Arpit Bhayani

Hey, I am Arpit

CS Engineer, Educator, and a Polymath

I am a CS engineer passionate about building systems that scale and currently working with Google as a Staff Software Engineer with the Dataproc team and making Apache Spark faster.

Throughout my career, I have worked across a variety of domains and have built systems, services, and platforms that scale to billions; and have worked at companies like Unacademy, Amazon, Practo, and D. E. Shaw.

I hold a Master's in CS from IIIT-Hyderabad, specializing in Databases, Information Retrieval and Web Mining. I teach a cohort-based course on System Design and have taught 1000+ engineers. On the side, I am


Arpit Bhayani

Be a better engineer

A set of courses designed to make you a better engineer and excel at your career; no-fluff, pure engineering.


Paid Courses

System Design Masterclass

A masterclass that helps you become great at designing scalable, fault-tolerant, and highly available systems.

1000+ learners

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Redis Internals

Learn internals of Redis by re-implementing some of the core features in Golang.

46+ learners

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Free Courses

Designing Microservices

A free playlist to help you understand Microservices and their high-level patterns in depth.

106+ learners

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GitHub Outage Dissections

A free playlist to help you learn core engineering from outages that happened at GitHub.

251+ learners

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Hash Table Internals

A free playlist to help you understand the internal workings and construction of Hash Tables.

427+ learners

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BitTorrent Internals

A free playlist to help you understand the algorithms and strategies that power P2P networks and BitTorrent.

192+ learners

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Topics I talk about

Being a passionate engineer, I love to talk about a wide range of topics, but these are my personal favourites.


Recent talks and interviews






Arpit Bhayani speaking at Sandbox by Cohesive

A recent essay I wrote



Genetic Algorithm to solve the Knapsack Problem


The 0/1 Knapsack Problem has a pseudo-polynomial run-time complexity. In this essay, we look at an approximation algorithm inspired by genetics that finds a high-quality solution to it in polynomial time.

441 reads Published on 2022-03-07