Tuning Apache Spark for Large Scale Workloads - Sital Kedia & Gaoxiang Liu

Описание: Apache Spark is a fast and flexible compute engine for a variety of diverse workloads. Optimizing performance for different applications often requires an understanding of Spark internals and can be challenging for Spark application developers. In this session, learn how Facebook tunes Spark to run large-scale workloads reliably and efficiently. The speakers will begin by explaining the various tools and techniques they use to discover performance bottlenecks in Spark jobs. Next, you'll hear about important configuration parameters and their experiments tuning these parameters on large-scale production workload.

You'll also learn about Facebook's new efforts towards automatically tuning several important configurations based on nature of the workload. The speakers will conclude by sharing their results with automatic tuning and future directions for the project.ing several important configurations based on nature of the workload. We will conclude by sharing our result with automatic tuning and future directions for the project.

Session hashtag: #SFexp1

Session overview:
Apache Spark at Facebook
Spark Architecture
Scaling Spark Driver
Dynamic Executor Allocation
Multi-threaded event processor
Better fetch failure handling
Scaling Spark Driver
executor memory layout
Tuning memory configurations
Eliminating disk i/o bottleneck
Scaling external shuffle service
Cache index files on shuffle server
Scaling external shuffle service
Application tuning
motivation
Auto tuning of mapper and reducer
Tools
Resources
Questions?

Sign up for a 1-day course on Apache Spark Tuning and Best Practices: https://bit.ly/2I0KMcj

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