Daniel Larkin-york
Staff Engineer at MongoDB
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Larkin-york is a Staff Engineer in Chicago with 15 years of experience building and optimizing core database systems, currently leading storage and workload management efforts at MongoDB. He combines a strong algorithmic and academic background (PhD and MA from Princeton) with hands-on C/C++ systems work, specializing in time-series storage, performance tuning, and concurrency for high-cardinality workloads. His open-source contributions include bug fixes and storage-engine improvements to cornerstone projects like MongoDB and ArangoDB, addressing issues from time-series bucket serialization to RocksDB-backed indexing and replication. Known for cross-team technical leadership and mentorship, he balances deep low-level debugging with pragmatic product-facing engineering, and has a track record of converting research-grade algorithms into production storage features.
15 years of coding experience
10 years of employment as a software developer
University of Illinois Urbana-ChampaignMaster of Arts (M.A.), Computer Science, Master of Arts (M.A.), Computer Science at Princeton University
English
Github Skills (25)
timeseries-database10
transaction10
c-language10
rocksdb10
databases10
query-optimization10
data-structure10
mongodb-database10
transactionscope10
optimisation10
database-design10
data-structures10
transaction-manager10
cprogramming-language10
mongodb10
Programming languages (7)
Github contributions (5)
arangodb/arangodb
Jan 2017 - Jan 2021
🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:103 reviews, 180 commits, 279 PRs in 4 years
Contributions summary:Daniel's contributions primarily involved modifications to the ArangoDB backend, specifically within the RocksDB engine. These changes focused on improving performance and data management related to indexes, including modifications to the edge index, improvements to transactional cache and the implementation of a document cache. They also worked on improving the replication process.
data-modelsdatabase-modelextensionsjavascriptquery-language
mongodb/mongo
Jan 2021 - Jan 2023
The MongoDB Database
Role in this project:Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:199 commits, 1 push in 2 years
Contributions summary:Daniel's contributions primarily involved fixing bugs and implementing improvements to the MongoDB database's internal storage components and related testing infrastructure. They focused on addressing issues in time series storage and flow control, including preventing division-by-zero errors and handling potential data inconsistencies within the bucket catalog. Additionally, the user made changes to handle createIndex commands and improve database locking within the time series collection. Furthermore, the user worked on refactoring and optimizing code related to time-series bucket data serialization.
nosqlc-plus-plusmongodb-databasedatabasemongodb
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