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 doing behind the scenes?

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.

In the ever-evolving landscape of big data, Apache Spark and Apache Parquet continue to introduce game-changing features.

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.

Dive into a comprehensive load-testing exploration using Apache Spark with CPU-intensive workloads.

TLDR; Apache Spark 3.0 comes with many improvements, including new features for memory monitoring.

SparkMeasure

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 post you can find a few simple examples illustrating im

Topic: This post is about performance optimizations introduced in Apache Spark 2.0, in particular whole-stage cod