System Design Interview - Rate Limiting (local and distributed)
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- Опубликовано: 10 апр 2019
- Please check out my other video courses here: www.systemdesignthinking.com
Topics mentioned in the video:
- Token bucket algorithm.
- Object-oriented design of the rate limiting solution.
- Load balancer max connections, auto-scaling.
- Message broadcasting: full mesh network topology, gossip communication, distributed cache, coordination service.
- Communication protocols: TCP, UDP.
- Embedded rate limiter vs daemon process.
- Bucket management, synchronization.
Inspired by the following interview questions:
Amazon (www.careercup.com/question?id...)
Google (www.careercup.com/question?id..., www.careercup.com/question?id...)
Uber (leetcode.com/discuss/intervie...) 
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4:00 why a rate limiter needs to be designed as a distributed system and not just for a single host
11:00 algorithm for deciding wether to accept or reject req, token bucket algorithm keeps a bucket of tokens for each unique client, and has a re-fill rate. When client makes a req we check if it has tokens remaining, if not reject
14:50 classes and interfaces oop
17:00 rate limiter across different hosts in a cluster
18:30 how token buckets communicate each just says how much they’ve used so far and the others can sum and make decision
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My pleasure.
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@MIkhail, pretty awesome work you have done. Thank you!. I wanted to propose a slight change to refill method see if you accept it. 1. private void refill ( int tokens) 2. replace line #3 in refill method with -> currentBucketSize = Math.min(currentBucketSize + tokenToAdd, maxBucketSize + tokens); Also in the allowRequest method first line will change from refill() to refill(tokens);