milvus-io/milvus
Oct 2024 – Oct 2024
1 Month active
Languages Used
C++GoPython
Technical Skills
C++Data SerializationDatabase InternalsGeospatial Data HandlingGoProtocol Design
Hao Tan
PROFILE
Hao Tan
Developed geospatial data type and GIS function support for the Milvus server, enabling users to create collections with geospatial fields, ingest spatial data, and perform GIS-based queries. This work, delivered in the milvus-io/milvus repository, involved cross-language integration using C++ and Go, leveraging libraries such as GDAL and go-geom to handle geospatial storage, loading, and querying. Updates to the protocol and data pipeline ensured seamless support for geospatial workflows, including spatial filtering and validation of end-to-end data operations. The feature expanded Milvus’s capabilities for scalable location-based analytics, addressing production use cases in geospatial data management and querying.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,052
Activity Months1
Your Network
114 peopleShared Repositories
114
Work History
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered Geospatial Data Type and GIS Function Support for Milvus server, enabling collections with geospatial fields, data ingestion, and spatial queries via GIS functions. Implemented across C++ and Go layers, with integration of GDAL and go-geom, and protocol/data pipeline updates to support geospatial workflows. No major bugs reported; focused on expanding core capabilities for location-based analytics and positioning Milvus to handle geospatial workloads in production. This work enhances business value by enabling scalable geospatial querying and broader use-case coverage for customers.
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered Geospatial Data Type and GIS Function Support for Milvus server, enabling collections with geospatial fields, data ingestion, and spatial queries via GIS functions. Implemented across C++ and Go layers, with integration of GDAL and go-geom, and protocol/data pipeline updates to support geospatial workflows. No major bugs reported; focused on expanding core capabilities for location-based analytics and positioning Milvus to handle geospatial workloads in production. This work enhances business value by enabling scalable geospatial querying and broader use-case coverage for customers.
October 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++GoPython
Technical Skills
C++Data SerializationDatabase InternalsGeospatial Data HandlingGoProtocol DesignPython
Repositories Contributed To
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