Amatya Avadhanula
Software Engineer at The Apache Software Foundation
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolAmatya Avadhanula is a software engineer with 4 years of hands-on experience building scalable back-end systems, currently at Google after advancing through roles at AppDynamics and Imply. He is an active Apache Druid committer and PMC member who has contributed performance and resiliency improvements to the widely used real-time analytics database, including smarter segment pruning and safer Kinesis ingestion handling. Comfortable across distributed systems and stream processing, he focuses on pragmatic fixes that improve query efficiency and operational robustness. His background includes practical performance engineering from internships to production services, and he brings a pattern of turning complex distributed concerns into maintainable code. Based in Bengaluru and educated at IIIT Hyderabad, he blends strong engineering rigor with open-source stewardship. An understated strength is his knack for anticipating operational edge cases—evident in contributions that prevent ingestion limits and improve task assignment after resharding.
4 years of coding experience
5 years of employment as a software developer
Bachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at International Institute of Information Technology Hyderabad (IIITH)
Github Skills (13)
algorithm10
data-structures10
javas10
algorithms10
amazon-kinesis10
datastructures-algorithms10
apache-druid10
back-end-development10
kin10
data-structure10
java10
data-engineering10
performance-optimization9
Programming languages (2)
Github contributions (5)
apache/druid
Dec 2021 - Dec 2022
Apache Druid: a high performance real-time analytics database.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 928 reviews, 38 commits in 1 year
Contributions summary:Amatya made several contributions to improve segment pruning for multi-dimensional partitioning in Apache Druid. These changes involved modifying dimension range shard specifications to enhance pruning efficiency for complex queries. They also worked on mitigating a potential limit-exceeded exception in Kinesis stream ingestion by using the listShards API, adding code to maintain caches of closed shards, and improving task assignment after resharding. The user also added new task context parameters to better handle thread pool initialization.
real-timebig-datadruiddatabasehadoop
AmatyaAvadhanula/druid
Dec 2021 - Oct 2024
Apache Druid: a high performance real-time analytics database.
Contributions:1 PR, 552 pushes, 154 branches in 2 years 11 months
real-time-analyticsanalyticssqlapachebig-data
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