NVIDIA/spark-rapids-tools
Oct 2024 – Feb 2026
17 Months active
Languages Used
JavaPythonScalaYAMLBash
Technical Skills
Data EngineeringError HandlingPerformance AnalysisPython DevelopmentScalaSpark
Partho Sarthi
PROFILE
Partho Sarthi
Sarthi worked extensively on the NVIDIA/spark-rapids-tools repository, building and refining advanced tuning and profiling capabilities for GPU-accelerated Spark workloads. Over 17 months, he engineered features such as dynamic resource allocation, cluster-aware configuration, and distributed execution, focusing on robust error handling and maintainable code structure. Using Python and Scala, Sarthi implemented memory management improvements, YAML-based configuration parsing, and GPU resource estimation, addressing challenges in multi-tenant and on-prem environments. His work emphasized test-driven development, code refactoring, and performance tuning, resulting in more reliable, scalable, and user-configurable tooling that improved resource utilization and reduced operational risk for Spark users.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
37Total
Bugs
9
Commits
37
Features
20
Lines of code
13,637
Activity Months17
Your Network
1537 peopleSame Organization
@nvidia.com1525
Work History
February 2026
1 Commits
Feb 1, 2026February 2026 (NVIDIA/spark-rapids-tools): Delivered a critical AutoTuner fix that corrects resource provisioning for GPU applications by respecting user-enforced executor core settings during cluster sizing. This resolves misestimation risks and ensures allocations align with user specifications, improving predictability and GPU utilization for multi-tenant workloads. The change is fully auditable via commit 22298e525daba124bc7748a2e62827166a343a39 (Fix AutoTuner to respect user-enforced executor cores in cluster sizing), addressing issue #2036. Impact includes steadier performance, reduced over/under-provisioning, and clearer resource governance across deployments.
1 Commits
Feb 1, 2026February 2026 (NVIDIA/spark-rapids-tools): Delivered a critical AutoTuner fix that corrects resource provisioning for GPU applications by respecting user-enforced executor core settings during cluster sizing. This resolves misestimation risks and ensures allocations align with user specifications, improving predictability and GPU utilization for multi-tenant workloads. The change is fully auditable via commit 22298e525daba124bc7748a2e62827166a343a39 (Fix AutoTuner to respect user-enforced executor cores in cluster sizing), addressing issue #2036. Impact includes steadier performance, reduced over/under-provisioning, and clearer resource governance across deployments.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 — NVIDIA/spark-rapids-tools: Focused on delivering cross-output naming consistency and improving profiling coverage, with clear business value in cluster configuration and recommendations. Key work includes standardizing GPU device naming across Qualification and Profiling outputs, plus adding utilities to convert between platform-specific and generic GPU names. Resolved profiling gaps by ensuring default driver node types are included in cluster recommendations, supported by updated tests to validate behavior. These efforts reduce manual corrections, accelerate cluster setup decisions, and improve the reliability of GPU-resource recommendations. Demonstrated skills in data normalization, test-driven development, and cross-repo collaboration to enhance tooling quality.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 — NVIDIA/spark-rapids-tools: Focused on delivering cross-output naming consistency and improving profiling coverage, with clear business value in cluster configuration and recommendations. Key work includes standardizing GPU device naming across Qualification and Profiling outputs, plus adding utilities to convert between platform-specific and generic GPU names. Resolved profiling gaps by ensuring default driver node types are included in cluster recommendations, supported by updated tests to validate behavior. These efforts reduce manual corrections, accelerate cluster setup decisions, and improve the reliability of GPU-resource recommendations. Demonstrated skills in data normalization, test-driven development, and cross-repo collaboration to enhance tooling quality.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focused on delivering technical improvements that enable faster, safer future work and reduce maintenance overhead for NVIDIA/spark-rapids-tools. Key effort: maintainability refactor of ToolsPlanGraph to reduce cognitive complexity and eliminate string literal duplication.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focused on delivering technical improvements that enable faster, safer future work and reduce maintenance overhead for NVIDIA/spark-rapids-tools. Key effort: maintainability refactor of ToolsPlanGraph to reduce cognitive complexity and eliminate string literal duplication.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Monthly summary for 2025-11 focusing on the NVIDIA/spark-rapids-tools repository. Delivered On-Prem Target Cluster Configuration Support enabling CSP-style target specifications for flexible tuning across arbitrary hardware configurations. This work strengthens on-prem deployment capabilities and sets the foundation for broader CSP integration and performance optimization.
November 2025
1 Commits • 1 Features
Nov 1, 2025Monthly summary for 2025-11 focusing on the NVIDIA/spark-rapids-tools repository. Delivered On-Prem Target Cluster Configuration Support enabling CSP-style target specifications for flexible tuning across arbitrary hardware configurations. This work strengthens on-prem deployment capabilities and sets the foundation for broader CSP integration and performance optimization.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on delivering a high-impact feature to optimize GPU-intensive Spark workloads. Key feature delivered: AutoTuner: Dynamic Allocation Tuning for GPU-Intensive Runs, which recommends dynamic allocation properties based on CPU-GPU cores ratio to optimize executor allocation for GPU runs. This work culminated in commit d239f70e1c9da9643deb00bc548093ad51be1c91 and aligns executor instances to workload demands. Major bugs fixed: No major bugs fixed this month related to the feature. Minor documentation improvements and code cleanup were performed to support the AutoTuner feature. Overall impact and accomplishments: The AutoTuner enhancement improves resource utilization for GPU-bound Spark workloads by aligning executor instances to the CPU-GPU cores ratio, reducing over-provisioning and improving throughput for GPU runs. This delivers business value through faster job completion, better cluster efficiency, and easier tuning for GPU-enabled pipelines. The change also sets the foundation for further auto-tuning improvements and better integration with CPU-GPU profiling feedback loops. Technologies/skills demonstrated: GPU-accelerated Spark tuning, dynamic resource allocation strategies, CPU-GPU profiling considerations, code review and collaboration, and end-to-end delivery including documentation and PR alignment (commit referenced: d239f70e1c9da9643deb00bc548093ad51be1c91).
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Focused on delivering a high-impact feature to optimize GPU-intensive Spark workloads. Key feature delivered: AutoTuner: Dynamic Allocation Tuning for GPU-Intensive Runs, which recommends dynamic allocation properties based on CPU-GPU cores ratio to optimize executor allocation for GPU runs. This work culminated in commit d239f70e1c9da9643deb00bc548093ad51be1c91 and aligns executor instances to workload demands. Major bugs fixed: No major bugs fixed this month related to the feature. Minor documentation improvements and code cleanup were performed to support the AutoTuner feature. Overall impact and accomplishments: The AutoTuner enhancement improves resource utilization for GPU-bound Spark workloads by aligning executor instances to the CPU-GPU cores ratio, reducing over-provisioning and improving throughput for GPU runs. This delivers business value through faster job completion, better cluster efficiency, and easier tuning for GPU-enabled pipelines. The change also sets the foundation for further auto-tuning improvements and better integration with CPU-GPU profiling feedback loops. Technologies/skills demonstrated: GPU-accelerated Spark tuning, dynamic resource allocation strategies, CPU-GPU profiling considerations, code review and collaboration, and end-to-end delivery including documentation and PR alignment (commit referenced: d239f70e1c9da9643deb00bc548093ad51be1c91).
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 Concise monthly summary focusing on business value and technical achievements for NVIDIA/spark-rapids-tools. 1) Key features delivered - AutoTuner Shuffle Partition Optimization and Spill Handling: Enhances AutoTuner's recommendations for spark.sql.shuffle.partitions by aligning with AQE-related partition properties. Adds logic to increase shuffle partitions when CPU spills are detected to prevent GPU spills. Commit: 2d8f65c66602b904d524bf502acb42ded1f820bf. 2) Major bugs fixed - No major bugs fixed reported this month for the NVIDIA/spark-rapids-tools scope. 3) Overall impact and accomplishments - Improves resilience and efficiency of Spark workloads using AutoTuner with AQE by reducing GPU spill risk, optimizing partitioning decisions, and aligning CPU/GPU behavior. This supports more predictable performance and better resource utilization in GPU-accelerated data processing. 4) Technologies/skills demonstrated - Spark SQL AQE integration, AutoTuner configuration, dynamic partition tuning, GPU/CPU spill handling, code collaboration and change management (commit-level delivery). Business value: - Reduced spill-related job failures, improved throughput for shuffle-heavy workloads, and better hardware utilization in GPU-accelerated pipelines.
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 Concise monthly summary focusing on business value and technical achievements for NVIDIA/spark-rapids-tools. 1) Key features delivered - AutoTuner Shuffle Partition Optimization and Spill Handling: Enhances AutoTuner's recommendations for spark.sql.shuffle.partitions by aligning with AQE-related partition properties. Adds logic to increase shuffle partitions when CPU spills are detected to prevent GPU spills. Commit: 2d8f65c66602b904d524bf502acb42ded1f820bf. 2) Major bugs fixed - No major bugs fixed reported this month for the NVIDIA/spark-rapids-tools scope. 3) Overall impact and accomplishments - Improves resilience and efficiency of Spark workloads using AutoTuner with AQE by reducing GPU spill risk, optimizing partitioning decisions, and aligning CPU/GPU behavior. This supports more predictable performance and better resource utilization in GPU-accelerated data processing. 4) Technologies/skills demonstrated - Spark SQL AQE integration, AutoTuner configuration, dynamic partition tuning, GPU/CPU spill handling, code collaboration and change management (commit-level delivery). Business value: - Reduced spill-related job failures, improved throughput for shuffle-heavy workloads, and better hardware utilization in GPU-accelerated pipelines.
August 2025
1 Commits
Aug 1, 2025Month: 2025-08 | NVIDIA/spark-rapids-tools: Delivered a fix to the Spark GPU configuration recommendations, addressing issues with 'spark.plugins' support and GPU discovery scripts. The change refactors the configuration logic, adds tool-specific plugin recommendation logic, and replaces hardcoded script paths with guidance/comments to improve flexibility and clarity for advanced users configuring Spark with GPU acceleration. This patch enhances reliability of GPU acceleration setup, reduces misconfigurations, and accelerates onboarding for GPU-enabled Spark deployments.
1 Commits
Aug 1, 2025Month: 2025-08 | NVIDIA/spark-rapids-tools: Delivered a fix to the Spark GPU configuration recommendations, addressing issues with 'spark.plugins' support and GPU discovery scripts. The change refactors the configuration logic, adds tool-specific plugin recommendation logic, and replaces hardcoded script paths with guidance/comments to improve flexibility and clarity for advanced users configuring Spark with GPU acceleration. This patch enhances reliability of GPU acceleration setup, reduces misconfigurations, and accelerates onboarding for GPU-enabled Spark deployments.
August 2025
July 2025
4 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for NVIDIA/spark-rapids-tools focusing on automation of GPU configuration and bootstrap reliability. Delivered enhancements to AutoTuner that improve GPU resource management and Spark property handling, fixed critical data type and bootstrap configuration issues, and tightened cluster-info enrichment to ensure correct RAPIDS accelerator wiring across diverse environments. The work increases automation, reduces misconfiguration risk, and improves cluster portability and performance through targeted, instrumented changes.
July 2025
4 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for NVIDIA/spark-rapids-tools focusing on automation of GPU configuration and bootstrap reliability. Delivered enhancements to AutoTuner that improve GPU resource management and Spark property handling, fixed critical data type and bootstrap configuration issues, and tightened cluster-info enrichment to ensure correct RAPIDS accelerator wiring across diverse environments. The work increases automation, reduces misconfiguration risk, and improves cluster portability and performance through targeted, instrumented changes.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered user-configurable Spark property overrides in Profiling Tool AutoTuner with On-Prem support, including worker information and YAML-based Spark settings. This enables targeted performance profiling and tuning for enterprise Spark workloads on-prem, improving configuration fidelity, reproducibility, and time-to-insight. No major bugs fixed this month; ongoing stabilization of the profiling workflow in NVIDIA/spark-rapids-tools.
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered user-configurable Spark property overrides in Profiling Tool AutoTuner with On-Prem support, including worker information and YAML-based Spark settings. This enables targeted performance profiling and tuning for enterprise Spark workloads on-prem, improving configuration fidelity, reproducibility, and time-to-insight. No major bugs fixed this month; ongoing stabilization of the profiling workflow in NVIDIA/spark-rapids-tools.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025In May 2025, the NVIDIA/spark-rapids-tools team delivered a focused enhancement to the AutoTuner's memory model, improving memory calculation and resource estimation across CPU/GPU, off-heap, and container reservations. The update tightened checks against available container memory, refined executor heap/overhead estimation, and introduced clearer handling for off-heap and PySpark memory with improved warnings when capacity is insufficient. This work reduces the risk of over-allocation, improves GPU utilization, and enhances cluster stability in multi-tenant environments.
May 2025
2 Commits • 1 Features
May 1, 2025In May 2025, the NVIDIA/spark-rapids-tools team delivered a focused enhancement to the AutoTuner's memory model, improving memory calculation and resource estimation across CPU/GPU, off-heap, and container reservations. The update tightened checks against available container memory, refined executor heap/overhead estimation, and introduced clearer handling for off-heap and PySpark memory with improved warnings when capacity is insufficient. This work reduces the risk of over-allocation, improves GPU utilization, and enhances cluster stability in multi-tenant environments.
April 2025
2 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04 focusing on NVIDIA/spark-rapids-tools: code quality improvements and cluster-aware profiling enhancements.
2 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04 focusing on NVIDIA/spark-rapids-tools: code quality improvements and cluster-aware profiling enhancements.
April 2025
March 2025
3 Commits • 2 Features
Mar 1, 2025For 2025-03, NVIDIA/spark-rapids-tools delivered practical AutoTuner enhancements to improve stability and performance in GPU-accelerated Spark pipelines, with a focus on OOM resilience and test reliability. Key outcomes include GPU OOM-aware partition sizing and shuffle partition recommendations to reduce OOM failures during table scans and YARN shuffle stages; and unit test reliability improvements for dynamic plugin URL handling, including a helper for suggesting newer plugin versions. These changes collectively reduce failed runs, improve throughput, and provide clearer guidance to users on plugin versions and partition tuning. Technologies involved include Spark SQL tuning, GPU OOM detection, YARN-based orchestration, and dynamic plugin URL testing.
March 2025
3 Commits • 2 Features
Mar 1, 2025For 2025-03, NVIDIA/spark-rapids-tools delivered practical AutoTuner enhancements to improve stability and performance in GPU-accelerated Spark pipelines, with a focus on OOM resilience and test reliability. Key outcomes include GPU OOM-aware partition sizing and shuffle partition recommendations to reduce OOM failures during table scans and YARN shuffle stages; and unit test reliability improvements for dynamic plugin URL handling, including a helper for suggesting newer plugin versions. These changes collectively reduce failed runs, improve throughput, and provide clearer guidance to users on plugin versions and partition tuning. Technologies involved include Spark SQL tuning, GPU OOM detection, YARN-based orchestration, and dynamic plugin URL testing.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on enabling distributed execution for RAPIDS Qualification tool within NVIDIA/spark-rapids-tools, delivering scalable Spark cluster runs and enhanced output processing. Key deliverable includes integration of a distributed submission workflow and the consolidation of the Distributed Qualification Tools CLI, enabling easier distributed execution across clusters.
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on enabling distributed execution for RAPIDS Qualification tool within NVIDIA/spark-rapids-tools, delivering scalable Spark cluster runs and enhanced output processing. Key deliverable includes integration of a distributed submission workflow and the consolidation of the Distributed Qualification Tools CLI, enabling easier distributed execution across clusters.
February 2025
January 2025
6 Commits • 3 Features
Jan 1, 20252025-01 monthly summary for NVIDIA/spark-rapids-tools: Key features delivered include Spark Version Compatibility Update, AutoTuner Enhancements, and GPU Cluster Configuration Strategy. Major bug fix: HDFS test reliability improvement. Overall impact: enabled support for Spark 3.2.0+ and 3.5.1, clarified AutoTuner guidance, standardized GPU configurations, and improved test stability. Technologies demonstrated: version validation, runtime mapping, memory/pinned-pool tuning, and CI/test reliability improvements.
January 2025
6 Commits • 3 Features
Jan 1, 20252025-01 monthly summary for NVIDIA/spark-rapids-tools: Key features delivered include Spark Version Compatibility Update, AutoTuner Enhancements, and GPU Cluster Configuration Strategy. Major bug fix: HDFS test reliability improvement. Overall impact: enabled support for Spark 3.2.0+ and 3.5.1, clarified AutoTuner guidance, standardized GPU configurations, and improved test stability. Technologies demonstrated: version validation, runtime mapping, memory/pinned-pool tuning, and CI/test reliability improvements.
December 2024
5 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/spark-rapids-tools: Focused on strengthening correctness and flexibility of profiling/qualification workflows, improving runtime safety, and enhancing tuning guidance to drive business value. Key features delivered include enforcing the 'platform' argument as mandatory for qualification and profiling CLI tools, with tests updated to reflect the requirement; introducing platform-specific runtime validation to skip processing when the detected Spark runtime is not supported by the chosen platform; modularizing the AutoTuner to separately manage configurations for Profiling and Qualification and adding a 1GB batch size override to enhance tuning flexibility; and extending AutoTuner with a Spark SQL shuffle partitions configuration to provide guidance even when full logic calculation is disabled, accompanied by test updates. Major bugs fixed include preventing invalid configurations by skipping processing for unsupported platform-runtime combos. Overall impact: increased reliability and predictability of profiling/qualification runs, reduced risk of misconfigurations, and faster, more accurate tuning recommendations, leading to better resource utilization and shorter time-to-value for users. Technologies/skills demonstrated: Python-based CLI validation and configuration management, test-driven development, modular refactoring, and evidence of end-to-end improvement in tuning workflows.
5 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/spark-rapids-tools: Focused on strengthening correctness and flexibility of profiling/qualification workflows, improving runtime safety, and enhancing tuning guidance to drive business value. Key features delivered include enforcing the 'platform' argument as mandatory for qualification and profiling CLI tools, with tests updated to reflect the requirement; introducing platform-specific runtime validation to skip processing when the detected Spark runtime is not supported by the chosen platform; modularizing the AutoTuner to separately manage configurations for Profiling and Qualification and adding a 1GB batch size override to enhance tuning flexibility; and extending AutoTuner with a Spark SQL shuffle partitions configuration to provide guidance even when full logic calculation is disabled, accompanied by test updates. Major bugs fixed include preventing invalid configurations by skipping processing for unsupported platform-runtime combos. Overall impact: increased reliability and predictability of profiling/qualification runs, reduced risk of misconfigurations, and faster, more accurate tuning recommendations, leading to better resource utilization and shorter time-to-value for users. Technologies/skills demonstrated: Python-based CLI validation and configuration management, test-driven development, modular refactoring, and evidence of end-to-end improvement in tuning workflows.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for NVIDIA/spark-rapids-tools focused on expanding runtime awareness and Photon integration to improve performance qualification workflows.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for NVIDIA/spark-rapids-tools focused on expanding runtime awareness and Photon integration to improve performance qualification workflows.
October 2024
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — NVIDIA/spark-rapids-tools. Focused on delivering business-value through improved observability and reliability for Photon workloads. Delivered two focused improvements: Photon-specific Spark SQL metrics analytics enabling accumulator-based metrics (peak memory, shuffle write time) with updated parsing helpers to recognize Photon metrics, enabling deeper performance insights and faster tuning (commit 1504968fa2bc48d4cbd74559b9cd9864d86c0040). Robust cluster information parsing strengthening error handling to validate worker counts and total cores per node, logging failures, and returning None on invalid/missing values to prevent operations with incomplete data (commit 730a05dc7b56750d2805ccb5d3261fe6fa938433). Overall impact: improved reliability, reduced troubleshooting time, and data-driven optimization for Photon workloads. Technologies/skills demonstrated: Python error handling, Spark metrics instrumentation, parsing logic enhancements, logging, and commit traceability.
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — NVIDIA/spark-rapids-tools. Focused on delivering business-value through improved observability and reliability for Photon workloads. Delivered two focused improvements: Photon-specific Spark SQL metrics analytics enabling accumulator-based metrics (peak memory, shuffle write time) with updated parsing helpers to recognize Photon metrics, enabling deeper performance insights and faster tuning (commit 1504968fa2bc48d4cbd74559b9cd9864d86c0040). Robust cluster information parsing strengthening error handling to validate worker counts and total cores per node, logging failures, and returning None on invalid/missing values to prevent operations with incomplete data (commit 730a05dc7b56750d2805ccb5d3261fe6fa938433). Overall impact: improved reliability, reduced troubleshooting time, and data-driven optimization for Photon workloads. Technologies/skills demonstrated: Python error handling, Spark metrics instrumentation, parsing logic enhancements, logging, and commit traceability.
October 2024
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Quality Metrics
Correctness94.0%
Maintainability86.8%
Architecture89.4%
Performance79.4%
AI Usage22.2%
Skills & Technologies
Programming Languages
BashJavaPythonScalaYAML
Technical Skills
Argument ParsingBackend DevelopmentBig DataBuild AutomationBuild ToolingCI/CDCLI DevelopmentCloud ComputingCloud PlatformsCloud Platforms (AWS, Azure, GCP)Cloud Platforms (Databricks, Dataproc, EMR)Cloud Platforms (Dataproc, EMR, Databricks)Code AnalysisCode LintingCode Organization
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