Kris Mok
Software Engineer at Databricks
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKris Mok is a software engineer with 15 years of experience specializing in compilers, JVM internals, and large-scale data processing. He contributed to HotSpot and Zing JVM JIT compilers at Oracle and Azul, implementing JSR 292 support and low-level optimizations before moving to Databricks to work on Apache Spark SQL. At Databricks he focuses on backend code generation and query execution improvements for the widely used Apache Spark project, including timezone fixes, shuffle exchange enhancements, and per-query codegen staging. Based in Sunnyvale, Kris blends deep runtime expertise with practical engineering for distributed analytics systems. Beyond production work, he’s a compiler and managed-runtime enthusiast who has transitioned between JVM and LLVM-based JIT development—an indicator of both systems-level depth and adaptability. His background in software engineering from Nanjing University complements a career that spans both research-level VM work and production data-platform engineering.
15 years of coding experience
4 years of employment as a software developer
HSFZBachelor, Software Engineering, Bachelor, Software Engineering at Nanjing University
Chinese, English, Japanese
Stackoverflow
221reputation
12kreached
2answers
0questions
Github Skills (16)
javas10
spark-sql10
big-data10
code-generation10
java10
scala10
sql9
testing8
clr6
crash6
security6
linq6
data-structures4
algorithm4
algorithms4
Programming languages (5)
Github contributions (5)
apache/spark
Dec 2018 - Sep 2022
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:28 reviews, 12 commits, 44 PRs in 3 years 9 months
Contributions summary:Kris primarily contributed to the Apache Spark SQL module, focusing on improving the code generation process and related aspects. Their work involved fixing issues related to the handling of time zones in cast operations, enhancing the shuffle exchange node, and optimizing the codegen templates for literals and other expressions. They also introduced new features, such as adding a per-query codegen stage ID and improved structural integrity checks.
analyticspythondata-processingsqlapache
rednaxelafx/apache-spark
Apr 2017 - Sep 2022
Mirror of Apache Spark
Contributions:75 commits, 105 pushes, 39 branches in 5 years 5 months
connectorspark-mlapachebig-dataspark
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