Tag: Tools
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Unlocking Apache Spark Performance: Three Open-Source Tools We Use at CERN
Unlocking Apache Spark Performance: Three Open-Source Tools We Use at CERN Apache Spark is incredibly powerful, but anyone who has worked with it long enough knows the feeling: Why is this job suddenly slower today? Why are executors running out of memory? Why is one stage taking 90% of the runtime? What exactly is Spark
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Troubleshoot I/O & Wait Latency with OraLatencyMap and PyLatencyMap
Troubleshoot I/O & Wait Latency with OraLatencyMap and PyLatencyMap I recently chased an Oracle performance issue where most reads were sub-millisecond (cache), but a thin band around ~10 ms (spindles) dominated total wait time. Classic bimodal latency: the fast band looked fine in averages, yet the rare slow band owned the delay. To investigate, and
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Kepler’s Mars Orbit Analysis with Python Notebooks & AI-Assisted Coding
Kepler’s Mars Orbit Analysis with Python Notebooks & AI-Assisted Coding Johannes Kepler ’s analysis of Mars’ orbit stands as one of the greatest achievements in scientific history, revealing the elliptical nature of planetary paths and establishing the foundational laws of planetary motion. In this post, you will explore how you can recreate Kepler’s revolutionary findings
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Building an Apache Spark Performance Lab: Tools and Techniques for Spark Optimization
Building an Apache Spark Performance Lab: Tools and Techniques for Spark Optimization Apache Spark is renowned for its speed and efficiency in handling large-scale data processing. However, optimizing Spark to achieve maximum performance requires a precise understanding of its inner workings. This blog post will guide you through establishing a Spark Performance Lab with essential
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Enhancing Apache Spark and Parquet Efficiency: A Deep Dive into Column Indexes and Bloom Filters
Enhancing Apache Spark and Parquet Efficiency: A Deep Dive into Column Indexes and Bloom Filters In the ever-evolving landscape of big data, Apache Spark and Apache Parquet continue to introduce game-changing features. Their latest updates have brought forward significant enhancements, including column indexes, bloom filters. This blog post delves into these new features, exploring their
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Performance Comparison of 5 JDKs on Apache Spark
Performance Comparison of 5 JDKs on Apache Spark Dive into a comprehensive load-testing exploration using Apache Spark with CPU-intensive workloads. This blog provides a comparative analysis of five distinct JDKs’ performance under heavy-duty tasks generated through Spark. Discover a meticulous breakdown of our testing methodology, tools, and insightful results. Keep in mind, our observations primarily
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Exploratory Notebooks for Deep Learning, AI, and Data Tools: A Beginner’s Guide
Exploratory Notebooks for Deep Learning, AI, and Data Tools: A Beginner’s Guide Are you looking at some resources to get you up to speed with popular Deep Learning and Data processing frameworks? This blog entry provides a curated collection of notebooks that will help you kickstart your journey. You can find the notebooks at this
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CPU Load Testing Exercises: Tools and Analysis for Oracle Database Servers
CPU Load Testing Exercises: Tools and Analysis for Oracle Database Servers This document describes some basic CPU load testing exercises on three different types of database servers used by the Oracle Service at CERN. It reports on the tests performed, tools used for data gathering, data analysis, findings, and lessons learned. Motivations CPU usage is
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Apache Spark 3.0 Memory Monitoring Improvements
Apache Spark 3.0 Memory Monitoring Improvements TLDR; Apache Spark 3.0 comes with many improvements, including new features for memory monitoring. This can help you troubleshooting memory usage and optimizing the memory configuration of your Spark jobs for better performance and stability, see SPARK-23429 and SPARK-27189. The problem with memory Memory is key for the performance
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Distributed Deep Learning for Physics with TensorFlow and Kubernetes
Distributed Deep Learning for Physics with TensorFlow and Kubernetes Summary: This post details a solution for distributed deep learning training for a High Energy Physics use case, deployed using cloud resources and Kubernetes. You will find the results for training using CPU and GPU nodes. This post also describes an experimental tool that we developed,