This repository contains the development code for sparkMeasure, an Apache Spark performance analysis and troubleshooting library. It simplifies collecting, aggregating, and exporting Spark task/stage metrics, and is designed for practical use by developers and data engineers in interactive analysis, testing, and production monitoring workflows.
C-Rank Grade: C (Moderate) - 47/100
External PR Merge Rate: 100%
Response Time: -
First Timer Success: -
LucaCanali/sparkMeasure has a recorded first-timer success rate of 0.0%. Repositories ranked C typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in LucaCanali/sparkMeasure respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 47.3 places LucaCanali/sparkMeasure in the C tier.
The external contributor pull request merge rate for LucaCanali/sparkMeasure is 100.0%, based on public PR activity from non-core contributors.
LucaCanali/sparkMeasure does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
This repository contains the development code for sparkMeasure, an Apache Spark performance analysis and troubleshooting library. It simplifies collecting, aggregating, and exporting Spark task/stage metrics, and is designed for practical use by developers and data engineers in interactive analysis, testing, and production monitoring workflows.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/LucaCanali/sparkMeasure?utm_source=github&utm_medium=badge)
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No cached good first issues currently tracked for LucaCanali/sparkMeasure.
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When evaluating whether to contribute to LucaCanali/sparkMeasure, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of LucaCanali/sparkMeasure acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 100.0% were successfully merged into the primary branch.
LucaCanali/sparkMeasure has a recorded first-timer success rate of 0.0%. Repositories ranked Moderate typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 47.3 places LucaCanali/sparkMeasure in the Moderate tier.
The external pull request merge rate is 100.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
LucaCanali/sparkMeasure does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for LucaCanali/sparkMeasure are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.