Overview of Spark for HPC

MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.

This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.

This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark

This document provides an overview of the MapReduce paradigm and Hadoop framework. It describes how MapReduce uses a map and reduce phase to process large amounts of distributed data in parallel. Hadoop is an open-source implementation of MapReduce that stores data in HDFS. It allows applications to work with thousands of computers and petabytes of data. Key advantages of MapReduce include fault tolerance, scalability, and flexibility. While it is well-suited for batch processing, it may not replace traditional databases for data warehousing. Overall efficiency remains an area for improvement.

This document summarizes Viadeo's use of Apache Spark. It discusses how Spark is used to build models for job offer click prediction and member segmentation. Spark jobs process event log data from HDFS and HBase to cluster job titles, build relationship graphs, compute input variables for regression models, and evaluate segments. The models improve click-through rates and allow flexible, fast member targeting. Future work includes indexing segmentations and exposing them for analytics and online campaign building.

This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.

Uber developed an new Spark ingestion system, Marmaray, for data ingestion from various sources. It’s designed to ingest billions of Kafka messages every 30 minutes. The amount of data handled by the pipeline is of the order hundreds of TBs. Omar details how to tackle such scale and insights into the optimizations techniques. Some key highlights are how to understand bottlenecks in Spark applications, to cache or not to cache your Spark DAG to avoid rereading your input data, how to effectively use accumulators to avoid unnecessary Spark actions, how to inspect your heap and nonheap memory usage across hundreds of executors, how you can change the layout of data to save long-term storage cost, how to effectively use serializers and compression to save network and disk traffic, and how to reduce amortize the cost of your application by multiplexing your jobs, different techniques for reducing memory footprint, runtime, and on-disk usage. CGI was able to significantly (~10%–40%) reduce memory footprint, runtime, and disk usage. Speaker: Omkar Joshi (Uber) Omkar Joshi is a senior software engineer on Uber’s Hadoop platform team, where he’s architecting Marmaray. Previously, he led object store and NFS solutions at Hedvig and was an initial contributor to Hadoop’s YARN scheduler.

This document provides an overview of MapReduce programming and best practices for Apache Hadoop. It describes the key components of Hadoop including HDFS, MapReduce, and the data flow. It also discusses optimizations that can be made to MapReduce jobs, such as using combiners, compression, and speculation. Finally, it outlines some anti-patterns to avoid and tips for debugging MapReduce applications.

Pig is a platform for analyzing large datasets that uses a high-level language to express data analysis programs. It compiles programs into MapReduce jobs that can run in parallel on a Hadoop cluster. Pig provides built-in functions for common tasks and allows users to define their own custom functions (UDFs). Programs can be run locally or on a Hadoop cluster by placing commands in a script or Grunt shell.

The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.

If you are interviewing for a Hadoop jobs, here are few frequently asked Question and Answers which can be asked in an interview.

Zstandard is a fast compression algorithm which you can use in Apache Spark in various way. In this talk, I briefly summarized the evolution history of Apache Spark in this area and four main use cases and the benefits and the next steps: 1) ZStandard can optimize Spark local disk IO by compressing shuffle files significantly. This is very useful in K8s environments. It’s beneficial not only when you use `emptyDir` with `memory` medium, but also it maximizes OS cache benefit when you use shared SSDs or container local storage. In Spark 3.2, SPARK-34390 takes advantage of ZStandard buffer pool feature and its performance gain is impressive, too. 2) Event log compression is another area to save your storage cost on the cloud storage like S3 and to improve the usability. SPARK-34503 officially switched the default event log compression codec from LZ4 to Zstandard. 3) Zstandard data file compression can give you more benefits when you use ORC/Parquet files as your input and output. Apache ORC 1.6 supports Zstandardalready and Apache Spark enables it via SPARK-33978. The upcoming Parquet 1.12 will support Zstandard compression. 4) Last, but not least, since Apache Spark 3.0, Zstandard is used to serialize/deserialize MapStatus data instead of Gzip. There are more community works to utilize Zstandard to improve Spark. For example, Apache Avro community also supports Zstandard and SPARK-34479 aims to support Zstandard in Spark’s avro file format in Spark 3.2.0.

This document discusses common mistakes people make when writing Spark applications and provides recommendations to address them. It covers issues related to executor configuration, application failures due to shuffle block sizes exceeding limits, slow jobs caused by data skew, and managing the DAG to avoid excessive shuffles and stages. Recommendations include using smaller executors, increasing the number of partitions, addressing skew through techniques like salting, and preferring ReduceByKey over GroupByKey and TreeReduce over Reduce to improve performance and resource usage.

This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.

This document discusses using Python for Hadoop and data mining. It introduces Dumbo, which allows writing Hadoop programs in Python. K-means clustering in MapReduce is also covered. Dumbo provides a Pythonic API for MapReduce and allows extending Hadoop functionality. Examples demonstrate implementing K-means in Dumbo and optimizing it by computing partial centroids locally in mappers. The document also lists Python books and tools for data mining and scientific computing.

The document provides an overview of various Apache Pig features including: - The Grunt shell which allows interactive execution of Pig Latin scripts and access to HDFS. - Advanced relational operators like SPLIT, ASSERT, CUBE, SAMPLE, and RANK for transforming data. - Built-in functions and user defined functions (UDFs) for data processing. Macros can also be defined. - Running Pig in local or MapReduce mode and accessing HDFS from within Pig scripts.

Overview of myHadoop 0.30, a framework for deploying Hadoop on existing high-performance computing infrastructure. Discussion of how to install it, spin up a Hadoop cluster, and use the new features. myHadoop 0.30's project page is now on GitHub (https://github.com/glennklockwood/myhadoop) and the latest release tarball can be downloaded from my website (glennklockwood.com/files/myhadoop-0.30.tar.gz)

A brief overview of a case study where 438 human genomes underwent read mapping and variant calling in under two months. Architectural requirements for the multi-stage pipeline are covered.

A transpiler is a type of compiler that takes source code from one programming language and outputs source code in another programming language, while a compiler converts source code directly into machine code. Transpilers allow code to be translated between languages at similar levels of abstraction, such as C++ to C, while compilers translate to a lower level like C to assembly code. Transpilers are useful for porting codebases to new languages, translating between language versions, or implementing domain-specific languages. Popular transpilers include Babel, TypeScript, and Emscripten.

Presented at the Oak Ridge Interconnects Workshop in 1999. A fun historical perpsective on where the HPC industry in 1999 thought we would be going forward into the petascale industry.

This document discusses using Apache Spark to parallelize proteomic scoring, which involves matching tandem mass spectra against a large database of peptides. The author developed a version of the Comet scoring algorithm and implemented it on a Spark cluster. This outperformed single machines by over 10x, allowing searches that took 8 hours to be done in under 30 minutes. Key considerations for running large jobs in parallel on Spark are discussed, such as input formatting, accumulator functions for debugging, and smart partitioning of data. The performance improvements allow searching larger databases and considering more modifications.

C++ AMPの概要と簡単なチュートリアルです。まだ使いだしなので詳しいことは書けませんが、導入には十分でしょうか？

Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы распределенной обработки больших объемов данных в Hadoop" Видео лекции курса https://www.youtube.com/playlist?list=PLrCZzMib1e9rPxMIgPri9YnOpvyDAL9HD

スパース性に基づく機械学習(機械学習プロフェッショナルシリーズ)の勉強会の資料です。 1章のスライドです。

Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы распределенной обработки больших объемов данных в Hadoop" Видео лекции курса https://www.youtube.com/playlist?list=PLrCZzMib1e9rPxMIgPri9YnOpvyDAL9HD

機械学習プロフェッショナルシリーズ輪読会資料

Glenn K. Lockwood's document summarizes his professional background and experience with data-intensive computing systems. It then discusses the Gordon supercomputer deployed at SDSC in 2012, which was one of the world's first systems to use flash storage. The document analyzes Gordon's architecture using burst buffers and SSDs, experiences using the flash file system, and lessons learned. It also compares Gordon's proto-burst buffer approach to the dedicated burst buffer nodes on the Cori supercomputer.