Feilong Liu
Research Scientist at Facebook
Columbus, Ohio Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolFeilong Liu is a research scientist at Facebook with seven years of experience specializing in query execution, data shuffling, and query optimization for distributed main-memory database systems. He holds a Ph.D. in Computer Science from The Ohio State University and has a strong background in high-performance communication (RDMA) and database internals from internships and graduate research. At Facebook he applies research-driven engineering to production-scale systems, and as a notable open-source contributor he has improved Presto’s core query planner and optimizations to boost performance and stability. Comfortable bridging academia and industry, he blends rigorous experimental methods with pragmatic engineering to simplify query plans and reduce runtime overhead.
6 years of coding experience
4 years of employment as a software developer
MS, Computer Architecture, MS, Computer Architecture at Institute of Computing Technology, Chinese Academy of ScienceBachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at Huazhong University of Science and TechnologyDoctor of Philosophy (Ph.D.), Computer Science, Database, Doctor of Philosophy (Ph.D.), Computer Science, Database at The Ohio State University
English, Chinese
Github Skills (9)
javas10
query-optimization10
presto10
sql10
back-end-development10
java10
algorithms6
data-structures6
data-structure6
Programming languages (2)
Github contributions (5)
prestodb/presto
Jul 2022 - Dec 2022
The official home of the Presto distributed SQL query engine for big data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:741 reviews, 21 commits, 254 PRs in 5 months
Contributions summary:Feilong's contributions centered around the enhancement and optimization of the Presto query engine's core functionalities. Their work involved implementing features such as adding timeout checks in the relation planner, and extending the existing framework to handle new features, such as adding group related functions. Additionally, the user focused on improving the efficiency of the query engine by simplifying the query plan via several optimizations, like removing unnecessary operations in the plan. These enhancements improved both performance and stability of the query engine.
distributed-sqlquerybigdataquery-enginesql
feilong-liu/presto
Jul 2022 - Dec 2024
The official home of the Presto distributed SQL query engine for big data
Contributions:1071 pushes, 275 branches in 2 years 5 months
hbasebigdataoracle-databasebig-datatrino
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