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Lecture

Advanced Spark: Partitioning and Optimization

In course

COM-490: Large-scale data science for real-world data

This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...

Description

This lecture covers advanced topics in Spark, focusing on partitioning strategies, memory optimization, and shuffle operations. It delves into the internals of Spark architecture, the cost of shuffle operations, and memory management. The instructor explains how to optimize Spark jobs by tuning partitions, avoiding shuffling, and minimizing memory usage. Additionally, the lecture explores Spark parallelization, RDDs, DataFrames, and the PySpark internals. Practical exercises and demos are included to illustrate the concepts discussed.

Instructors (3)

Olivier Verscheure

Sofiane Sarni

Pamela Isabel Delgado Borda

I am a PhD student in the School of Computer and Communication Sciences at EPFL. I am part of the Operating Systems Laboratory and my advisor is Prof. Willy Zwaenepoel. I received my Bachelor's degree in Systems Engineering from Universidad Catolica Boliviana, Bolivia in 2008 and Master's degree in Computer Science, specialization Foundations of Software from EPFL in 2012.

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Related concepts (30)

Memory management

Memory management is a form of resource management applied to computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time. Several methods have been devised that increase the effectiveness of memory management.

Parallel computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.

Memory management unit

A memory management unit (MMU), sometimes called paged memory management unit (PMMU), is a computer hardware unit that examines all memory references on the memory bus, translating these requests, known as virtual memory addresses, into physical addresses in main memory. In modern systems, programs generally have addresses that access the theoretical maximum memory of the computer architecture, 32 or 64 bits. The MMU maps the addresses from each program into separate areas in physical memory, which is generally much smaller than the theoretical maximum.

Memory paging

In computer operating systems, memory paging (or swapping on some Unix-like systems) is a memory management scheme by which a computer stores and retrieves data from secondary storage for use in main memory. In this scheme, the operating system retrieves data from secondary storage in same-size blocks called pages. Paging is an important part of virtual memory implementations in modern operating systems, using secondary storage to let programs exceed the size of available physical memory.

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

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