NVIDIA cuDF for Apache Spark plugin - accelerate Apache Spark with GPUs
C-Rank Grade: A (Welcoming) - 66/100
External PR Merge Rate: 70%
Response Time: -
First Timer Success: 76%
NVIDIA/cudf-spark has a recorded first-timer success rate of 76.5%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in NVIDIA/cudf-spark 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 66.3 places NVIDIA/cudf-spark in the A tier.
The external contributor pull request merge rate for NVIDIA/cudf-spark is 69.7%, based on public PR activity from non-core contributors.
NVIDIA/cudf-spark does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Strict review standards in NVIDIA/cudf-spark. Ensure PR scope matches issue requirements closely before requesting review.
Average Response Latency
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External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
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Show contributors that your repository actively reviews and merges external pull requests.
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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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Maintainers are most responsive around 06:00 UTC (06:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 03:00 to 07:00 UTC (03:00 - 07:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (87% approval). Large PRs (>500 lines) take ~115 hours (31% approval). Keep your diff compact for rapid review.
This comes from the investigation of SPARK-56444 (apache/spark@74530b911b5) in Apache Spark 4.2. It was noticed that the equals() implementation and hashCode() were not congruous in BatchScanExec. Specifically, BatchScanExec did not consider the keyGroupedPartitioning in its hashCode() calculation, but did take it into account for equals(). I see now that we have a similar problem in GpuBatchScanExec: override def equals(other: Any): Boolean = other match { case other: GpuBatchScanExec => this.batch != null && this.batch == other.batch && this.runtimeFilters == other.runtimeFilters && this.spjParams == other.spjParams case _ => false } override def hashCode(): Int = Objects.hashCode(batch, runtimeFilters) We should consider including the spjParams in hashCode(). Applies to all shims.
Describe the bug Three cache tests carry @allow_non_gpu_conditional(is_spark_350_or_351(), "InMemoryTableScanExec"), but that allowance never does anything. The test-mode plan checker has a dedicated branch for InMemoryTableScanExec that accepts a CPU cache scan without ever consulting the allowed-non-GPU list, so the marker grants an allowance that is never read. Impact None functional. On Spark 3.5.0/3.5.1 the cache scan is expected to run on CPU (InMemoryTableScan is disabled there — see PR #13434), and the checker already tolerates that regardless of the marker. Removing the markers changes no test outcome on any Spark version. This is dead test config, not a correctness bug. What to do Delete the three allow_non_gpu_conditional(...) lines in cache_test.py (test_aqe_cache_version_specific_behavior, test_persist_with_groupby_join_version_specific, test_cached_groupby_sum_version_specific). Nothing else is required. Environment details N/A (test harness; all environments). Addition
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When evaluating whether to contribute to NVIDIA/cudf-spark, 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 NVIDIA/cudf-spark acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 69.7% were successfully merged into the primary branch.
NVIDIA/cudf-spark has a recorded first-timer success rate of 76.5%. Repositories ranked Welcoming 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 66.3 places NVIDIA/cudf-spark in the Welcoming tier.
The external pull request merge rate is 69.7%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
NVIDIA/cudf-spark 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 NVIDIA/cudf-spark 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.