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- How Stripe Prevents Double Payment Using Idempotent API
↳ https://lnkd.in/erMkqwq4 How YouTube Was Able to Support 2.49 Billion Users With MySQL
↳ https://lnkd.in/efmJw4Dx
3. Pastebin Architecture
↳ https://lnkd.in/eZpfaVjc
4. Gaming Leaderboard Architecture
↳ https://lnkd.in/edfTDq5R
- How Uber Finds Nearby Drivers at 1 Million Requests per Second
↳ https://lnkd.in/eeqH9Hjh How to Scale an App to 10 Million Users on AWS
↳ https://lnkd.in/eU736g9QHow Cloudflare Supports 55 Million Requests per Second With 15 Postgres Clusters
↳ https://lnkd.in/eEQP6Apw
8. Slack Architecture
↳ https://lnkd.in/eATMDjrK
- How Uber Computes ETA at Half a Million Requests per Second
↳ https://lnkd.in/eVKV2ePC
10. Distributed Counter Architecture
↳ https://lnkd.in/eGwaA62J
- How Tinder Scaled to 1.6 Billion Swipes per Day
↳ https://lnkd.in/en65fv-W
12. URL Shortener Architecture
↳ https://lnkd.in/evFTZVQq
13. How Meta Achieves 99.99999999% Cache Consistency
↳ https://lnkd.in/e88kUZAm
14. Zoom Architecture
↳ https://lnkd.in/edidhxZw
15. How Amazon S3 Achieves 99.999999999% Durability
↳ https://lnkd.in/eutGiK35
What Undercode Say
System design is a critical skill for software engineers, and learning from real-world case studies is one of the most effective ways to master it. These case studies provide insights into how top tech companies like Stripe, YouTube, Uber, and Amazon solve complex problems at scale. By understanding their architectures, you can apply similar principles to your own projects.
For example, to implement idempotency in your APIs (as Stripe does), you can use tools like Redis or PostgreSQL to track unique request IDs. Here’s a simple Python Flask example:
from flask import Flask, request, jsonify
import redis
app = Flask(<strong>name</strong>)
redis_client = redis.Redis(host='localhost', port=6379, db=0)
@app.route('/payment', methods=['POST'])
def process_payment():
request_id = request.headers.get('X-Request-ID')
if redis_client.get(request_id):
return jsonify({"status": "already_processed"}), 200
<h1>Process payment logic here</h1>
redis_client.set(request_id, "processed", ex=3600) # Cache for 1 hour
return jsonify({"status": "success"}), 200
if <strong>name</strong> == '<strong>main</strong>':
app.run(debug=True)
For scaling databases like YouTube, consider sharding your MySQL database. Use the following command to create a sharded table:
CREATE TABLE user_data ( user_id INT PRIMARY KEY, data VARCHAR(255) ) PARTITION BY HASH(user_id) PARTITIONS 10;
To handle high request rates like Uber, leverage distributed systems and caching. Use Redis for caching:
redis-cli SET driver_location:12345 "40.7128,-74.0060" redis-cli GET driver_location:12345
For AWS scaling, use Elastic Load Balancers (ELB) and Auto Scaling Groups (ASG). Here’s a basic AWS CLI command to create an ASG:
aws autoscaling create-auto-scaling-group \ --auto-scaling-group-name my-asg \ --launch-configuration-name my-launch-config \ --min-size 2 --max-size 10 --desired-capacity 4 \ --vpc-zone-identifier "subnet-12345678"
By studying these architectures and practicing with real-world tools, you can build scalable, resilient systems. Dive deeper into these case studies and experiment with the provided commands to solidify your understanding.
For more resources, check out the official documentation for Redis, MySQL, and AWS.
**Conclusion**
System design is a blend of theory and practice. By analyzing how industry leaders tackle scalability, consistency, and performance, you can develop a robust skill set. Use the provided code snippets and commands to experiment and build your own scalable systems. Keep learning, keep building, and keep scaling!
References:
Hackers Feeds, Undercode AI
