ClickHouse/ClickBench
Nov 2025 – Nov 2025
1 Month active
Languages Used
BashJSONPythonSQLbashsql
Technical Skills
Python scriptingSQLbash scriptingbenchmarkingdata analysisdata engineering
Conor McCarter
PROFILE
Conor Mccarter
Conor McCarter developed and stabilized the Databricks SQL Warehouse Benchmark Framework within the ClickHouse/ClickBench repository, focusing on reproducible performance benchmarking for Databricks SQL warehouses. He implemented Python and SQL scripts to automate benchmark setup, expanded coverage to support multiple warehouse configurations, and ensured reliable results by disabling query result caching. Conor introduced environment checks, such as S3 parquet path validation and uv tool verification, to streamline troubleshooting and maintain consistency across runs. His work emphasized thorough documentation and addressed pull request feedback, resulting in a robust framework that enables data engineers to evaluate and compare warehouse performance with confidence.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
2,027
Activity Months1
Your Network
69 people
Work History
November 2025
3 Commits • 1 Features
Nov 1, 2025Monthly work summary for 2025-11 focusing on key accomplishments in ClickBench benchmarking work. Delivered and stabilized the Databricks SQL Warehouse Benchmark Framework and Setup for ClickBench, expanding coverage, improving reproducibility, and documenting the process. Key activities included enabling configuration for multiple warehouses, creating benchmark scripts and queries, disabling query result caching, implementing environment checks (S3 parquet path, uv tool check), and addressing PR feedback to improve reliability.
3 Commits • 1 Features
Nov 1, 2025Monthly work summary for 2025-11 focusing on key accomplishments in ClickBench benchmarking work. Delivered and stabilized the Databricks SQL Warehouse Benchmark Framework and Setup for ClickBench, expanding coverage, improving reproducibility, and documenting the process. Key activities included enabling configuration for multiple warehouses, creating benchmark scripts and queries, disabling query result caching, implementing environment checks (S3 parquet path, uv tool check), and addressing PR feedback to improve reliability.
November 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability80.0%
Architecture86.6%
Performance73.4%
AI Usage26.6%
Skills & Technologies
Programming Languages
BashJSONPythonSQLbashsql
Technical Skills
Python scriptingSQLbash scriptingbenchmarkingdata analysisdata engineeringperformance benchmarking
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