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 tools and techniques aimed at enhancing Spark performance through detailed metrics analysis.
TLDR; Apache Spark 3.0 comes with many improvements, including new features for memory monitoring.
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, TF-Spawner, and how we used it to run distributed TensorFlow on a Kubernetes cluster.
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.
Topic: In this short post you can find examples of how to use IPython/Jupyter notebooks for running SQL on Oracle.
Topic: In this post you will find a short discussion and pointers to the code of a few sample scripts that I have written using Linux BPF/bcc and uprobes for Orac
Topic: in this post you can find examples of how to get started with using IPython/Jupyter notebooks for querying Apache Impala.
Topic: This post provides a short summary and pointers to previous work on Extended Stack Profiling for troubleshooting and performance investigations.
Topic: this post is about investigating Oracle wait events using stack profiles and flame graphs extended with OS-process state and Oracle wait event details.
The views expressed in this blog are those of the authors and cannot be regarded as representing CERN’s official position.
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