NVIDIA/spark-rapids-tools
Oct 2024 – Sep 2025
12 Months active
Languages Used
JavaPythonScalaYAMLBash
Technical Skills
Data EngineeringError HandlingPerformance AnalysisPython DevelopmentScalaSpark
Partho Sarthi
PROFILE
Partho Sarthi
Over the past year, Prashant Sarthi engineered advanced performance tuning and profiling features for the NVIDIA/spark-rapids-tools repository, focusing on GPU-accelerated Spark workloads. He developed and refined the AutoTuner, enabling dynamic configuration recommendations based on cluster and runtime analysis, and introduced distributed execution support for large-scale qualification runs. His work included robust error handling, memory management improvements, and integration of user-configurable Spark property overrides using Python, Scala, and YAML. By aligning Spark SQL partitioning with adaptive query execution and enhancing resource estimation, Prashant reduced job failures and improved throughput, demonstrating deep expertise in backend development, performance optimization, and cloud platforms.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
31Total
Bugs
7
Commits
31
Features
16
Lines of code
12,270
Activity Months12
Your Network
1353 people
Work History
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness93.0%
Maintainability87.4%
Architecture88.6%
Performance79.4%
AI Usage21.2%
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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