Tag: Parquet
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ATLAS DCS Analysis with Apache Spark and Jupyter Notebooks
ATLAS DCS Analysis with Apache Spark and Jupyter Notebooks The ATLAS Detector Control System (DCS) at CERN is essential for ensuring optimal detector performance. Each year, the system generates tens of billions of time-stamped sensor readings, presenting considerable challenges for large-scale data analysis. Although these data are stored in Oracle databases that excel in real-time
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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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Enhancing Apache Spark Performance with Flame Graphs: A Practical Example Using Grafana Pyroscope
Enhancing Apache Spark Performance with Flame Graphs: A Practical Example Using Grafana Pyroscope TL;DR Explore a step-by-step example of troubleshooting Apache Spark job performance using flame graph visualization and profiling. Discover the seamless integration of Grafana Pyroscope with Spark for streamlined data collection and visualization. The Puzzle of the Slow Query Set within the framework
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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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Evaluation of Erasure Coding in Hadoop 3
Evaluation of Erasure Coding in Hadoop 3 Authored By: Nazerke Seidan, Emil Kleszcz, Zbigniew Baranowski Published By: CERN IT-DB-SAS In this post, we will dive into the evaluation of the Erasure Coding feature of Hadoop 3 that I worked on this summer as a CERN Openlab intern. The evaluation has been performed on one of
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Machine Learning Pipelines for High Energy Physics Using Apache Spark with BigDL and Analytics Zoo
Machine Learning Pipelines for High Energy Physics Using Apache Spark with BigDL and Analytics Zoo Topic: This post describes a data pipeline for a machine learning task of interest in high energy physics: building a particle classifier to improve event selection at the particle detectors. The pipeline is built using tools from the “Big Data
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A Performance Dashboard for Apache Spark
A Performance Dashboard for Apache Spark Topic: This post dives into the steps for deploying a performance dashboard for Apache Spark, using Spark metrics system instrumentation, InfluxDB and Grafana. What problem does it solve: The dashboard can provide important insights for performance troubleshooting and online monitoring of Apache Spark workloads. In particular when running Spark
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Performance Analysis of a CPU-Intensive Workload in Apache Spark
Performance Analysis of a CPU-Intensive Workload in Apache Spark Topic: This post is about techniques and tools for measuring and understanding CPU-bound and memory-bound workloads in Apache Spark. You will find examples applied to studying a simple workload consisting of reading Apache Parquet files into a Spark DataFrame. Why are the topics discussed here relevant
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Diving into Spark and Parquet Workloads, by Example
Diving into Spark and Parquet Workloads, by Example Topic: In this post you can find a few simple examples illustrating important features of Spark when reading partitioned tables stored in Parquet, in particular with a focus on performance investigations. The main topics covered are: Motivations: The combination of Spark and Parquet currently is a very