# Map-Reduce. Marco Mura 2010 March, 31th

Size: px
Start display at page:

## Transcription

1 Map-Reduce Marco Mura 2010 March, 31th This paper is a note from the course Strumenti di programmazione per sistemi paralleli e distribuiti and it s based by the lessons of Dr. Nicola Tonellotto and his slides. The pictures in this paper are taken from the course slides by Dr. Nicola Tonellotto.

2 Introduction A typical application model we are used to do in computer systems can be represented by a process that takes an input and generates an output. What changes if we have a very large input (Gigabytes and more) to process and a very large output to generate? A single-process application could run for long time before processing all data. The common approach to handle this scenario is the Divide & Conquer model: we split the input in smaller subsets and then we process these portions with as many workers running in parallel which, each one, produces a single portion of output. Merging all the output portions we can build the final output. In theory, this is possible and it works perfectly. But in real, this approach throws lots of problems, like how do we split the input and what if the input splits is greater than the number of workers? how do we aggregate the output? how do we coordinate the work and what if the workers need to share data? And so on Just to decide how to design the software to resolve all those problems you may spend more time than the time to write the application code. And maybe, when you start with a new project, you may restart with all this decisions from scratch! This is certainly a big scale problem. The MapReduce model Yahoo and Google, separately of course, looked for some ideas to manage this big scale problem, reducing its complexity under some hypothesis: - We don t need huge processing power, we don t need to scale up with supercomputers we can handle all that with low commodity infrastructure and with low end computers, just put a bunch of them into a room and you have all the processing power you need. So, don t invest money with huge computers but in a large numbers of low cost machines - We don t move the data. We move the computation where the data is stored! The data is very large (Terabytes) and it s more efficient to move a small application to run where the data is stored instead of moving the data itself. - We don t need random access to the files. Usually, in this kind of application, you read the entire input in sequence and you write the entire output in sequence. So, we don t need standard file systems which are optimized to random file access; we will need special file system optimized to sequence file access. But more than that, it must be distributed to allow the data to be stored along the network. 1

3 - We don t need to know the low level details of this implementation. The framework should expose the right level of abstraction (not too high, not too low). You need to write just the business code of the application, you don t care about the reliability, how to manage failures, replications, how to send the data, etc You just want to specify the input, the output, the code and then run it. So they though up the MapReduce model, summary described with this schema: The INPUT block is the entire input data we want to process and this block is splitted in several partitions l 1, l 2, l n. For instance, the INPUT block could be a document and every portion l n could be a single row of the document which contains a list of few words. The map (key 1,value 1 ) -> [(key 2,value 2 )] 1 takes one of those partition, represented by a (key 1,value 1 ) pair, and returns a list of (key 2,value 2 ) pairs not necessarly related to the (key 1,value 1 ) domain. In other words, the map could change the domain taking a string and returning a list of integers. For instance, map could take the row of a document and, for each word it contains, return a list of (word, 1). Note that in this instance the input value is a single row (the input key could be the row number) and the output value is the number 1 (the output key is a word). The maps output is aggregated by keys: every (k, v 1 ), (k, v 2 ), (k, v n ) is stored in a single (k, [v 1 ;v 2 ;,v n ]). For instance, for five ( pippo, 1) pair it stores ( pippo, *1;1;1;1;1]). 1 In functional programming, the (T 1,T 2 ) value is a pair of two types T 1 and T 2. For instance, the type (string, int) represents the instances like ( blabla, 10). Brackets are used to indicate lists: the [T 1 ] value is a list of T 1 elements. For instance, [int] represents instances like [1;2;3;4;5]. Consequently, the type [(T 1,T 2 )] represents list of pairs; for instance [(string,int)] represents instances like * ( a,1); ( b,2); ( c,3); ( d,4) +. 2

4 Then the reduce (key 2, [values]) -> (key 3, value 3 ) takes a single (key 2, [values]) pair to produce a single (key 3, value 3 ) pair. Usually, but not necessarily, the key 3 is the key 2 itself. For instance, the reduce could take the ( pippo, *1;1;1;1;1]) pair to produce ( pippo, 5). Finally, the OUTPUT block is built merging all the reduce outputs. In this model, programmers need to specify just the map and the reduce function. All the rest is done by the runtime, which handles scheduling, the data distribution, synchronization, errors, faults, etc Actually, the programmer must specify other two functions other than map and reduce ones, but they are not part of the Map-Reduce model: the combine and the partition functions. The combine and the partition functions are useful to optimize the works, avoiding the transmission of useless (replicated) data and to help the load balancing. The combine [(key, value)] -> (key, [values]) is called after the single map, before sending the data to the aggregator, to minimize the number of data sent. It works like a mini-reducer that combines pairs with same key with a single pair with the list of values. For instance, if the single map produces three ( pippo, 1), the combine combines all that pairs in a single ( pippo, *1;1;1]) one. In this instance, I send one key and three values instead of three keys and three values, resulting in a saving of transmission time. The partition (key, num_reducers) -> reducer takes a key and the number of reducers and returns which reducer will handle the specified key. With this function the programmer can balance the load specifying which reducers must process the keys. For instance, if we have two reducers and four keys a, b, c and d, with the size a = 1Tb, b = 10 bytes, c = 10Gb and d = 1Kb, we could prefer to send a and b in the first partition and c and d in the second one, to balance the load as the best we can. The MapReduce term can refer to some different things: - it can refer to the MapReduce model we have just seen, - or it can refer to its execution framework (the runtime), - or it can refers to a specific MapReduce implementation, like MapReduce: an implementation of MapReduce in C++ by Google, used internally and not released to the public. Hadoop is an open source implementation inspired by Google s MapReduce, it s written in Java and it s used (in production) by Yahoo who is the main contributor to the project. 3

### Distributed 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

### Distributed 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

### CA485 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

### GFS: 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

### MapReduce. 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

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

### GFS: 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

### GFS 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,

### ! 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

### ECE 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

### The 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

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

### BigData 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

### CLOUD-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

### Distributed 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

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

### CS435 Introduction to Big Data FALL 2018 Colorado State University. 11/7/2018 Week 12-B Sangmi Lee Pallickara. FAQs

11/7/2018 CS435 Introduction to Big Data - FALL 2018 W12.B.0.0 CS435 Introduction to Big Data 11/7/2018 CS435 Introduction to Big Data - FALL 2018 W12.B.1 FAQs Deadline of the Programming Assignment 3

### Google 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

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

### Distributed 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

### Google 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

### Georgia 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

The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google SOSP 03, October 19 22, 2003, New York, USA Hyeon-Gyu Lee, and Yeong-Jae Woo Memory & Storage Architecture Lab. School

### Distributed 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

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

### Authors : 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

### 18-hdfs-gfs.txt Thu Oct 27 10:05: Notes on Parallel File Systems: HDFS & GFS , Fall 2011 Carnegie Mellon University Randal E.

18-hdfs-gfs.txt Thu Oct 27 10:05:07 2011 1 Notes on Parallel File Systems: HDFS & GFS 15-440, Fall 2011 Carnegie Mellon University Randal E. Bryant References: Ghemawat, Gobioff, Leung, "The Google File

### CS6030 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

### Hadoop 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

### The 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

### NPTEL 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,

### 4/9/2018 Week 13-A Sangmi Lee Pallickara. CS435 Introduction to Big Data Spring 2018 Colorado State University. FAQs. Architecture of GFS

W13.A.0.0 CS435 Introduction to Big Data W13.A.1 FAQs Programming Assignment 3 has been posted PART 2. LARGE SCALE DATA STORAGE SYSTEMS DISTRIBUTED FILE SYSTEMS Recitations Apache Spark tutorial 1 and

### Cloud 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

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

### CSE 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

### Google 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

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

### CSE 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

### 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

### Flat Datacenter Storage. Edmund B. Nightingale, Jeremy Elson, et al. 6.S897

Flat Datacenter Storage Edmund B. Nightingale, Jeremy Elson, et al. 6.S897 Motivation Imagine a world with flat data storage Simple, Centralized, and easy to program Unfortunately, datacenter networks

### 18-hdfs-gfs.txt Thu Nov 01 09:53: Notes on Parallel File Systems: HDFS & GFS , Fall 2012 Carnegie Mellon University Randal E.

18-hdfs-gfs.txt Thu Nov 01 09:53:32 2012 1 Notes on Parallel File Systems: HDFS & GFS 15-440, Fall 2012 Carnegie Mellon University Randal E. Bryant References: Ghemawat, Gobioff, Leung, "The Google File

### goals 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

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

### Google 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

### Yuval Carmel Tel-Aviv University "Advanced Topics in Storage Systems" - Spring 2013

Yuval Carmel Tel-Aviv University "Advanced Topics in About & Keywords Motivation & Purpose Assumptions Architecture overview & Comparison Measurements How does it fit in? The Future 2 About & Keywords

### This material is covered in the textbook in Chapter 21.

This material is covered in the textbook in Chapter 21. The Google File System paper, by S Ghemawat, H Gobioff, and S-T Leung, was published in the proceedings of the ACM Symposium on Operating Systems

### HDFS: 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

### MI-PDB, MIE-PDB: Advanced Database Systems

MI-PDB, MIE-PDB: Advanced Database Systems http://www.ksi.mff.cuni.cz/~svoboda/courses/2015-2-mie-pdb/ Lecture 10: MapReduce, Hadoop 26. 4. 2016 Lecturer: Martin Svoboda svoboda@ksi.mff.cuni.cz Author:

### Abstract. 1. Introduction. 2. Design and Implementation Master Chunkserver

Abstract GFS from Scratch Ge Bian, Niket Agarwal, Wenli Looi https://github.com/looi/cs244b Dec 2017 GFS from Scratch is our partial re-implementation of GFS, the Google File System. Like GFS, our system

### Engineering Goals. Scalability Availability. Transactional behavior Security EAI... CS530 S05

Engineering Goals Scalability Availability Transactional behavior Security EAI... Scalability How much performance can you get by adding hardware (\$)? Performance perfect acceptable unacceptable Processors

### CS 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

### 9/26/2017 Sangmi Lee Pallickara Week 6- A. CS535 Big Data Fall 2017 Colorado State University

CS535 Big Data - Fall 2017 Week 6-A-1 CS535 BIG DATA FAQs PA1: Use only one word query Deadends {{Dead end}} Hub value will be?? PART 1. BATCH COMPUTING MODEL FOR BIG DATA ANALYTICS 4. GOOGLE FILE SYSTEM

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

### FLAT DATACENTER STORAGE. Paper-3 Presenter-Pratik Bhatt fx6568

FLAT DATACENTER STORAGE Paper-3 Presenter-Pratik Bhatt fx6568 FDS Main discussion points A cluster storage system Stores giant "blobs" - 128-bit ID, multi-megabyte content Clients and servers connected

### Google 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)

### Introduction 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

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

The Google File System Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung ACM SIGOPS 2003 {Google Research} Vaibhav Bajpai NDS Seminar 2011 Looking Back time Classics Sun NFS (1985) CMU Andrew FS (1988) Fault

### NPTEL Course Jan K. Gopinath Indian Institute of Science

Storage Systems NPTEL Course Jan 2012 (Lecture 40) K. Gopinath Indian Institute of Science Google File System Non-Posix scalable distr file system for large distr dataintensive applications performance,

### Map Reduce. Yerevan.

Map Reduce Erasmus+ @ Yerevan dacosta@irit.fr Divide and conquer at PaaS 100 % // Typical problem Iterate over a large number of records Extract something of interest from each Shuffle and sort intermediate

### Parallel 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

### TITLE: PRE-REQUISITE THEORY. 1. Introduction to Hadoop. 2. Cluster. Implement sort algorithm and run it using HADOOP

TITLE: Implement sort algorithm and run it using HADOOP PRE-REQUISITE Preliminary knowledge of clusters and overview of Hadoop and its basic functionality. THEORY 1. Introduction to Hadoop The Apache Hadoop

### Outline. 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)

### 2/27/2019 Week 6-B Sangmi Lee Pallickara

2/27/2019 - Spring 2019 Week 6-B-1 CS535 BIG DATA FAQs Participation scores will be collected separately Sign-up page is up PART A. BIG DATA TECHNOLOGY 5. SCALABLE DISTRIBUTED FILE SYSTEMS: GOOGLE FILE

### Staggeringly 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

### ECE 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: Software Infrastructure in Data Centers: Distributed File Systems 1 Permanently stores data Filesystems

### Distributed Systems. GFS / HDFS / Spanner

15-440 Distributed Systems GFS / HDFS / Spanner Agenda Google File System (GFS) Hadoop Distributed File System (HDFS) Distributed File Systems Replication Spanner Distributed Database System Paxos Replication

### Bigtable: 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

### BigTable: 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,

### CS 537: Introduction to Operating Systems Fall 2015: Midterm Exam #4 Tuesday, December 15 th 11:00 12:15. Advanced Topics: Distributed File Systems

CS 537: Introduction to Operating Systems Fall 2015: Midterm Exam #4 Tuesday, December 15 th 11:00 12:15 Advanced Topics: Distributed File Systems SOLUTIONS This exam is closed book, closed notes. All

### GOOGLE FILE SYSTEM: MASTER Sanjay Ghemawat, Howard Gobioff and Shun-Tak Leung

ECE7650 Scalable and Secure Internet Services and Architecture ---- A Systems Perspective (Winter 2015) Presentation Report GOOGLE FILE SYSTEM: MASTER Sanjay Ghemawat, Howard Gobioff and Shun-Tak Leung

### ΕΠΛ 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

### Distributed Computation Models

Distributed Computation Models SWE 622, Spring 2017 Distributed Software Engineering Some slides ack: Jeff Dean HW4 Recap https://b.socrative.com/ Class: SWE622 2 Review Replicating state machines Case

### CS427 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

### Distributed 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

### GFS. CS6450: Distributed Systems Lecture 5. Ryan Stutsman

GFS CS6450: Distributed Systems Lecture 5 Ryan Stutsman Some material taken/derived from Princeton COS-418 materials created by Michael Freedman and Kyle Jamieson at Princeton University. Licensed for

### UNIT-IV HDFS. Ms. Selva Mary. G

UNIT-IV HDFS HDFS ARCHITECTURE Dataset partition across a number of separate machines Hadoop Distributed File system The Design of HDFS HDFS is a file system designed for storing very large files with

### CSE 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)

### Clustering Lecture 8: MapReduce

Clustering Lecture 8: MapReduce Jing Gao SUNY Buffalo 1 Divide and Conquer Work Partition w 1 w 2 w 3 worker worker worker r 1 r 2 r 3 Result Combine 4 Distributed Grep Very big data Split data Split data

### 11/5/2018 Week 12-A Sangmi Lee Pallickara. CS435 Introduction to Big Data FALL 2018 Colorado State University

11/5/2018 CS435 Introduction to Big Data - FALL 2018 W12.A.0.0 CS435 Introduction to Big Data 11/5/2018 CS435 Introduction to Big Data - FALL 2018 W12.A.1 Consider a Graduate Degree in Computer Science

### CS555: Distributed Systems [Fall 2017] Dept. Of Computer Science, Colorado State University

CS 555: DISTRIBUTED SYSTEMS [DYNAMO & GOOGLE FILE SYSTEM] Frequently asked questions from the previous class survey What s the typical size of an inconsistency window in most production settings? Dynamo?

### Where We Are. Review: Parallel DBMS. Parallel DBMS. Introduction to Data Management CSE 344

Where We Are Introduction to Data Management CSE 344 Lecture 22: MapReduce We are talking about parallel query processing There exist two main types of engines: Parallel DBMSs (last lecture + quick review)

### References. 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

### Programming Systems for Big Data

Programming Systems for Big Data CS315B Lecture 17 Including material from Kunle Olukotun Prof. Aiken CS 315B Lecture 17 1 Big Data We ve focused on parallel programming for computational science There

### Google 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

### Distributed File Systems. Directory Hierarchy. Transfer Model

Distributed File Systems Ken Birman Goal: view a distributed system as a file system Storage is distributed Web tries to make world a collection of hyperlinked documents Issues not common to usual file

### HDFS: Hadoop Distributed File System. CIS 612 Sunnie Chung

HDFS: Hadoop Distributed File System CIS 612 Sunnie Chung What is Big Data?? Bulk Amount Unstructured Introduction Lots of Applications which need to handle huge amount of data (in terms of 500+ TB per

### FLAT DATACENTER STORAGE CHANDNI MODI (FN8692)

FLAT DATACENTER STORAGE CHANDNI MODI (FN8692) OUTLINE Flat datacenter storage Deterministic data placement in fds Metadata properties of fds Per-blob metadata in fds Dynamic Work Allocation in fds Replication

### Dept. Of Computer Science, Colorado State University

CS 455: INTRODUCTION TO DISTRIBUTED SYSTEMS [HADOOP/HDFS] Trying to have your cake and eat it too Each phase pines for tasks with locality and their numbers on a tether Alas within a phase, you get one,

### Distributed File Systems

Distributed File Systems Today l Basic distributed file systems l Two classical examples Next time l Naming things xkdc Distributed File Systems " A DFS supports network-wide sharing of files and devices

### A Distributed Namespace for a Distributed File System

A Distributed Namespace for a Distributed File System Wasif Riaz Malik wasif@kth.se Master of Science Thesis Examiner: Dr. Jim Dowling, KTH/SICS Berlin, Aug 7, 2012 TRITA-ICT-EX-2012:173 Abstract Due

### Distributed 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

### The Google File System GFS

The Google File System GFS Common Goals of GFS and most Distributed File Systems Performance Reliability Scalability Availability Other GFS Concepts Component failures are the norm rather than the exception.

### Lecture 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