7.1. Serverless Infrastructure and Cost Efficiency
7.2. S3 Bucket Setup and Lambda Function
7.3. Permissions and Policies
7.4. Origin Group Setup for CloudFront
7.5. Image Transformation Using Sharp Library
8. ЁЯФН CloudFront Functions and URL Normalization
8.1. Image Transformation Process
8.2. URL Normalization with CloudFront
9. ЁЯдЦ AI Integration and Performance Enhancement
- Implement AI transformation on existing systems to enhance functionality, such as image processing tasks.
- Use AI models like Flux for applying prompts on images, enabling transformations like background removal or black and white conversion.
- Optimize transformation speed by using e2 instances with robust CPU and GPU capabilities to avoid cold start delays.
- Consider programming languages like Python or Rust to write transformers for better performance.
- Leverage pre-existing resources to build projects quickly, exemplified by a 30-minute implementation for a teacher-focused project.
- Develop a UI dashboard akin to Cloudinary's to manage usage, upload images, and apply transformations directly.
- For performance optimization, focus on efficient resource allocation and minimizing latency in image processing tasks.