Big Data 7. Resource Management
|
|
- Job Reeves
- 5 years ago
- Views:
Transcription
1 Ghislain Fourny Big Data 7. Resource Management artjazz / 123RF Stock Photo
2 Data Technology Stack User interfaces Querying Data stores Indexing Processing Validation Data models Syntax Encoding Storage 2
3 Where we are User interfaces Querying Data stores Indexing Processing Validation Data models Syntax Encoding Storage 3
4 Last week: MapReduce Input data Map Map Map Map Map Map Map Map Intermediate data (shuffled) Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce Output data 4
5 Hadoop infrastructure (version 1) Namenode /dir/file Datanode Datanode Datanode Datanode Datanode Datanode 5
6 Hadoop infrastructure (version 1) Namenode + JobTracker /dir/file Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 6
7 Responsibilities of the MapReduce JobTracker Resource Management 7
8 Responsibilities of the MapReduce JobTracker Resource Management Scheduling 8
9 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring 9
10 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring Job lifecycle 10
11 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring Job lifecycle Fault-tolerance 11
12 Issue 1: scalability M M M M M M M M M M M M < 4,000 nodes < 40,000 tasks 12
13 Issue 2: bottleneck JobTracker Bottleneck TaskTracker TaskTracker TaskTracker TaskTracker TaskTracker TaskTracker 13 13
14 Issue 3: Jack of all trades Scheduling Monitoring 14 14
15 Issue 4: Utilization (task slots) Static (Decide on M/R at configuration time) Fixed-size 15 15
16 Issue 5: Not fungible Map Reduce 16 16
17 Issue 5: Not fungible Working at maximum capacity Idle Map Reduce 17 17
18 kirtchanut / 123RF Stock Photo YARN 18
19 YARN Yet Another Resource Negotiator 19
20 YARN Scheduling Application Monitoring management Resource Manager Application Master Application Master Application Master Application Master Application Master 20
21 Scales more M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 10,000 nodes 100,000 tasks 21
22 YARN architecture ResourceManager 22
23 YARN architecture ResourceManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager 23
24 YARN architecture ResourceManager Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager 24
25 Remember... It does ring a bell, doesn't it? 25
26 Master-slave architecture Master Slave Slave Slave Slave Slave Slave 26
27 HDFS server architecture Namenode /dir/file1 /dir/file2 /file3 Datanode Datanode Datanode Datanode Datanode Datanode 27
28 YARN ResourceManager Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 28
29 YARN Client ResourceManager Job Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 29
30 YARN: RM allocates an Application Master Client ResourceManager Job Schedules Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 30
31 YARN: RM allocates an Application Master Client ResourceManager Job Schedules Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 31
32 YARN: RM allocates an Application Master Client ResourceManager Job Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 32
33 YARN: RM allocates an Application Master Client ResourceManager Job Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 33
34 Application Master communicates with containers Application Master Container Container Container Execute Monitor Container 34
35 kirtchanut / 123RF Stock Photo YARN's Resource Manager 35
36 Resource Manager Capacity guarantees Cluster Utilization Fairness SLAs 36
37 Communication with clients 37
38 Communication with clients Client Service Application (start, end) Queue information Statistics 38
39 Communication with clients Client Service Application (start, end) Queue information Statistics Admin Service Refresh the node list Queue configuration 39
40 Communication with the node managers 40
41 Communication with the node managers Resource Tracker 41
42 Communication with the node managers Resource Tracker Liveliness 42
43 Communication with the node managers Resource Tracker Liveliness Nodes List Manager valid invalid 43
44 Communication with the application masters 44
45 Communication with the application masters Application Master Service (registration) 45
46 Communication with the application masters Application Master Service (registration) Liveliness 46
47 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) 47
48 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) Applications Manager 48
49 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) Applications Manager + Launcher 49
50 Authentication 50
51 Authentication Application Token 51
52 Authentication Application Token Container Token 52
53 Authentication Application Token Application ACL Container Token 53
54 Pure scheduler Does not monitor tasks. Does not restart upon failure. 54
55 Scheduling strategies: pluggable scheduler 55
56 Scheduling strategies: pluggable scheduler FIFO scheduler 56
57 Scheduling strategies: pluggable scheduler FIFO scheduler 57
58 Scheduling strategies: pluggable scheduler FIFO scheduler 58
59 Scheduling strategies: pluggable scheduler FIFO scheduler 59
60 Scheduling strategies: pluggable scheduler FIFO scheduler 60
61 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 61
62 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 62
63 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 63
64 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 64
65 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 65
66 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 66
67 Hierarchical queues Root 67
68 Hierarchical queues Root Math 4 Physics 1 CS 5 68
69 Hierarchical queues Root Math 4 Physics 1 CS 5 40% 10% 50% 69
70 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra TI DB
71 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra TI DB % 10% 10% 32% 40% 71
72 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra Best effort DB % 10% 0% 32% 50% 72
73 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra Best effort DB % 10% 50% 32% 0% 73
74 Scheduling strategies: pluggable scheduler Fair scheduler 74
75 Scheduling strategies: pluggable scheduler Fair scheduler 75
76 Scheduling strategies: pluggable scheduler Fair scheduler 76
77 Scheduling strategies: pluggable scheduler Fair scheduler 77
78 Scheduling strategies: pluggable scheduler Fair scheduler 78
79 Scheduling strategies: pluggable scheduler Fair scheduler 79
80 Scheduling strategies: pluggable scheduler Fair scheduler 80
81 Scheduling strategies: pluggable scheduler Fair scheduler 81
82 Fine grained resource requests Memory Application A: 10 GB Application A: 30 GB 82
83 Fine grained resource requests Memory Application A: 10 GB Application A: 30 GB 25% 75% 83
84 Fine grained resource requests Memory CPU 84
85 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) 85
86 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 86
87 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 30% Memory, 4% CPU 1% Memory, 50% CPU 87
88 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 30% Memory, 4% CPU 1% Memory, 50% CPU 37.5% 62.5% 88
89 Fine grained resource requests Memory CPU Disk Network 89
90 Fine grained resource requests Memory CPU Work in progress Disk Network 90
91 Resource container X GB W cores, U GHz Y TB Z MBps 91
92 kirtchanut / 123RF Stock Photo YARN's Node Manager 92
93 NodeManager: one per node NodeManager NodeManager NodeManager NodeManager 93
94 Monitoring Memory CPU Disk Network 94
95 Reports to ResourceManager Memory CPU ResourceManager Disk Network 95
96 Container 96
97 kirtchanut / 123RF Stock Photo YARN's Application Masters 97
98 Application Master Application Master is per application. 98
99 Application Master Application Master is application-specific. 99
100 Framework-specific application masters MapReduce DAG distributed processing Message Passing Interface Graph processing 100
101 Complexity is moved to the Application Master complexity 101
102 Application Master ResourceManager negotiates resources 102
103 Application Master ResourceManager negotiates resources executes and monitors NodeManager 103
104 Fault tolerance is on the application master 104
105 Fault tolerance is on the application master 105
106 Fault tolerance is on the application master relaunch 106
107 Application-specific monitoring no longer a bottleneck 107
108 Application Master is not trusted 108
109 Application Master is not trusted Evil plan to book containers and not use them 109
110 Summary Separation between scheduling and monitoring 110
111 Summary Separation between scheduling and monitoring Scalability 111
112 Summary Separation between scheduling and monitoring Scalability Availability 112
113 Summary Separation between scheduling and monitoring Scalability Availability Multi-tenancy 113
114 Forward compatibility with DAGs of tasks 114
Big Data for Engineers Spring Resource Management
Ghislain Fourny Big Data for Engineers Spring 2018 7. Resource Management artjazz / 123RF Stock Photo Data Technology Stack User interfaces Querying Data stores Indexing Processing Validation Data models
More informationCCA-410. Cloudera. Cloudera Certified Administrator for Apache Hadoop (CCAH)
Cloudera CCA-410 Cloudera Certified Administrator for Apache Hadoop (CCAH) Download Full Version : http://killexams.com/pass4sure/exam-detail/cca-410 Reference: CONFIGURATION PARAMETERS DFS.BLOCK.SIZE
More informationBig Data Programming: an Introduction. Spring 2015, X. Zhang Fordham Univ.
Big Data Programming: an Introduction Spring 2015, X. Zhang Fordham Univ. Outline What the course is about? scope Introduction to big data programming Opportunity and challenge of big data Origin of Hadoop
More informationDatabase Applications (15-415)
Database Applications (15-415) Hadoop Lecture 24, April 23, 2014 Mohammad Hammoud Today Last Session: NoSQL databases Today s Session: Hadoop = HDFS + MapReduce Announcements: Final Exam is on Sunday April
More informationL5-6:Runtime Platforms Hadoop and HDFS
Indian Institute of Science Bangalore, India भ रत य व ज ञ न स स थ न ब गल र, भ रत Department of Computational and Data Sciences SE256:Jan16 (2:1) L5-6:Runtime Platforms Hadoop and HDFS Yogesh Simmhan 03/
More informationVendor: Cloudera. Exam Code: CCA-505. Exam Name: Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam.
Vendor: Cloudera Exam Code: CCA-505 Exam Name: Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam Version: Demo QUESTION 1 You have installed a cluster running HDFS and MapReduce
More informationMI-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:
More informationGhislain Fourny. Big Data 6. Massive Parallel Processing (MapReduce)
Ghislain Fourny Big Data 6. Massive Parallel Processing (MapReduce) So far, we have... Storage as file system (HDFS) 13 So far, we have... Storage as tables (HBase) Storage as file system (HDFS) 14 Data
More informationGhislain Fourny. Big Data Fall Massive Parallel Processing (MapReduce)
Ghislain Fourny Big Data Fall 2018 6. Massive Parallel Processing (MapReduce) Let's begin with a field experiment 2 400+ Pokemons, 10 different 3 How many of each??????????? 4 400 distributed to many volunteers
More informationIntroduction To YARN. Adam Kawa, Spotify The 9 Meeting of Warsaw Hadoop User Group 2/23/13
Introduction To YARN Adam Kawa, Spotify th The 9 Meeting of Warsaw Hadoop User Group About Me Data Engineer at Spotify, Sweden Hadoop Instructor at Compendium (Cloudera Training Partner) +2.5 year of experience
More informationA Glimpse of the Hadoop Echosystem
A Glimpse of the Hadoop Echosystem 1 Hadoop Echosystem A cluster is shared among several users in an organization Different services HDFS and MapReduce provide the lower layers of the infrastructures Other
More informationCCA Administrator Exam (CCA131)
CCA Administrator Exam (CCA131) Cloudera CCA-500 Dumps Available Here at: /cloudera-exam/cca-500-dumps.html Enrolling now you will get access to 60 questions in a unique set of CCA- 500 dumps Question
More informationFacilitating Consistency Check between Specification & Implementation with MapReduce Framework
Facilitating Consistency Check between Specification & Implementation with MapReduce Framework Shigeru KUSAKABE, Yoichi OMORI, Keijiro ARAKI Kyushu University, Japan 2 Our expectation Light-weight formal
More informationCS 378 Big Data Programming
CS 378 Big Data Programming Lecture 5 Summariza9on Pa:erns CS 378 Fall 2017 Big Data Programming 1 Review Assignment 2 Ques9ons? mrunit How do you test map() or reduce() calls that produce mul9ple outputs?
More informationHadoop MapReduce Framework
Hadoop MapReduce Framework Contents Hadoop MapReduce Framework Architecture Interaction Diagram of MapReduce Framework (Hadoop 1.0) Interaction Diagram of MapReduce Framework (Hadoop 2.0) Hadoop MapReduce
More informationPaaS and Hadoop. Dr. Laiping Zhao ( 赵来平 ) School of Computer Software, Tianjin University
PaaS and Hadoop Dr. Laiping Zhao ( 赵来平 ) School of Computer Software, Tianjin University laiping@tju.edu.cn 1 Outline PaaS Hadoop: HDFS and Mapreduce YARN Single-Processor Scheduling Hadoop Scheduling
More informationConfiguring Ports for Big Data Management, Data Integration Hub, Enterprise Information Catalog, and Intelligent Data Lake 10.2
Configuring s for Big Data Management, Data Integration Hub, Enterprise Information Catalog, and Intelligent Data Lake 10.2 Copyright Informatica LLC 2016, 2017. Informatica, the Informatica logo, Big
More informationExam Questions CCA-500
Exam Questions CCA-500 Cloudera Certified Administrator for Apache Hadoop (CCAH) https://www.2passeasy.com/dumps/cca-500/ Question No : 1 Your cluster s mapred-start.xml includes the following parameters
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 informationDistributed Systems CS6421
Distributed Systems CS6421 Intro to Distributed Systems and the Cloud Prof. Tim Wood v I teach: Software Engineering, Operating Systems, Sr. Design I like: distributed systems, networks, building cool
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 information50 Must Read Hadoop Interview Questions & Answers
50 Must Read Hadoop Interview Questions & Answers Whizlabs Dec 29th, 2017 Big Data Are you planning to land a job with big data and data analytics? Are you worried about cracking the Hadoop job interview?
More informationCloudera Exam CCA-410 Cloudera Certified Administrator for Apache Hadoop (CCAH) Version: 7.5 [ Total Questions: 97 ]
s@lm@n Cloudera Exam CCA-410 Cloudera Certified Administrator for Apache Hadoop (CCAH) Version: 7.5 [ Total Questions: 97 ] Question No : 1 Which two updates occur when a client application opens a stream
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 informationHADOOP 3.0 is here! Dr. Sandeep Deshmukh Sadepach Labs Pvt. Ltd. - Let us grow together!
HADOOP 3.0 is here! Dr. Sandeep Deshmukh sandeep@sadepach.com Sadepach Labs Pvt. Ltd. - Let us grow together! About me BE from VNIT Nagpur, MTech+PhD from IIT Bombay Worked with Persistent Systems - Life
More informationHADOOP FRAMEWORK FOR BIG DATA
HADOOP FRAMEWORK FOR BIG DATA Mr K. Srinivas Babu 1,Dr K. Rameshwaraiah 2 1 Research Scholar S V University, Tirupathi 2 Professor and Head NNRESGI, Hyderabad Abstract - Data has to be stored for further
More informationMixing and matching virtual and physical HPC clusters. Paolo Anedda
Mixing and matching virtual and physical HPC clusters Paolo Anedda paolo.anedda@crs4.it HPC 2010 - Cetraro 22/06/2010 1 Outline Introduction Scalability Issues System architecture Conclusions & Future
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 informationdocs.hortonworks.com
docs.hortonworks.com : Getting Started Guide Copyright 2012, 2014 Hortonworks, Inc. Some rights reserved. The, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing,
More informationMap Reduce & Hadoop Recommended Text:
Map Reduce & Hadoop Recommended Text: Hadoop: The Definitive Guide Tom White O Reilly 2010 VMware Inc. All rights reserved Big Data! Large datasets are becoming more common The New York Stock Exchange
More informationA Multilevel Secure MapReduce Framework for Cross-Domain Information Sharing in the Cloud
Calhoun: The NPS Institutional Archive Faculty and Researcher Publications Faculty and Researcher Publications 2013-03 A Multilevel Secure MapReduce Framework for Cross-Domain Information Sharing in the
More informationHadoop/MapReduce Computing Paradigm
Hadoop/Reduce Computing Paradigm 1 Large-Scale Data Analytics Reduce computing paradigm (E.g., Hadoop) vs. Traditional database systems vs. Database Many enterprises are turning to Hadoop Especially applications
More informationYARN: A Resource Manager for Analytic Platform Tsuyoshi Ozawa
YARN: A Resource Manager for Analytic Platform Tsuyoshi Ozawa ozawa.tsuyoshi@lab.ntt.co.jp ozawa@apache.org About me Tsuyoshi Ozawa Research Engineer @ NTT Twitter: @oza_x86_64 Over 150 reviews in 2015
More informationAn Enhanced Approach for Resource Management Optimization in Hadoop
An Enhanced Approach for Resource Management Optimization in Hadoop R. Sandeep Raj 1, G. Prabhakar Raju 2 1 MTech Student, Department of CSE, Anurag Group of Institutions, India 2 Associate Professor,
More informationCloud Computing CS
Cloud Computing CS 15-319 Programming Models- Part III Lecture 6, Feb 1, 2012 Majd F. Sakr and Mohammad Hammoud 1 Today Last session Programming Models- Part II Today s session Programming Models Part
More informationHadoop Map Reduce 10/17/2018 1
Hadoop Map Reduce 10/17/2018 1 MapReduce 2-in-1 A programming paradigm A query execution engine A kind of functional programming We focus on the MapReduce execution engine of Hadoop through YARN 10/17/2018
More information2/26/2017. For instance, consider running Word Count across 20 splits
Based on the slides of prof. Pietro Michiardi Hadoop Internals https://github.com/michiard/disc-cloud-course/raw/master/hadoop/hadoop.pdf Job: execution of a MapReduce application across a data set Task:
More information3. Monitoring Scenarios
3. Monitoring Scenarios This section describes the following: Navigation Alerts Interval Rules Navigation Ambari SCOM Use the Ambari SCOM main navigation tree to browse cluster, HDFS and MapReduce performance
More informationVendor: Cloudera. Exam Code: CCD-410. Exam Name: Cloudera Certified Developer for Apache Hadoop. Version: Demo
Vendor: Cloudera Exam Code: CCD-410 Exam Name: Cloudera Certified Developer for Apache Hadoop Version: Demo QUESTION 1 When is the earliest point at which the reduce method of a given Reducer can be called?
More informationProgramming Models MapReduce
Programming Models MapReduce Majd Sakr, Garth Gibson, Greg Ganger, Raja Sambasivan 15-719/18-847b Advanced Cloud Computing Fall 2013 Sep 23, 2013 1 MapReduce In a Nutshell MapReduce incorporates two phases
More informationVolume 3, Issue 9, September 2015 ISSN Hadoop2 Yarn. Asso.Prof. Ashish Sharma #1 Snehlata Vyas *2
Hadoop2 Yarn Asso.Prof. Ashish Sharma #1 Snehlata Vyas *2 # Computer Science Jodhpur National University,Jodhpur, Rajasthan,India # ComputerScience,Mahila PG Mahavidhyalaya,JNVU Jodhpur, Rajasthan,India
More informationInternational Journal of Advance Engineering and Research Development. A Study: Hadoop Framework
Scientific Journal of Impact Factor (SJIF): e-issn (O): 2348- International Journal of Advance Engineering and Research Development Volume 3, Issue 2, February -2016 A Study: Hadoop Framework Devateja
More informationChapter 5. The MapReduce Programming Model and Implementation
Chapter 5. The MapReduce Programming Model and Implementation - Traditional computing: data-to-computing (send data to computing) * Data stored in separate repository * Data brought into system for computing
More informationDistributed Face Recognition Using Hadoop
Distributed Face Recognition Using Hadoop A. Thorat, V. Malhotra, S. Narvekar and A. Joshi Dept. of Computer Engineering and IT College of Engineering, Pune {abhishekthorat02@gmail.com, vinayak.malhotra20@gmail.com,
More informationCS370 Operating Systems
CS370 Operating Systems Colorado State University Yashwant K Malaiya Fall 2017 Lecture 26 File Systems Slides based on Text by Silberschatz, Galvin, Gagne Various sources 1 1 FAQ Cylinders: all the platters?
More informationApril Final Quiz COSC MapReduce Programming a) Explain briefly the main ideas and components of the MapReduce programming model.
1. MapReduce Programming a) Explain briefly the main ideas and components of the MapReduce programming model. MapReduce is a framework for processing big data which processes data in two phases, a Map
More informationHadoop Integration User Guide. Functional Area: Hadoop Integration. Geneos Release: v4.9. Document Version: v1.0.0
Hadoop Integration User Guide Functional Area: Hadoop Integration Geneos Release: v4.9 Document Version: v1.0.0 Date Published: 25 October 2018 Copyright 2018. ITRS Group Ltd. All rights reserved. Information
More informationPerformance evaluation of job schedulers on Hadoop YARN
Performance evaluation of job schedulers on Hadoop YARN Jia-Chun Lin Department of Informatics, University of Oslo Gaustadallèen 23 B, Oslo, N-0373, Norway kellylin@ifi.uio.no Ming-Chang Lee Department
More informationActual4Dumps. Provide you with the latest actual exam dumps, and help you succeed
Actual4Dumps http://www.actual4dumps.com Provide you with the latest actual exam dumps, and help you succeed Exam : HDPCD Title : Hortonworks Data Platform Certified Developer Vendor : Hortonworks Version
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 informationAutomation of Rolling Upgrade for Hadoop Cluster without Data Loss and Job Failures. Hiroshi Yamaguchi & Hiroyuki Adachi
Automation of Rolling Upgrade for Hadoop Cluster without Data Loss and Job Failures Hiroshi Yamaguchi & Hiroyuki Adachi About Us 2 Hiroshi Yamaguchi Hiroyuki Adachi Hadoop DevOps Engineer Hadoop Engineer
More informationAutomatic Voting Machine using Hadoop
GRD Journals- Global Research and Development Journal for Engineering Volume 2 Issue 7 June 2017 ISSN: 2455-5703 Ms. Shireen Fatima Mr. Shivam Shukla M. Tech Student Assistant Professor Department of Computer
More informationTITLE: 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
More informationMAXIMIZING PARALLELIZATION OPPORTUNITIES BY AUTOMATICALLY INFERRING OPTIMAL CONTAINER MEMORY FOR ASYMMETRICAL MAP TASKS. Shubhendra Shrimal.
MAXIMIZING PARALLELIZATION OPPORTUNITIES BY AUTOMATICALLY INFERRING OPTIMAL CONTAINER MEMORY FOR ASYMMETRICAL MAP TASKS Shubhendra Shrimal A Thesis Submitted to the Graduate College of Bowling Green State
More informationDistributed Systems. CS422/522 Lecture17 17 November 2014
Distributed Systems CS422/522 Lecture17 17 November 2014 Lecture Outline Introduction Hadoop Chord What s a distributed system? What s a distributed system? A distributed system is a collection of loosely
More informationHadoop. copyright 2011 Trainologic LTD
Hadoop Hadoop is a framework for processing large amounts of data in a distributed manner. It can scale up to thousands of machines. It provides high-availability. Provides map-reduce functionality. Hides
More information1. Introduction (Sam) 2. Syntax and Semantics (Paul) 3. Compiler Architecture (Ben) 4. Runtime Environment (Kurry) 5. Testing (Jason) 6. Demo 7.
Jason Halpern Testing/Validation Samuel Messing Project Manager Benjamin Rapaport System Architect Kurry Tran System Integrator Paul Tylkin Language Guru THE HOG LANGUAGE A scripting MapReduce language.
More informationNovel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in Heterogeneous Computing Environments
Novel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in Heterogeneous Computing Environments Sun-Yuan Hsieh 1,2,3, Chi-Ting Chen 1, Chi-Hao Chen 1, Tzu-Hsiang Yen 1, Hung-Chang
More informationAeromancer: A Workflow Manager for Large- Scale MapReduce-Based Scientific Workflows
Aeromancer: A Workflow Manager for Large- Scale MapReduce-Based Scientific Workflows Presented by Sarunya Pumma Supervisors: Dr. Wu-chun Feng, Dr. Mark Gardner, and Dr. Hao Wang synergy.cs.vt.edu Outline
More informationDynamic Data Placement Strategy in MapReduce-styled Data Processing Platform Hua-Ci WANG 1,a,*, Cai CHEN 2,b,*, Yi LIANG 3,c
2016 Joint International Conference on Service Science, Management and Engineering (SSME 2016) and International Conference on Information Science and Technology (IST 2016) ISBN: 978-1-60595-379-3 Dynamic
More informationApache Hadoop 3. Balazs Gaspar Sales Engineer CEE & CIS Cloudera, Inc. All rights reserved.
Apache Hadoop 3 Balazs Gaspar Sales Engineer CEE & CIS balazs@cloudera.com 1 We believe data can make what is impossible today, possible tomorrow 2 We empower people to transform complex data into clear
More information7 Deadly Hadoop Misconfigurations. Kathleen Hadoop Talks Meetup, 27 March 2014
7 Deadly Hadoop Misconfigurations Kathleen Ting kathleen@apache.org @kate_ting Hadoop Talks Meetup, 27 March 2014 Who Am I? Started 3 yr ago as 1 st Cloudera Support Eng Now manages Cloudera s 2 largest
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 informationWelcome to. uweseiler
5.03.014 Welcome to uweseiler 5.03.014 Your Travel Guide Big Data Nerd Hadoop Trainer NoSQL Fan Boy Photography Enthusiast Travelpirate 5.03.014 Your Travel Agency specializes on... Big Data Nerds Agile
More informationGetting Started with Hadoop
Getting Started with Hadoop May 28, 2018 Michael Völske, Shahbaz Syed Web Technology & Information Systems Bauhaus-Universität Weimar 1 webis 2018 What is Hadoop Started in 2004 by Yahoo Open-Source implementation
More informationHadoop محبوبه دادخواه کارگاه ساالنه آزمایشگاه فناوری وب زمستان 1391
Hadoop محبوبه دادخواه کارگاه ساالنه آزمایشگاه فناوری وب زمستان 1391 Outline Big Data Big Data Examples Challenges with traditional storage NoSQL Hadoop HDFS MapReduce Architecture 2 Big Data In information
More informationHadoop 2.x Core: YARN, Tez, and Spark. Hortonworks Inc All Rights Reserved
Hadoop 2.x Core: YARN, Tez, and Spark YARN Hadoop Machine Types top-of-rack switches core switch client machines have client-side software used to access a cluster to process data master nodes run Hadoop
More informationLecture 30: Distributed Map-Reduce using Hadoop and Spark Frameworks
COMP 322: Fundamentals of Parallel Programming Lecture 30: Distributed Map-Reduce using Hadoop and Spark Frameworks Mack Joyner and Zoran Budimlić {mjoyner, zoran}@rice.edu http://comp322.rice.edu COMP
More informationYour First Hadoop App, Step by Step
Learn Hadoop in one evening Your First Hadoop App, Step by Step Martynas 1 Miliauskas @mmiliauskas Your First Hadoop App, Step by Step By Martynas Miliauskas Published in 2013 by Martynas Miliauskas On
More information2/4/2019 Week 3- A Sangmi Lee Pallickara
Week 3-A-0 2/4/2019 Colorado State University, Spring 2019 Week 3-A-1 CS535 BIG DATA FAQs PART A. BIG DATA TECHNOLOGY 3. DISTRIBUTED COMPUTING MODELS FOR SCALABLE BATCH COMPUTING SECTION 1: MAPREDUCE PA1
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 informationHuge Data Analysis and Processing Platform based on Hadoop Yuanbin LI1, a, Rong CHEN2
2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017) Huge Data Analysis and Processing Platform based on Hadoop Yuanbin LI1, a, Rong CHEN2 1 Information Engineering
More information1Z Oracle Big Data 2017 Implementation Essentials Exam Summary Syllabus Questions
1Z0-449 Oracle Big Data 2017 Implementation Essentials Exam Summary Syllabus Questions Table of Contents Introduction to 1Z0-449 Exam on Oracle Big Data 2017 Implementation Essentials... 2 Oracle 1Z0-449
More informationHortonworks HDPCD. Hortonworks Data Platform Certified Developer. Download Full Version :
Hortonworks HDPCD Hortonworks Data Platform Certified Developer Download Full Version : https://killexams.com/pass4sure/exam-detail/hdpcd QUESTION: 97 You write MapReduce job to process 100 files in HDFS.
More informationTowards Automatic Optimization of MapReduce Programs (Position Paper) Shivnath Babu Duke University
Towards Automatic Optimization of MapReduce Programs (Position Paper) Shivnath Babu Duke University Roadmap Call to action to improve automatic optimization techniques in MapReduce frameworks Challenges
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 informationClustering 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
More informationThe Hadoop Ecosystem. EECS 4415 Big Data Systems. Tilemachos Pechlivanoglou
The Hadoop Ecosystem EECS 4415 Big Data Systems Tilemachos Pechlivanoglou tipech@eecs.yorku.ca A lot of tools designed to work with Hadoop 2 HDFS, MapReduce Hadoop Distributed File System Core Hadoop component
More informationExam Questions CCA-505
Exam Questions CCA-505 Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam https://www.2passeasy.com/dumps/cca-505/ 1.You want to understand more about how users browse you public
More informationCloud Computing. Hwajung Lee. Key Reference: Prof. Jong-Moon Chung s Lecture Notes at Yonsei University
Cloud Computing Hwajung Lee Key Reference: Prof. Jong-Moon Chung s Lecture Notes at Yonsei University Cloud Computing Cloud Introduction Cloud Service Model Big Data Hadoop MapReduce HDFS (Hadoop Distributed
More informationHadoop On Demand: Configuration Guide
Hadoop On Demand: Configuration Guide Table of contents 1 1. Introduction...2 2 2. Sections... 2 3 3. HOD Configuration Options...2 3.1 3.1 Common configuration options...2 3.2 3.2 hod options... 3 3.3
More informationMixApart: Decoupled Analytics for Shared Storage Systems. Madalin Mihailescu, Gokul Soundararajan, Cristiana Amza University of Toronto and NetApp
MixApart: Decoupled Analytics for Shared Storage Systems Madalin Mihailescu, Gokul Soundararajan, Cristiana Amza University of Toronto and NetApp Hadoop Pig, Hive Hadoop + Enterprise storage?! Shared storage
More informationModeling and Optimization of Resource Allocation in Cloud
PhD Thesis Progress First Report Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman Istanbul Technical University Department of Computer Engineering January 8, 2015 Outline 1 Introduction 2 Studies Time Plan
More informationMapReduce, Hadoop and Spark. Bompotas Agorakis
MapReduce, Hadoop and Spark Bompotas Agorakis Big Data Processing Most of the computations are conceptually straightforward on a single machine but the volume of data is HUGE Need to use many (1.000s)
More informationCertified Big Data and Hadoop Course Curriculum
Certified Big Data and Hadoop Course Curriculum The Certified Big Data and Hadoop course by DataFlair is a perfect blend of in-depth theoretical knowledge and strong practical skills via implementation
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 informationResource Management for Dynamic MapReduce Clusters in Multicluster Systems
Resource Management for Dynamic MapReduce Clusters in Multicluster Systems Bogdan Ghiţ, Nezih Yigitbasi, Dick Epema Delft University of Technology, the Netherlands {b.i.ghit, m.n.yigitbasi, d.h.j.epema}@tudelft.nl
More informationTop 25 Big Data Interview Questions And Answers
Top 25 Big Data Interview Questions And Answers By: Neeru Jain - Big Data The era of big data has just begun. With more companies inclined towards big data to run their operations, the demand for talent
More informationA Survey on Big Data
A Survey on Big Data D.Prudhvi 1, D.Jaswitha 2, B. Mounika 3, Monika Bagal 4 1 2 3 4 B.Tech Final Year, CSE, Dadi Institute of Engineering & Technology,Andhra Pradesh,INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------
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 informationSinbad. Leveraging Endpoint Flexibility in Data-Intensive Clusters. Mosharaf Chowdhury, Srikanth Kandula, Ion Stoica. UC Berkeley
Sinbad Leveraging Endpoint Flexibility in Data-Intensive Clusters Mosharaf Chowdhury, Srikanth Kandula, Ion Stoica UC Berkeley Communication is Crucial for Analytics at Scale Performance Facebook analytics
More informationIntroduction to the Hadoop Ecosystem - 1
Hello and welcome to this online, self-paced course titled Administering and Managing the Oracle Big Data Appliance (BDA). This course contains several lessons. This lesson is titled Introduction to the
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 Analytics. Izabela Moise, Evangelos Pournaras, Dirk Helbing
Big Data Analytics Izabela Moise, Evangelos Pournaras, Dirk Helbing Izabela Moise, Evangelos Pournaras, Dirk Helbing 1 Big Data "The world is crazy. But at least it s getting regular analysis." Izabela
More informationCS370 Operating Systems
CS370 Operating Systems Colorado State University Yashwant K Malaiya Spring 2018 Lecture 24 Mass Storage, HDFS/Hadoop Slides based on Text by Silberschatz, Galvin, Gagne Various sources 1 1 FAQ What 2
More informationLocality Aware Fair Scheduling for Hammr
Locality Aware Fair Scheduling for Hammr Li Jin January 12, 2012 Abstract Hammr is a distributed execution engine for data parallel applications modeled after Dryad. In this report, we present a locality
More informationExamTorrent. Best exam torrent, excellent test torrent, valid exam dumps are here waiting for you
ExamTorrent http://www.examtorrent.com Best exam torrent, excellent test torrent, valid exam dumps are here waiting for you Exam : Apache-Hadoop-Developer Title : Hadoop 2.0 Certification exam for Pig
More informationIntroduction to Data Management CSE 344
Introduction to Data Management CSE 344 Lecture 24: MapReduce CSE 344 - Winter 215 1 HW8 MapReduce (Hadoop) w/ declarative language (Pig) Due next Thursday evening Will send out reimbursement codes later
More informationAutomatic-Hot HA for HDFS NameNode Konstantin V Shvachko Ari Flink Timothy Coulter EBay Cisco Aisle Five. November 11, 2011
Automatic-Hot HA for HDFS NameNode Konstantin V Shvachko Ari Flink Timothy Coulter EBay Cisco Aisle Five November 11, 2011 About Authors Konstantin Shvachko Hadoop Architect, ebay; Hadoop Committer Ari
More informationTopics. Big Data Analytics What is and Why Hadoop? Comparison to other technologies Hadoop architecture Hadoop ecosystem Hadoop usage examples
Hadoop Introduction 1 Topics Big Data Analytics What is and Why Hadoop? Comparison to other technologies Hadoop architecture Hadoop ecosystem Hadoop usage examples 2 Big Data Analytics What is Big Data?
More information