Plumbing the Web. Narayanan Shivakumar. Google Distinguished Entrepreneur & Director
|
|
- Brenda Lydia Watkins
- 6 years ago
- Views:
Transcription
1
2 Plumbing the Web Narayanan Shivakumar Google Distinguished Entrepreneur & Director
3 Google Developer Day 2007 powered by 3 Copyright 2007, Google Inc
4 Developers and Google AJAX Search API Google Code Project Hosting Google Web Toolkit Calendar Data API Base Data API Blogger Data API Notebook Data API PicasaData API Spreadsheets Data API Google SOAP Search API Desktop API Sitemaps API Gadgets API AJAX Feed API Mashup Editor Mapplets Google Gears Copyright 2007, Google Inc
5 How much information is out there? How large is the Web? Hundreds of billions of documents? Trillions? ~10KB/doc => 100s of Terabytes Then there s everything else , personal files, closed databases, broadcast media, print, etc. Estimated 5 Exabytes/year (growing at 30%)* 800MB/year/person ~90% in magnetic media Web is just a tiny starting point Source: How much information Copyright 2007, Google Inc
6 Early Search Search Link Extraction Web pages 6 Copyright 2007, Google Inc
7 Webserver-search ecosystem Part A: Sitemaps, tell us what you have Part B: Feedback to webservers about problems 7 Copyright 2007, Google Inc
8 Sitemaps ( ls for web) XML sitemaps auto-produced and maintained on webservers {<url>, <changerate> <lastmod> <priority> } Autodiscovery through robots.txt Log structured protocol Scalable from 50 ->50M+ urls 8 Copyright 2007, Google Inc
9 Sitemaps adoption Open protocol launched in Jun 05 under Creative-Commons Joint support announced by MSN, Yahoo in Nov 06 ( Auto-discovery thro robots.txt in Apr 07 (+IBM, Ask.com) Billions of URLs auto-produced by servers, tools, plugins 9 Copyright 2007, Google Inc
10 Early Search Search Link Extraction Web pages 10 Copyright 2007, Google Inc
11 Comprehensive Search Search Sitemaps WmTools Link Extraction Web pages 11 Copyright 2007, Google Inc
12 Google s Developer Products Integrate Integrate Google services Reach Reach Google users Build Build next gen web apps 12 Copyright 2007, Google Inc
13 Integrate Reach Build 13 Copyright 2007, Google Inc
14 Integrate Reach Build 14 Copyright 2007, Google Inc
15 Integrate Reach Build 15 Copyright 2007, Google Inc
16 Integrate Reach Build 16 Copyright 2007, Google Inc
17 Integrate Reach Build 17 Copyright 2007, Google Inc
18 Integrate Reach Build 18 Copyright 2007, Google Inc
19 Integrate Reach Build 19 Copyright 2007, Google Inc
20 Integrate Reach Build 20 Copyright 2007, Google Inc
21 Integrate Reach Build 21 Copyright 2007, Google Inc
22 Integrate Reach Build 22 Copyright 2007, Google Inc
23 Integrate Reach Build 23 Copyright 2007, Google Inc
24 Behind the plumbing Apps Standards Systems Infra Hardware 24 Copyright 2007, Google Inc
25 Google s Explosive Computational Requirements Every Google service sees continuing growth in computational needs More queries More users, happier users More data Bigger web, mailbox, blog, etc. Better results Find the right information, and find it faster better results more data more queries 25 Copyright 2007, Google Inc
26 Hardware Design Philosophy Prefer low-end server/pc-class designs Build lots of them! Why? Single machine performance is not interesting Our smaller problems are too large for any single system Large problems are easily partitioned into multiple threads Ultra-reliable hardware makes programmers lazy Most reliable platform will still fail fault-tolerant software needed Fault-tolerant software enables use of commodity components Interesting systems can be designed with commodity components 26 Copyright 2007, Google Inc
27 google.stanford.edu (circa 1997) 27 Copyright 2007, Google Inc
28 google.com (1999) 28 Copyright 2007, Google Inc
29 Google Data Center (circa 2000) 29 Copyright 2007, Google Inc
30 google.com (new data center 2001) 30 Copyright 2007, Google Inc
31 google.com (3 days later) 31 Copyright 2007, Google Inc
32 Current Design In-house rack design PC-class motherboards Low-end storage and networking hardware Linux + in-house software 32 Copyright 2007, Google Inc
33 33 Copyright 2007, Google Inc
34 Behind the plumbing Apps Standards Systems Infra Hardware 34 Copyright 2007, Google Inc
35 Systems Infrastructure Goal: Create very large scale, high performance computing infrastructure Hardware + software systems to make it easy to build products Focus on price/performance, and ease of use Enables better products: indices containing more documents updated more often faster queries faster product development cycles 35 Copyright 2007, Google Inc
36 GFS: Google File System Why YADFS? Google has unique FS requirements Huge read/write bandwidth Reliability over thousands of nodes Mostly operating on large data blocks Need efficient distributed operations Unfair advantage We have control over applications, libraries and operating system 36 Copyright 2007, Google Inc
37 GFS Setup Masters Replicas GFS Master GFS Master Misc. servers Client Client C 0 C 1 C 1 C 0 C 5 C 5 C 2 C 5 C 3 C 2 Chunkserver 1 Chunkserver 2 Chunkserver N Master manages metadata Data transfers happen directly between clients/chunkservers Files broken into chunks (typically 64 MB) 37 Copyright 2007, Google Inc
38 MapReduce + BigTable Okay, GFS lets us store lots of data now what? We want to process that data in new and interesting ways! MapReduce: a programming model and library to simplify large-scale computations on large clusters BigTable: A large-scale storage system for semi-structured data Database-like model, but data stored on thousands of machines.. 38 Copyright 2007, Google Inc
39 Developers and Google AJAX Search API Google Code Project Hosting Google Web Toolkit Calendar Data API Base Data API Blogger Data API Notebook Data API PicasaData API Spreadsheets Data API Google SOAP Search API Desktop API Sitemaps API Gadgets API AJAX Feed API Mashup Editor Mapplets Google Gears Copyright 2007, Google Inc
40 40 Copyright 2007, Google Inc
41
CPSC 426/526. Cloud Computing. Ennan Zhai. Computer Science Department Yale University
CPSC 426/526 Cloud Computing Ennan Zhai Computer Science Department Yale University Recall: Lec-7 In the lec-7, I talked about: - P2P vs Enterprise control - Firewall - NATs - Software defined network
More informationDistributed Systems. 05r. Case study: Google Cluster Architecture. Paul Krzyzanowski. Rutgers University. Fall 2016
Distributed Systems 05r. Case study: Google Cluster Architecture Paul Krzyzanowski Rutgers University Fall 2016 1 A note about relevancy This describes the Google search cluster architecture in the mid
More informationGoogle: A Computer Scientist s Playground
Google: A Computer Scientist s Playground Jochen Hollmann Google Zürich und Trondheim joho@google.com Outline Mission, data, and scaling Systems infrastructure Parallel programming model: MapReduce Googles
More informationGoogle: A Computer Scientist s Playground
Outline Mission, data, and scaling Google: A Computer Scientist s Playground Jochen Hollmann Google Zürich und Trondheim joho@google.com Systems infrastructure Parallel programming model: MapReduce Googles
More informationGeorgia Institute of Technology ECE6102 4/20/2009 David Colvin, Jimmy Vuong
Georgia Institute of Technology ECE6102 4/20/2009 David Colvin, Jimmy Vuong Relatively recent; still applicable today GFS: Google s storage platform for the generation and processing of data used by services
More informationLessons Learned While Building Infrastructure Software at Google
Lessons Learned While Building Infrastructure Software at Google Jeff Dean jeff@google.com Google Circa 1997 (google.stanford.edu) Corkboards (1999) Google Data Center (2000) Google Data Center (2000)
More informationMapReduce. U of Toronto, 2014
MapReduce U of Toronto, 2014 http://www.google.org/flutrends/ca/ (2012) Average Searches Per Day: 5,134,000,000 2 Motivation Process lots of data Google processed about 24 petabytes of data per day in
More informationGFS Overview. Design goals/priorities Design for big-data workloads Huge files, mostly appends, concurrency, huge bandwidth Design for failures
GFS Overview Design goals/priorities Design for big-data workloads Huge files, mostly appends, concurrency, huge bandwidth Design for failures Interface: non-posix New op: record appends (atomicity matters,
More informationCA485 Ray Walshe Google File System
Google File System Overview Google File System is scalable, distributed file system on inexpensive commodity hardware that provides: Fault Tolerance File system runs on hundreds or thousands of storage
More information! Design constraints. " Component failures are the norm. " Files are huge by traditional standards. ! POSIX-like
Cloud background Google File System! Warehouse scale systems " 10K-100K nodes " 50MW (1 MW = 1,000 houses) " Power efficient! Located near cheap power! Passive cooling! Power Usage Effectiveness = Total
More informationBigData and Map Reduce VITMAC03
BigData and Map Reduce VITMAC03 1 Motivation Process lots of data Google processed about 24 petabytes of data per day in 2009. A single machine cannot serve all the data You need a distributed system to
More informationDistributed File Systems II
Distributed File Systems II To do q Very-large scale: Google FS, Hadoop FS, BigTable q Next time: Naming things GFS A radically new environment NFS, etc. Independence Small Scale Variety of workloads Cooperation
More informationECE 7650 Scalable and Secure Internet Services and Architecture ---- A Systems Perspective
ECE 7650 Scalable and Secure Internet Services and Architecture ---- A Systems Perspective Part II: Data Center Software Architecture: Topic 1: Distributed File Systems GFS (The Google File System) 1 Filesystems
More informationData Clustering on the Parallel Hadoop MapReduce Model. Dimitrios Verraros
Data Clustering on the Parallel Hadoop MapReduce Model Dimitrios Verraros Overview The purpose of this thesis is to implement and benchmark the performance of a parallel K- means clustering algorithm on
More informationDistributed Systems 16. Distributed File Systems II
Distributed Systems 16. Distributed File Systems II Paul Krzyzanowski pxk@cs.rutgers.edu 1 Review NFS RPC-based access AFS Long-term caching CODA Read/write replication & disconnected operation DFS AFS
More informationThe amount of data increases every day Some numbers ( 2012):
1 The amount of data increases every day Some numbers ( 2012): Data processed by Google every day: 100+ PB Data processed by Facebook every day: 10+ PB To analyze them, systems that scale with respect
More information2/26/2017. The amount of data increases every day Some numbers ( 2012):
The amount of data increases every day Some numbers ( 2012): Data processed by Google every day: 100+ PB Data processed by Facebook every day: 10+ PB To analyze them, systems that scale with respect to
More informationIntroduction to Hadoop. Owen O Malley Yahoo!, Grid Team
Introduction to Hadoop Owen O Malley Yahoo!, Grid Team owen@yahoo-inc.com Who Am I? Yahoo! Architect on Hadoop Map/Reduce Design, review, and implement features in Hadoop Working on Hadoop full time since
More informationNPTEL Course Jan K. Gopinath Indian Institute of Science
Storage Systems NPTEL Course Jan 2012 (Lecture 39) K. Gopinath Indian Institute of Science Google File System Non-Posix scalable distr file system for large distr dataintensive applications performance,
More informationData Analysis Using MapReduce in Hadoop Environment
Data Analysis Using MapReduce in Hadoop Environment Muhammad Khairul Rijal Muhammad*, Saiful Adli Ismail, Mohd Nazri Kama, Othman Mohd Yusop, Azri Azmi Advanced Informatics School (UTM AIS), Universiti
More informationGFS: The Google File System. Dr. Yingwu Zhu
GFS: The Google File System Dr. Yingwu Zhu Motivating Application: Google Crawl the whole web Store it all on one big disk Process users searches on one big CPU More storage, CPU required than one PC can
More informationHDFS: Hadoop Distributed File System. Sector: Distributed Storage System
GFS: Google File System Google C/C++ HDFS: Hadoop Distributed File System Yahoo Java, Open Source Sector: Distributed Storage System University of Illinois at Chicago C++, Open Source 2 System that permanently
More informationCSE 124: Networked Services Fall 2009 Lecture-19
CSE 124: Networked Services Fall 2009 Lecture-19 Instructor: B. S. Manoj, Ph.D http://cseweb.ucsd.edu/classes/fa09/cse124 Some of these slides are adapted from various sources/individuals including but
More informationProgramming model and implementation for processing and. Programs can be automatically parallelized and executed on a large cluster of machines
A programming model in Cloud: MapReduce Programming model and implementation for processing and generating large data sets Users specify a map function to generate a set of intermediate key/value pairs
More informationBigTable: A System for Distributed Structured Storage
BigTable: A System for Distributed Structured Storage Jeff Dean Joint work with: Mike Burrows, Tushar Chandra, Fay Chang, Mike Epstein, Andrew Fikes, Sanjay Ghemawat, Robert Griesemer, Bob Gruber, Wilson
More informationGoogle File System 2
Google File System 2 goals monitoring, fault tolerance, auto-recovery (thousands of low-cost machines) focus on multi-gb files handle appends efficiently (no random writes & sequential reads) co-design
More informationCSE 124: Networked Services Lecture-16
Fall 2010 CSE 124: Networked Services Lecture-16 Instructor: B. S. Manoj, Ph.D http://cseweb.ucsd.edu/classes/fa10/cse124 11/23/2010 CSE 124 Networked Services Fall 2010 1 Updates PlanetLab experiments
More informationGFS: The Google File System
GFS: The Google File System Brad Karp UCL Computer Science CS GZ03 / M030 24 th October 2014 Motivating Application: Google Crawl the whole web Store it all on one big disk Process users searches on one
More informationThe Google File System
October 13, 2010 Based on: S. Ghemawat, H. Gobioff, and S.-T. Leung: The Google file system, in Proceedings ACM SOSP 2003, Lake George, NY, USA, October 2003. 1 Assumptions Interface Architecture Single
More informationGoogle File System. Arun Sundaram Operating Systems
Arun Sundaram Operating Systems 1 Assumptions GFS built with commodity hardware GFS stores a modest number of large files A few million files, each typically 100MB or larger (Multi-GB files are common)
More informationBigTable: A Distributed Storage System for Structured Data (2006) Slides adapted by Tyler Davis
BigTable: A Distributed Storage System for Structured Data (2006) Slides adapted by Tyler Davis Motivation Lots of (semi-)structured data at Google URLs: Contents, crawl metadata, links, anchors, pagerank,
More informationDistributed System. Gang Wu. Spring,2018
Distributed System Gang Wu Spring,2018 Lecture7:DFS What is DFS? A method of storing and accessing files base in a client/server architecture. A distributed file system is a client/server-based application
More informationThe Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff and Shun Tak Leung Google* Shivesh Kumar Sharma fl4164@wayne.edu Fall 2015 004395771 Overview Google file system is a scalable distributed file system
More informationDistributed Systems. Lec 10: Distributed File Systems GFS. Slide acks: Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung
Distributed Systems Lec 10: Distributed File Systems GFS Slide acks: Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung 1 Distributed File Systems NFS AFS GFS Some themes in these classes: Workload-oriented
More informationGoogle is Really Different.
COMP 790-088 -- Distributed File Systems Google File System 7 Google is Really Different. Huge Datacenters in 5+ Worldwide Locations Datacenters house multiple server clusters Coming soon to Lenior, NC
More informationThe Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung SOSP 2003 presented by Kun Suo Outline GFS Background, Concepts and Key words Example of GFS Operations Some optimizations in
More informationgoals monitoring, fault tolerance, auto-recovery (thousands of low-cost machines) handle appends efficiently (no random writes & sequential reads)
Google File System goals monitoring, fault tolerance, auto-recovery (thousands of low-cost machines) focus on multi-gb files handle appends efficiently (no random writes & sequential reads) co-design GFS
More informationThe MapReduce Abstraction
The MapReduce Abstraction Parallel Computing at Google Leverages multiple technologies to simplify large-scale parallel computations Proprietary computing clusters Map/Reduce software library Lots of other
More informationBigtable: A Distributed Storage System for Structured Data By Fay Chang, et al. OSDI Presented by Xiang Gao
Bigtable: A Distributed Storage System for Structured Data By Fay Chang, et al. OSDI 2006 Presented by Xiang Gao 2014-11-05 Outline Motivation Data Model APIs Building Blocks Implementation Refinement
More informationGoogle File System. By Dinesh Amatya
Google File System By Dinesh Amatya Google File System (GFS) Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung designed and implemented to meet rapidly growing demand of Google's data processing need a scalable
More informationBigTable A System for Distributed Structured Storage
BigTable A System for Distributed Structured Storage Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber Adapted
More informationA brief history on Hadoop
Hadoop Basics A brief history on Hadoop 2003 - Google launches project Nutch to handle billions of searches and indexing millions of web pages. Oct 2003 - Google releases papers with GFS (Google File System)
More informationMapReduce & BigTable
CPSC 426/526 MapReduce & BigTable Ennan Zhai Computer Science Department Yale University Lecture Roadmap Cloud Computing Overview Challenges in the Clouds Distributed File Systems: GFS Data Process & Analysis:
More informationParallel Programming Principle and Practice. Lecture 10 Big Data Processing with MapReduce
Parallel Programming Principle and Practice Lecture 10 Big Data Processing with MapReduce Outline MapReduce Programming Model MapReduce Examples Hadoop 2 Incredible Things That Happen Every Minute On The
More informationRecap. CSE 486/586 Distributed Systems Google Chubby Lock Service. Recap: First Requirement. Recap: Second Requirement. Recap: Strengthening P2
Recap CSE 486/586 Distributed Systems Google Chubby Lock Service Steve Ko Computer Sciences and Engineering University at Buffalo Paxos is a consensus algorithm. Proposers? Acceptors? Learners? A proposer
More informationDistributed Filesystem
Distributed Filesystem 1 How do we get data to the workers? NAS Compute Nodes SAN 2 Distributing Code! Don t move data to workers move workers to the data! - Store data on the local disks of nodes in the
More informationΕΠΛ 602:Foundations of Internet Technologies. Cloud Computing
ΕΠΛ 602:Foundations of Internet Technologies Cloud Computing 1 Outline Bigtable(data component of cloud) Web search basedonch13of thewebdatabook 2 What is Cloud Computing? ACloudis an infrastructure, transparent
More informationCSE 444: Database Internals. Lectures 26 NoSQL: Extensible Record Stores
CSE 444: Database Internals Lectures 26 NoSQL: Extensible Record Stores CSE 444 - Spring 2014 1 References Scalable SQL and NoSQL Data Stores, Rick Cattell, SIGMOD Record, December 2010 (Vol. 39, No. 4)
More informationThe Google File System. Alexandru Costan
1 The Google File System Alexandru Costan Actions on Big Data 2 Storage Analysis Acquisition Handling the data stream Data structured unstructured semi-structured Results Transactions Outline File systems
More informationThe Google File System
The Google File System By Ghemawat, Gobioff and Leung Outline Overview Assumption Design of GFS System Interactions Master Operations Fault Tolerance Measurements Overview GFS: Scalable distributed file
More informationGoogle File System. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google fall DIP Heerak lim, Donghun Koo
Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google 2017 fall DIP Heerak lim, Donghun Koo 1 Agenda Introduction Design overview Systems interactions Master operation Fault tolerance
More informationReferences. What is Bigtable? Bigtable Data Model. Outline. Key Features. CSE 444: Database Internals
References CSE 444: Database Internals Scalable SQL and NoSQL Data Stores, Rick Cattell, SIGMOD Record, December 2010 (Vol 39, No 4) Lectures 26 NoSQL: Extensible Record Stores Bigtable: A Distributed
More informationGoogle File System (GFS) and Hadoop Distributed File System (HDFS)
Google File System (GFS) and Hadoop Distributed File System (HDFS) 1 Hadoop: Architectural Design Principles Linear scalability More nodes can do more work within the same time Linear on data size, linear
More informationGoogle Disk Farm. Early days
Google Disk Farm Early days today CS 5204 Fall, 2007 2 Design Design factors Failures are common (built from inexpensive commodity components) Files large (multi-gb) mutation principally via appending
More informationMap-Reduce. Marco Mura 2010 March, 31th
Map-Reduce Marco Mura (mura@di.unipi.it) 2010 March, 31th This paper is a note from the 2009-2010 course Strumenti di programmazione per sistemi paralleli e distribuiti and it s based by the lessons of
More informationThe Google File System (GFS)
1 The Google File System (GFS) CS60002: Distributed Systems Antonio Bruto da Costa Ph.D. Student, Formal Methods Lab, Dept. of Computer Sc. & Engg., Indian Institute of Technology Kharagpur 2 Design constraints
More informationCLOUD-SCALE FILE SYSTEMS
Data Management in the Cloud CLOUD-SCALE FILE SYSTEMS 92 Google File System (GFS) Designing a file system for the Cloud design assumptions design choices Architecture GFS Master GFS Chunkservers GFS Clients
More informationGoogle File System, Replication. Amin Vahdat CSE 123b May 23, 2006
Google File System, Replication Amin Vahdat CSE 123b May 23, 2006 Annoucements Third assignment available today Due date June 9, 5 pm Final exam, June 14, 11:30-2:30 Google File System (thanks to Mahesh
More informationDistributed Systems. 15. Distributed File Systems. Paul Krzyzanowski. Rutgers University. Fall 2017
Distributed Systems 15. Distributed File Systems Paul Krzyzanowski Rutgers University Fall 2017 1 Google Chubby ( Apache Zookeeper) 2 Chubby Distributed lock service + simple fault-tolerant file system
More informationOutline. INF3190:Distributed Systems - Examples. Last week: Definitions Transparencies Challenges&pitfalls Architecturalstyles
INF3190:Distributed Systems - Examples Thomas Plagemann & Roman Vitenberg Outline Last week: Definitions Transparencies Challenges&pitfalls Architecturalstyles Today: Examples Googel File System (Thomas)
More informationDistributed Systems. 15. Distributed File Systems. Paul Krzyzanowski. Rutgers University. Fall 2016
Distributed Systems 15. Distributed File Systems Paul Krzyzanowski Rutgers University Fall 2016 1 Google Chubby 2 Chubby Distributed lock service + simple fault-tolerant file system Interfaces File access
More informationStaggeringly Large File Systems. Presented by Haoyan Geng
Staggeringly Large File Systems Presented by Haoyan Geng Large-scale File Systems How Large? Google s file system in 2009 (Jeff Dean, LADIS 09) - 200+ clusters - Thousands of machines per cluster - Pools
More informationCS /30/17. Paul Krzyzanowski 1. Google Chubby ( Apache Zookeeper) Distributed Systems. Chubby. Chubby Deployment.
Distributed Systems 15. Distributed File Systems Google ( Apache Zookeeper) Paul Krzyzanowski Rutgers University Fall 2017 1 2 Distributed lock service + simple fault-tolerant file system Deployment Client
More informationPLATFORM AND SOFTWARE AS A SERVICE THE MAPREDUCE PROGRAMMING MODEL AND IMPLEMENTATIONS
PLATFORM AND SOFTWARE AS A SERVICE THE MAPREDUCE PROGRAMMING MODEL AND IMPLEMENTATIONS By HAI JIN, SHADI IBRAHIM, LI QI, HAIJUN CAO, SONG WU and XUANHUA SHI Prepared by: Dr. Faramarz Safi Islamic Azad
More informationCS 345A Data Mining. MapReduce
CS 345A Data Mining MapReduce Single-node architecture CPU Machine Learning, Statistics Memory Classical Data Mining Disk Commodity Clusters Web data sets can be very large Tens to hundreds of terabytes
More informationIntroduction to MapReduce
Basics of Cloud Computing Lecture 4 Introduction to MapReduce Satish Srirama Some material adapted from slides by Jimmy Lin, Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet, Google Distributed
More informationThe Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung December 2003 ACM symposium on Operating systems principles Publisher: ACM Nov. 26, 2008 OUTLINE INTRODUCTION DESIGN OVERVIEW
More informationCS6030 Cloud Computing. Acknowledgements. Today s Topics. Intro to Cloud Computing 10/20/15. Ajay Gupta, WMU-CS. WiSe Lab
CS6030 Cloud Computing Ajay Gupta B239, CEAS Computer Science Department Western Michigan University ajay.gupta@wmich.edu 276-3104 1 Acknowledgements I have liberally borrowed these slides and material
More informationCS427 Multicore Architecture and Parallel Computing
CS427 Multicore Architecture and Parallel Computing Lecture 9 MapReduce Prof. Li Jiang 2014/11/19 1 What is MapReduce Origin from Google, [OSDI 04] A simple programming model Functional model For large-scale
More informationCS November 2017
Bigtable Highly available distributed storage Distributed Systems 18. Bigtable Built with semi-structured data in mind URLs: content, metadata, links, anchors, page rank User data: preferences, account
More informationToday CSCI Coda. Naming: Volumes. Coda GFS PAST. Instructor: Abhishek Chandra. Main Goals: Volume is a subtree in the naming space
Today CSCI 5105 Coda GFS PAST Instructor: Abhishek Chandra 2 Coda Main Goals: Availability: Work in the presence of disconnection Scalability: Support large number of users Successor of Andrew File System
More informationHadoop An Overview. - Socrates CCDH
Hadoop An Overview - Socrates CCDH What is Big Data? Volume Not Gigabyte. Terabyte, Petabyte, Exabyte, Zettabyte - Due to handheld gadgets,and HD format images and videos - In total data, 90% of them collected
More informationRecap. CSE 486/586 Distributed Systems Google Chubby Lock Service. Paxos Phase 2. Paxos Phase 1. Google Chubby. Paxos Phase 3 C 1
Recap CSE 486/586 Distributed Systems Google Chubby Lock Service Steve Ko Computer Sciences and Engineering University at Buffalo Paxos is a consensus algorithm. Proposers? Acceptors? Learners? A proposer
More informationAuthors : Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung Presentation by: Vijay Kumar Chalasani
The Authors : Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung Presentation by: Vijay Kumar Chalasani CS5204 Operating Systems 1 Introduction GFS is a scalable distributed file system for large data intensive
More informationCloud Computing and Hadoop Distributed File System. UCSB CS170, Spring 2018
Cloud Computing and Hadoop Distributed File System UCSB CS70, Spring 08 Cluster Computing Motivations Large-scale data processing on clusters Scan 000 TB on node @ 00 MB/s = days Scan on 000-node cluster
More informationGoogle big data techniques (2)
Google big data techniques (2) Lecturer: Jiaheng Lu Fall 2016 10.12.2016 1 Outline Google File System and HDFS Relational DB V.S. Big data system Google Bigtable and NoSQL databases 2016/12/10 3 The Google
More informationGoogle Data Management
Google Data Management Vera Goebel Department of Informatics, University of Oslo 2009 Google Technology Kaizan: continuous developments and improvements Grid computing: Google data centers and messages
More informationCluster-Level Google How we use Colossus to improve storage efficiency
Cluster-Level Storage @ Google How we use Colossus to improve storage efficiency Denis Serenyi Senior Staff Software Engineer dserenyi@google.com November 13, 2017 Keynote at the 2nd Joint International
More informationGFS-python: A Simplified GFS Implementation in Python
GFS-python: A Simplified GFS Implementation in Python Andy Strohman ABSTRACT GFS-python is distributed network filesystem written entirely in python. There are no dependencies other than Python s standard
More informationHadoop File System S L I D E S M O D I F I E D F R O M P R E S E N T A T I O N B Y B. R A M A M U R T H Y 11/15/2017
Hadoop File System 1 S L I D E S M O D I F I E D F R O M P R E S E N T A T I O N B Y B. R A M A M U R T H Y Moving Computation is Cheaper than Moving Data Motivation: Big Data! What is BigData? - Google
More informationLecture 11 Hadoop & Spark
Lecture 11 Hadoop & Spark Dr. Wilson Rivera ICOM 6025: High Performance Computing Electrical and Computer Engineering Department University of Puerto Rico Outline Distributed File Systems Hadoop Ecosystem
More informationGoogle File System and BigTable. and tiny bits of HDFS (Hadoop File System) and Chubby. Not in textbook; additional information
Subject 10 Fall 2015 Google File System and BigTable and tiny bits of HDFS (Hadoop File System) and Chubby Not in textbook; additional information Disclaimer: These abbreviated notes DO NOT substitute
More informationA BigData Tour HDFS, Ceph and MapReduce
A BigData Tour HDFS, Ceph and MapReduce These slides are possible thanks to these sources Jonathan Drusi - SCInet Toronto Hadoop Tutorial, Amir Payberah - Course in Data Intensive Computing SICS; Yahoo!
More information7/22/2008. Transformations
Bandwidth Consumed by s Global Websites Bandwidth Consumed by What is? 7 Countries More than 76 million active customer accounts Approximately 1.3 million active seller accounts Hundreds of thousand of
More informationCS November 2018
Bigtable Highly available distributed storage Distributed Systems 19. Bigtable Built with semi-structured data in mind URLs: content, metadata, links, anchors, page rank User data: preferences, account
More informationIntroduction to Map Reduce
Introduction to Map Reduce 1 Map Reduce: Motivation We realized that most of our computations involved applying a map operation to each logical record in our input in order to compute a set of intermediate
More informationA Study of Comparatively Analysis for HDFS and Google File System towards to Handle Big Data
A Study of Comparatively Analysis for HDFS and Google File System towards to Handle Big Data Rajesh R Savaliya 1, Dr. Akash Saxena 2 1Research Scholor, Rai University, Vill. Saroda, Tal. Dholka Dist. Ahmedabad,
More informationCSE 544 Principles of Database Management Systems. Magdalena Balazinska Winter 2009 Lecture 12 Google Bigtable
CSE 544 Principles of Database Management Systems Magdalena Balazinska Winter 2009 Lecture 12 Google Bigtable References Bigtable: A Distributed Storage System for Structured Data. Fay Chang et. al. OSDI
More informationBigtable. A Distributed Storage System for Structured Data. Presenter: Yunming Zhang Conglong Li. Saturday, September 21, 13
Bigtable A Distributed Storage System for Structured Data Presenter: Yunming Zhang Conglong Li References SOCC 2010 Key Note Slides Jeff Dean Google Introduction to Distributed Computing, Winter 2008 University
More informationCS 138: Google. CS 138 XVII 1 Copyright 2016 Thomas W. Doeppner. All rights reserved.
CS 138: Google CS 138 XVII 1 Copyright 2016 Thomas W. Doeppner. All rights reserved. Google Environment Lots (tens of thousands) of computers all more-or-less equal - processor, disk, memory, network interface
More informationDremel: Interactive Analysis of Web- Scale Datasets
Dremel: Interactive Analysis of Web- Scale Datasets S. Melnik, A. Gubarev, J. Long, G. Romer, S. Shivakumar, M. Tolton Google Inc. VLDB 200 Presented by Ke Hong (slide adapted from Melnik s) Outline Problem
More informationTP1-2: Analyzing Hadoop Logs
TP1-2: Analyzing Hadoop Logs Shadi Ibrahim January 26th, 2017 MapReduce has emerged as a leading programming model for data-intensive computing. It was originally proposed by Google to simplify development
More informationCISC 7610 Lecture 2b The beginnings of NoSQL
CISC 7610 Lecture 2b The beginnings of NoSQL Topics: Big Data Google s infrastructure Hadoop: open google infrastructure Scaling through sharding CAP theorem Amazon s Dynamo 5 V s of big data Everyone
More informationIntroduction to MapReduce. Adapted from Jimmy Lin (U. Maryland, USA)
Introduction to MapReduce Adapted from Jimmy Lin (U. Maryland, USA) Motivation Overview Need for handling big data New programming paradigm Review of functional programming mapreduce uses this abstraction
More informationThe Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google* 정학수, 최주영 1 Outline Introduction Design Overview System Interactions Master Operation Fault Tolerance and Diagnosis Conclusions
More informationEmbedded Technosolutions
Hadoop Big Data An Important technology in IT Sector Hadoop - Big Data Oerie 90% of the worlds data was generated in the last few years. Due to the advent of new technologies, devices, and communication
More informationDremel: Interactive Analysis of Web-Scale Database
Dremel: Interactive Analysis of Web-Scale Database Presented by Jian Fang Most parts of these slides are stolen from here: http://bit.ly/hipzeg What is Dremel Trillion-record, multi-terabyte datasets at
More informationHadoop Distributed File System(HDFS)
Hadoop Distributed File System(HDFS) Bu eğitim sunumları İstanbul Kalkınma Ajansı nın 2016 yılı Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı kapsamında yürütülmekte olan TR10/16/YNY/0036 no lu İstanbul
More informationSystems Infrastructure for Data Science. Web Science Group Uni Freiburg WS 2013/14
Systems Infrastructure for Data Science Web Science Group Uni Freiburg WS 2013/14 MapReduce & Hadoop The new world of Big Data (programming model) Overview of this Lecture Module Background Cluster File
More informationBig Data and Object Storage
Big Data and Object Storage or where to store the cold and small data? Sven Bauernfeind Computacenter AG & Co. ohg, Consultancy Germany 28.02.2018 Munich Volume, Variety & Velocity + Analytics Velocity
More information