Hadoop @ Yahoo! - Internet Scale Data Processing

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From Eric Baldeschwieler's presentation "Hadoop @ Yahoo! - Internet Scale Data Processing" at the 2009 Cloud Computing Expo in Santa Clara, CA, USA. Here's the talk description on the Expo's site: http://cloudcomputingexpo.com/event/session/509

Tecnologia

Hadoop at Yahoo! Eric Baldeschwieler VP Hadoop Software Development Yahoo!

Agenda ,[object Object],[object Object],[object Object],[object Object]

Introduction to Hadoop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What we want you to know about Yahoo! and Hadoop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Largest Hadoop User 2006 now Hardware Internal Hadoop Users building new datacenter PB Disk, >82PB Today Nodes, >25,000 Today

The Largest Hadoop Tester ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The Largest Hadoop Contributor Core Patches

Why Hadoop @ Yahoo! ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Yahoo! front page - Case Study Ads Optimization Search Index

Yahoo! front page - Case Study Ads Optimization Content Optimization Search Index Machine Learned Spam filters Content Management Content Optimization

Large Applications 2008 2009 Webmap ~70 hours runtime ~300 TB shuffling ~200 TB output ~73 hours runtime ~490 TB shuffling ~280 TB output +55% Hardware Sort benchmarks (Jim Gray contest) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Largest cluster ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example: Search Assist TM ,[object Object],[object Object],[object Object],Before Hadoop After Hadoop Time 26 days 20 minutes Language C++ Python Development Time 2-3 weeks 2-3 days

Pig – Making Hadoop Easy! 1/20 the lines of code 1/16 the development time Performance within 2x

Pig – Making Hadoop Easy! Users = load ‘users’ as (name, age); Fltrd = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url); Jnd = join Fltrd by name, Pages by user; Grpd = group Jnd by url; Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks; Srtd = order Smmd by clicks desc; Top5 = limit Srtd 5; store Top5 into ‘top5sites’ ;

Current Yahoo! Development ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Questions? Eric Baldeschwieler VP Hadoop Software Development Yahoo! For more information: http://hadoop.apache.org/ http://hadoop.yahoo.com/ (including job openings)

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February 2017 HUG: Exactly-once end-to-end processing with Apache Apex

Yahoo Developer Network

Deep learning is a critical capability for gaining intelligence from datasets. Many existing frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline. The separated clusters require large datasets to be transferred between clusters, and introduce unwanted system complexity and latency for end-to-end learning. Yahoo introduced CaffeOnSpark to alleviate those pain points and bring deep learning onto Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data framework Apache Spark, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. The framework is complementary to non-deep learning libraries MLlib and Spark SQL, and its data-frame style API provides Spark applications with an easy mechanism to invoke deep learning over distributed datasets. Its server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck. Recently, we have released CaffeOnSpark at github.com/yahoo/CaffeOnSpark under Apache 2.0 License. In this talk, we will provide a technical overview of CaffeOnSpark, its API and deployment on a private cloud or public cloud (AWS EC2). A demo of IPython notebook will also be given to demonstrate how CaffeOnSpark will work with other Spark packages (ex. MLlib). Speakers: Andy Feng is a VP Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected major platforms for personalization, ads serving, NoSQL, and cloud infrastructure. Jun Shi is a Principal Engineer at Yahoo who specializes in machine learning platforms and large-scale machine learning algorithms. Prior to Yahoo, he was designing wireless communication chips at Broadcom, Qualcomm and Intel. Mridul Jain is Senior Principal at Yahoo, focusing on machine learning and big data platforms (especially realtime processing). He has worked on trending algorithms for search, unstructured content extraction, realtime processing for central monitoring platform, and is the co-author of Pig on Storm.

April 2016 HUG: CaffeOnSpark: Distributed Deep Learning on Spark Clusters

Yahoo Developer Network

Cassandraとh baseの比較して入門するno sql

Yutuki r

Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other. Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work). Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.

Yahoo compares Storm and Spark

Chicago Hadoop Users Group

Destaque (20)