Manish Jain
Principal Engineer at struct.org
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top School
Top expert inGo Programming Ecosystem and Distributed Systems Technology
Manish Jain is a Principal Engineer and serial founder with 11 years of experience building high-performance distributed systems and databases from Google to startups. He founded and scaled Dgraph Labs from a solo project to a $1M ARR company of 50+ engineers, and authored widely used open-source projects like Dgraph, Badger (13.5k stars), Ristretto and sroar. His engineering work emphasizes low-level performance and correctness—Badger is a production-grade Go key-value store and Dgraph is the only native graph DB to pass Jepsen transactional tests. More recently he built a compact, high-throughput blockchain indexing system at 0xFast and is exploring real-time, AI-enhanced knowledge preservation with Struct. Based in San Francisco, he blends CEO-level product instincts with hands-on systems engineering and a knack for squeezing order-of-magnitude improvements out of storage, caching and indexing layers.
11 years of coding experience
16 years of employment as a software developer
Bachelors Computer Engineering, Bachelors Computer Engineering at Nanyang Technological University Singapore
Stackoverflow
146reputation
6kreached
3answers
0questions
Github Skills (37)
algorithms10
key-value-database10
caching10
performance-monitor10
key-value-store10
tracer10
key-value10
databases10
distributed-systems10
data-structure10
go10
performance-analysis10
performance-optimization10
fileio10
jaeger10
Programming languages (21)
Github contributions (5)
dgraph-io/dgraph
Aug 2015 - Nov 2021
high-performance graph database for real-time use cases
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 155 reviews, 1246 commits in 6 years 3 months
Contributions summary:Manish focused on optimizing the posting list functionality within the high-performance graph database. They refactored the list implementation using iterators to simplify mutation layer logic. The user also improved the database's performance by introducing techniques to reduce memory allocations and handle the efficient stream-based data processing, including leveraging the new stream framework. Their work involved refactoring and optimizing memory handling in crucial database components.
graph-databasedatabasedistributedgoknowledge-graph
dgraph-io/ristretto
Feb 2019 - Aug 2021
A high performance memory-bound Go cache
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:17 reviews, 123 commits, 33 PRs in 2 years 6 months
Contributions summary:Manish primarily focused on optimizing the performance of the Go cache library. They implemented a bloom filter and a faster AES hash function to improve the cache's efficiency. Additionally, the user refactored and simplified existing code, changed naming, and modified the codebase to use AESHash for the tinyLFU admission policy, replacing a custom doorkeeper bloom filter. These changes indicate a focus on improving the core functionality and efficiency of the cache.
go-cachecacheperformancegolanggo
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