Kubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes.
C-Rank Grade: A (Welcoming) - 60/100
External PR Merge Rate: 35%
Response Time: 5h
First Timer Success: 50%
kubeflow/spark-operator has a recorded first-timer success rate of 50.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in kubeflow/spark-operator respond to incoming external pull requests in approximately 5.0 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 59.7 places kubeflow/spark-operator in the A tier.
The external contributor pull request merge rate for kubeflow/spark-operator is 34.9%, based on public PR activity from non-core contributors.
kubeflow/spark-operator currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in kubeflow/spark-operator. Prompt code reviews with positive guidance for new contributors.
Discussions in kubeflow/spark-operator focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
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/kubeflow/spark-operator?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 15:00 UTC (15:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (44% approval). Large PRs (>500 lines) take ~24 hours (16% approval). Keep your diff compact for rapid review.
What feature you would like to be added? Add an AGENTS.md file at the repository root, plus a thin CLAUDE.md that references it, following the same approach already used in the kubeflow/sdk and kubeflow/trainer repositories. AGENTS.md would give AI coding agents (Claude Code, Codex, Cursor, Copilot, etc.) and human contributors a concise, machine-friendly summary of how to work in this repo. CLAUDE.md would simply import AGENTS.md (e.g. @AGENTS.md) so there is a single source of truth and the two files don't drift. Why is this needed? No response Describe the solution you would like No response Describe alternatives you have considered No response Additional context No response Love this feature? Give it a 👍 We prioritize the features with most 👍
Similar to SparkConnect add webhook validation for SparkApplication dynamic allocation.
The goal of the issue is to update docs/api-docs.md to add the SparkConnect API reference.
What feature you would like to be added? Add a Dependencies field to SparkConnectSpec, mirroring the existing Dependencies struct already present on SparkApplicationSpec. Why is this needed? The Kubeflow SDK's SparkClient (kubeflow/sdk#470) wants to offer first-class dependency management so that users can attach table-format and storage connectors (Iceberg, Delta Lake, Hudi, S3A) without hand-writing Spark config strings. Today, there is no structured way to declare dependencies in SparkConnectSpec. SparkApplication already solves exactly this problem: its Dependencies struct is consumed by the submission path, which emits real --jars, --packages, --exclude-packages, and --repositories flags. SparkConnect is the only workload type in the operator without this capability, which is an inconsistency in the API surface and forces every higher-level tool to reinvent dependency handling on top of raw sparkConf. Describe the solution you would like No response Describe alternatives you have
What feature you would like to be added? Add GPU resource support to the SparkConnect API so the Connect server and (primarily) executor pods can request GPUs and have Spark's resource scheduler configured to use them. Why is this needed? Kubeflow's primary audience runs ML workloads, and GPU-accelerated Spark could further enable that workflow. The NVIDIA RAPIDS Accelerator for Apache Spark offloads SQL/DataFrame execution to GPUs on executors, and GPU ML inference/training via pandas/Python UDFs is common for feature engineering and batch scoring. Spark Connect is a natural fit for these as interactive or long-lived sessions - exactly what the Kubeflow Spark client provides. Describe the solution you would like No response Describe alternatives you have considered No response Additional context Relates to kubeflow/sdk#466 and kubeflow/sdk#470. Love this feature? Give it a 👍 We prioritize the features with most 👍
Rate what actually matters after opening a pull request here.
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When evaluating whether to contribute to kubeflow/spark-operator, 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 kubeflow/spark-operator acknowledge new external contributions in approximately 4 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 34.9% were successfully merged into the primary branch.
kubeflow/spark-operator has a recorded first-timer success rate of 50.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~4 hours. 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 59.7 places kubeflow/spark-operator in the Welcoming tier.
The external pull request merge rate is 34.9%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, kubeflow/spark-operator currently has 3 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for kubeflow/spark-operator 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.