alibaba/GraphScope
Mar 2026 – Mar 2026
1 Month active
Languages Used
PythonYAML
Technical Skills
DockerKubernetesdata generationgraph analyticsperformance benchmarking
Lingkai981
PROFILE
Lingkai981
Developed the Graph Analytics Benchmark Suite for the alibaba/GraphScope repository, focusing on enabling reproducible and cross-platform benchmarking for graph analytics research. The work involved building a data generator using Python and YAML, integrating an LLM-based usability evaluation framework, and creating performance benchmarking scripts compatible with multiple graph platforms. All components were packaged for seamless deployment with Docker and Kubernetes, supporting artifact evaluation for SIGMOD 2025. This suite addressed the need for standardized, reproducible benchmarks, allowing stakeholders to compare performance across platforms such as GraphX, PowerGraph, and Pregel+. The focus remained on robust feature delivery and reproducibility throughout the development cycle.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,224
Activity Months1
Your Network
42 peopleSame Organization
@sjtu.edu.cn28
ZhongwangZhangMember
Di LinMember
Linzhang LiMember
Siyuan FengMember
Zhuoqiang CaiMember
TooFunMember
Siyuan FengMember
Xihuai WangMember
StardustMember
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered the Graph Analytics Benchmark Suite for GraphScope to enable reproducible, cross-platform graph analytics benchmarking. The release includes a data generator (FFT-DG), an LLM-based usability evaluation framework, and performance evaluation scripts across multiple graph platforms, packaged for Kubernetes + Docker. This work supports the SIGMOD 2025 artifacts and enhances benchmarking capabilities, accelerating research-to-production cycles and enabling clearer performance comparisons for stakeholders.
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered the Graph Analytics Benchmark Suite for GraphScope to enable reproducible, cross-platform graph analytics benchmarking. The release includes a data generator (FFT-DG), an LLM-based usability evaluation framework, and performance evaluation scripts across multiple graph platforms, packaged for Kubernetes + Docker. This work supports the SIGMOD 2025 artifacts and enhances benchmarking capabilities, accelerating research-to-production cycles and enabling clearer performance comparisons for stakeholders.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
DockerKubernetesdata generationgraph analyticsperformance benchmarking
Repositories Contributed To
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