Linux Performance Optimizations for Big Data Environments

Save this PDF as:

Size: px
Start display at page:

Download "Linux Performance Optimizations for Big Data Environments"

Transcription

1 Linux Performance Optimizations for Big Data Environments Dominique A. Heger Ph.D. DHTechnologies (Performance, Capacity, Scalability) Data Nubes (Big Data, Hadoop, ML)

2 Performance & Capacity Studies Availability & Reliability Studies Systems Modeling Scalability & Speedup Studies Linux & UNIX Internals Design, Architecture & Feasibility Studies Systems Stress- Testing & Benchmarking Cloud Computing Research, Education & Training Machine Learning Operations Research BI, Data Analytics, Data Mining, Predictive Analytics Hadoop Ecosystem & MapReduce

3 Agenda Linux & Big Data (Hadoop Ecosystem) Performance Management Methodology Linux 3.x Task & I/O Framework Quantifying Linux & Application Performance Q&A

4 Linux Engineers Big Demand & Small Talent Pool Big Data, Hadoop Ecosystem & Cloud Computing in general is powered by Linux 91.4% of the top 500 supercomputers are Linux-based (Source TOP500, 2012) Linux Talent needed now A 2013 job report compiled by Dice showed that 93% of the contacted US companies (850 firms) are hiring Linux professionals this year (2013) The same study revealed that 90% of the firms stated that it is very difficult at the moment to even find Linux talent in the US. This number is up from 80% for the 2012 study According to Dice, the average salary increase for a Linux professional in the US is approximately 9% this year. At the same time, the average IT salary increase in the US is approximately 5%

5 Hadoop Ecosystem (Partial View) Twitter Real-Time Processing Data Handlers Data Serialization System Configuration Management Tools KAFKA Distributed Messaging System Schedulers RDBMS Database & No

6 Hadoop Linux Interaction Language Abstraction Java API MapReduce Framework(*) Hadoop Distributed Filesystem Hadoop (*) Some Hadoop Projects Bypass MapReduce Linux OS Node HW OS & Local FS HW Components

7 Hadoop MR2 Environment

8 Performance Management - Building Blocks Phase 1: Understand Goals & Objectives Phase 2: Phase 3: HW Profiles Workload Profiles Application & OS Traces Data Post-Processing Phase 4: Performance Study CSA Study Phase 5: Capacity Study Scalability Study Speedup Study

9 Performance Evaluation - Goals & Objectives Identify bottlenecks, predict future capacity shortcomings, and determine the most adequate (cost effective) way to configure, tune, and optimize computing environments to overcome performance problems and cope with increasing application workload demands. Combination of analytical, simulation, and empirical study based approaches that utilizes tracing techniques, HW profiles, actual application workload profiles, application log files, and performance data collected either in a Lab or production environment. If no empirical data is available, performance budgets are being used (PE). 9

10 Application Centric Systems Analysis System Hierarchy Application Abstraction Operating System Abstraction Hardware Abstraction Performance Hierarchy Application Vector Performance Code Path - Application to OS Interface Performance Code Path - High to Low Level OS Interface Performance Code Path - OS to HW Interface OS Vector Application Primitives High-Level OS Primitives Low-Level OS Primitives Hardware Primitives Process/Thread Monitors Application Trace Tools & Macro Benchmarks OS Performance Tools & Micro Benchmarks

11 Linux & Hadoop Tools & Techniques Linux Performance Evaluation Tools (Code Path Analysis) strace nmon, blktrace, blkparse, btt, blkiomon, iostat perf valgrind, kcachegrind Workload Generators (Macro & Micro Benchmarks) DHTUX toolset (Unix, Linux 46 systems benchmarks) TeraSort (Hadoop) K-Means Clustering (Hadoop) Bayesian Classification (Hadoop)

12 Performance by the Numbers (Ballpark Figures) L1 cache reference TLB miss Branch misprediction L2 cache reference Mutex lock/unlock Main memory reference Compress 1Kbytes with Zippy Send 2Kbytes over 1Gbps network Read 1MB sequentially from memory Round trip within same datacenter Disk seek (HD) Read 1MB sequentially from disk (HD) Send packet CA->UK->CA 1ns 4ns 5ns 7ns 25ns 100ns 3,000ns 20,000ns 250,000ns 500,000ns 10,000,000ns 20,000,000ns 150,000,000ns Execute Micro & Macro Benchmarks to Baseline the HW and the OS Hadoop MapReduce: With large-scale projects, the performance focus is on disk and interconnect/network performance rather than on the CPU and the DRAM subsystems

13 Micro & Macro Benchmarks Benchmarking & Stress-Testing the HW & the OS prior to deploying the Cluster Nodes Establish a Sound Performance Baseline

14 Application User Space Linux I/O Requests File System Layer Linux bio Layer Linux dequeue Function I/O Task Queue Linux enqueue Function Device Driver I/O Scheduler Disk/RAID/SAN Subsystem

15 Linux 3.x IO Schedulers (3.x) CFQ (default) synchronous verses asynchronous requests, IO priority, read favored over write requests, time-out value noop unordered FIFO queue, only merging, good for environments where IO is optimized at a lower level Deadline 5 IO queues, reorder requests, deadline value, read favored over write requests

16 Application Layer - strace

17 Kernel Layer - blktrace/blkparse

18 Kernel Layer - blktrace - Summary

19 Kernel Layer - btt

20 Kernel Layer - btt (time-line)

21 perf Linux Performance Tool

22 valgrin memcheck (Memory Leaks)

23 valgrin kcachegrin (Call Profiler)

24 Q & A

LARGE, DISTRIBUTED COMPUTING INFRASTRUCTURES OPPORTUNITIES & CHALLENGES. Dominique A. Heger Ph.D. DHTechnologies, Data Nubes Austin, TX, USA

LARGE, DISTRIBUTED COMPUTING INFRASTRUCTURES OPPORTUNITIES & CHALLENGES. Dominique A. Heger Ph.D. DHTechnologies, Data Nubes Austin, TX, USA LARGE, DISTRIBUTED COMPUTING INFRASTRUCTURES OPPORTUNITIES & CHALLENGES Dominique A. Heger Ph.D. DHTechnologies, Data Nubes Austin, TX, USA Performance & Capacity Studies Availability & Reliability Studies

More information

ZingMe Practice For Building Scalable PHP Website. By Chau Nguyen Nhat Thanh ZingMe Technical Manager Web Technical - VNG

ZingMe Practice For Building Scalable PHP Website. By Chau Nguyen Nhat Thanh ZingMe Technical Manager Web Technical - VNG ZingMe Practice For Building Scalable PHP Website By Chau Nguyen Nhat Thanh ZingMe Technical Manager Web Technical - VNG Agenda About ZingMe Scaling PHP application Scalability definition Scaling up vs

More information

Architecting for the next generation of Big Data Hortonworks HDP 2.0 on Red Hat Enterprise Linux 6 with OpenJDK 7

Architecting for the next generation of Big Data Hortonworks HDP 2.0 on Red Hat Enterprise Linux 6 with OpenJDK 7 Architecting for the next generation of Big Data Hortonworks HDP 2.0 on Red Hat Enterprise Linux 6 with OpenJDK 7 Yan Fisher Senior Principal Product Marketing Manager, Red Hat Rohit Bakhshi Product Manager,

More information

the road to cloud native applications Fabien Hermenier

the road to cloud native applications Fabien Hermenier the road to cloud native applications Fabien Hermenier 1 cloud ready applications single-tiered monolithic hardware specific cloud native applications leverage cloud services scalable reliable 2 Agenda

More information

Datacenter Operating Systems

Datacenter Operating Systems Datacenter Operating Systems CSE451 Simon Peter With thanks to Timothy Roscoe (ETH Zurich) Autumn 2015 This Lecture What s a datacenter Why datacenters Types of datacenters Hyperscale datacenters Major

More information

HiBench Introduction. Carson Wang (carson.wang@intel.com) Software & Services Group

HiBench Introduction. Carson Wang (carson.wang@intel.com) Software & Services Group HiBench Introduction Carson Wang (carson.wang@intel.com) Agenda Background Workloads Configurations Benchmark Report Tuning Guide Background WHY Why we need big data benchmarking systems? WHAT What is

More information

PERFORMANCE TUNING ORACLE RAC ON LINUX

PERFORMANCE TUNING ORACLE RAC ON LINUX PERFORMANCE TUNING ORACLE RAC ON LINUX By: Edward Whalen Performance Tuning Corporation INTRODUCTION Performance tuning is an integral part of the maintenance and administration of the Oracle database

More information

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM Sneha D.Borkar 1, Prof.Chaitali S.Surtakar 2 Student of B.E., Information Technology, J.D.I.E.T, sborkar95@gmail.com Assistant Professor, Information

More information

Performance Analysis of Mixed Distributed Filesystem Workloads

Performance Analysis of Mixed Distributed Filesystem Workloads Performance Analysis of Mixed Distributed Filesystem Workloads Esteban Molina-Estolano, Maya Gokhale, Carlos Maltzahn, John May, John Bent, Scott Brandt Motivation Hadoop-tailored filesystems (e.g. CloudStore)

More information

Agenda. Enterprise Application Performance Factors. Current form of Enterprise Applications. Factors to Application Performance.

Agenda. Enterprise Application Performance Factors. Current form of Enterprise Applications. Factors to Application Performance. Agenda Enterprise Performance Factors Overall Enterprise Performance Factors Best Practice for generic Enterprise Best Practice for 3-tiers Enterprise Hardware Load Balancer Basic Unix Tuning Performance

More information

Performance and scalability of a large OLTP workload

Performance and scalability of a large OLTP workload Performance and scalability of a large OLTP workload ii Performance and scalability of a large OLTP workload Contents Performance and scalability of a large OLTP workload with DB2 9 for System z on Linux..............

More information

Accelerating and Simplifying Apache

Accelerating and Simplifying Apache Accelerating and Simplifying Apache Hadoop with Panasas ActiveStor White paper NOvember 2012 1.888.PANASAS www.panasas.com Executive Overview The technology requirements for big data vary significantly

More information

Next Generation Operating Systems

Next Generation Operating Systems Next Generation Operating Systems Zeljko Susnjar, Cisco CTG June 2015 The end of CPU scaling Future computing challenges Power efficiency Performance == parallelism Cisco Confidential 2 Paradox of the

More information

Audit & Tune Deliverables

Audit & Tune Deliverables Audit & Tune Deliverables The Initial Audit is a way for CMD to become familiar with a Client's environment. It provides a thorough overview of the environment and documents best practices for the PostgreSQL

More information

Performance Tuning and Optimizing SQL Databases 2016

Performance Tuning and Optimizing SQL Databases 2016 Performance Tuning and Optimizing SQL Databases 2016 http://www.homnick.com marketing@homnick.com +1.561.988.0567 Boca Raton, Fl USA About this course This four-day instructor-led course provides students

More information

Linux Block I/O Scheduling. Aaron Carroll aaronc@gelato.unsw.edu.au December 22, 2007

Linux Block I/O Scheduling. Aaron Carroll aaronc@gelato.unsw.edu.au December 22, 2007 Linux Block I/O Scheduling Aaron Carroll aaronc@gelato.unsw.edu.au December 22, 2007 As of version 2.6.24, the mainline Linux tree provides four block I/O schedulers: Noop, Deadline, Anticipatory (AS)

More information

Hadoop Architecture. Part 1

Hadoop Architecture. Part 1 Hadoop Architecture Part 1 Node, Rack and Cluster: A node is simply a computer, typically non-enterprise, commodity hardware for nodes that contain data. Consider we have Node 1.Then we can add more nodes,

More information

2 Purpose. 3 Hardware enablement 4 System tools 5 General features. www.redhat.com

2 Purpose. 3 Hardware enablement 4 System tools 5 General features. www.redhat.com A Technical Introduction to Red Hat Enterprise Linux 5.4 The Enterprise LINUX Team 2 Purpose 3 Systems Enablement 3 Hardware enablement 4 System tools 5 General features 6 Virtualization 7 Conclusion www.redhat.com

More information

Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray VMware

Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray VMware Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray ware 2 Agenda The Hadoop Journey Why Virtualize Hadoop? Elasticity and Scalability Performance Tests Storage Reference

More information

KVM PERFORMANCE IMPROVEMENTS AND OPTIMIZATIONS. Mark Wagner Principal SW Engineer, Red Hat August 14, 2011

KVM PERFORMANCE IMPROVEMENTS AND OPTIMIZATIONS. Mark Wagner Principal SW Engineer, Red Hat August 14, 2011 KVM PERFORMANCE IMPROVEMENTS AND OPTIMIZATIONS Mark Wagner Principal SW Engineer, Red Hat August 14, 2011 1 Overview Discuss a range of topics about KVM performance How to improve out of the box experience

More information

Mixing Hadoop and HPC Workloads on Parallel Filesystems

Mixing Hadoop and HPC Workloads on Parallel Filesystems Mixing Hadoop and HPC Workloads on Parallel Filesystems Esteban Molina-Estolano *, Maya Gokhale, Carlos Maltzahn *, John May, John Bent, Scott Brandt * * UC Santa Cruz, ISSDM, PDSI Lawrence Livermore National

More information

A Framework for Performance Analysis and Tuning in Hadoop Based Clusters

A Framework for Performance Analysis and Tuning in Hadoop Based Clusters A Framework for Performance Analysis and Tuning in Hadoop Based Clusters Garvit Bansal Anshul Gupta Utkarsh Pyne LNMIIT, Jaipur, India Email: [garvit.bansal anshul.gupta utkarsh.pyne] @lnmiit.ac.in Manish

More information

CSE-E5430 Scalable Cloud Computing Lecture 2

CSE-E5430 Scalable Cloud Computing Lecture 2 CSE-E5430 Scalable Cloud Computing Lecture 2 Keijo Heljanko Department of Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 14.9-2015 1/36 Google MapReduce A scalable batch processing

More information

Mambo Running Analytics on Enterprise Storage

Mambo Running Analytics on Enterprise Storage Mambo Running Analytics on Enterprise Storage Jingxin Feng, Xing Lin 1, Gokul Soundararajan Advanced Technology Group 1 University of Utah Motivation No easy way to analyze data stored in enterprise storage

More information

Storage Architectures for Big Data in the Cloud

Storage Architectures for Big Data in the Cloud Storage Architectures for Big Data in the Cloud Sam Fineberg HP Storage CT Office/ May 2013 Overview Introduction What is big data? Big Data I/O Hadoop/HDFS SAN Distributed FS Cloud Summary Research Areas

More information

Impact of Big Data growth On Transparent Computing

Impact of Big Data growth On Transparent Computing Impact of Big Data growth On Transparent Computing Michael A. Greene Intel Vice President, Software and Services Group, General Manager, System Technologies and Optimization 1 Transparent Computing (TC)

More information

STeP-IN SUMMIT 2014. June 2014 at Bangalore, Hyderabad, Pune - INDIA. Performance testing Hadoop based big data analytics solutions

STeP-IN SUMMIT 2014. June 2014 at Bangalore, Hyderabad, Pune - INDIA. Performance testing Hadoop based big data analytics solutions 11 th International Conference on Software Testing June 2014 at Bangalore, Hyderabad, Pune - INDIA Performance testing Hadoop based big data analytics solutions by Mustufa Batterywala, Performance Architect,

More information

Red Hat Linux Internals

Red Hat Linux Internals Red Hat Linux Internals Learn how the Linux kernel functions and start developing modules. Red Hat Linux internals teaches you all the fundamental requirements necessary to understand and start developing

More information

Distributed File System. MCSN N. Tonellotto Complements of Distributed Enabling Platforms

Distributed File System. MCSN N. Tonellotto Complements of Distributed Enabling Platforms Distributed File System 1 How do we get data to the workers? NAS Compute Nodes SAN 2 Distributed File System Don t move data to workers move workers to the data! Store data on the local disks of nodes

More information

Exar. Optimizing Hadoop Is Bigger Better?? March 2013. sales@exar.com. Exar Corporation 48720 Kato Road Fremont, CA 510-668-7000. www.exar.

Exar. Optimizing Hadoop Is Bigger Better?? March 2013. sales@exar.com. Exar Corporation 48720 Kato Road Fremont, CA 510-668-7000. www.exar. Exar Optimizing Hadoop Is Bigger Better?? sales@exar.com Exar Corporation 48720 Kato Road Fremont, CA 510-668-7000 March 2013 www.exar.com Section I: Exar Introduction Exar Corporate Overview Section II:

More information

Performance Testing at Scale

Performance Testing at Scale Performance Testing at Scale An overview of performance testing at NetApp. Shaun Dunning shaun.dunning@netapp.com 1 Outline Performance Engineering responsibilities How we protect performance Overview

More information

Maximizing Hadoop Performance and Storage Capacity with AltraHD TM

Maximizing Hadoop Performance and Storage Capacity with AltraHD TM Maximizing Hadoop Performance and Storage Capacity with AltraHD TM Executive Summary The explosion of internet data, driven in large part by the growth of more and more powerful mobile devices, has created

More information

Block I/O Layer Tracing: blktrace

Block I/O Layer Tracing: blktrace Block I/O Layer Tracing: blktrace Gelato Cupertino, CA April 2006 Alan D. Brunelle Hewlett Packard Company Open Source and Linux Organization Scalability & Performance Group Alan.Brunelle@hp.com 1 Introduction

More information

Enabling High performance Big Data platform with RDMA

Enabling High performance Big Data platform with RDMA Enabling High performance Big Data platform with RDMA Tong Liu HPC Advisory Council Oct 7 th, 2014 Shortcomings of Hadoop Administration tooling Performance Reliability SQL support Backup and recovery

More information

Hadoop & Spark Using Amazon EMR

Hadoop & Spark Using Amazon EMR Hadoop & Spark Using Amazon EMR Michael Hanisch, AWS Solutions Architecture 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda Why did we build Amazon EMR? What is Amazon EMR?

More information

System Models for Distributed and Cloud Computing

System Models for Distributed and Cloud Computing System Models for Distributed and Cloud Computing Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Classification of Distributed Computing Systems

More information

GraySort on Apache Spark by Databricks

GraySort on Apache Spark by Databricks GraySort on Apache Spark by Databricks Reynold Xin, Parviz Deyhim, Ali Ghodsi, Xiangrui Meng, Matei Zaharia Databricks Inc. Apache Spark Sorting in Spark Overview Sorting Within a Partition Range Partitioner

More information

Big Fast Data Hadoop acceleration with Flash. June 2013

Big Fast Data Hadoop acceleration with Flash. June 2013 Big Fast Data Hadoop acceleration with Flash June 2013 Agenda The Big Data Problem What is Hadoop Hadoop and Flash The Nytro Solution Test Results The Big Data Problem Big Data Output Facebook Traditional

More information

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB bankmark UG (haftungsbeschränkt) Bahnhofstraße 1 9432 Passau Germany www.bankmark.de info@bankmark.de T +49 851 25 49 49 F +49 851 25 49 499 NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB,

More information

Hadoop Hardware @Twitter: Size does matter. @joep and @eecraft Hadoop Summit 2013

Hadoop Hardware @Twitter: Size does matter. @joep and @eecraft Hadoop Summit 2013 Hadoop Hardware : Size does matter. @joep and @eecraft Hadoop Summit 2013 v2.3 About us Joep Rottinghuis Software Engineer @ Twitter Engineering Manager Hadoop/HBase team @ Twitter Follow me @joep Jay

More information

Web Application s Performance Testing

Web Application s Performance Testing Web Application s Performance Testing B. Election Reddy (07305054) Guided by N. L. Sarda April 13, 2008 1 Contents 1 Introduction 4 2 Objectives 4 3 Performance Indicators 5 4 Types of Performance Testing

More information

Accelerating Hadoop MapReduce Using an In-Memory Data Grid

Accelerating Hadoop MapReduce Using an In-Memory Data Grid Accelerating Hadoop MapReduce Using an In-Memory Data Grid By David L. Brinker and William L. Bain, ScaleOut Software, Inc. 2013 ScaleOut Software, Inc. 12/27/2012 H adoop has been widely embraced for

More information

Operating System Components and Services

Operating System Components and Services Operating System Components and Services Tom Kelliher, CS 311 Feb. 6, 2012 Announcements: From last time: 1. System architecture issues. 2. I/O programming. 3. Memory hierarchy. 4. Hardware protection.

More information

HiBench Installation. Sunil Raiyani, Jayam Modi

HiBench Installation. Sunil Raiyani, Jayam Modi HiBench Installation Sunil Raiyani, Jayam Modi Last Updated: May 23, 2014 CONTENTS Contents 1 Introduction 1 2 Installation 1 3 HiBench Benchmarks[3] 1 3.1 Micro Benchmarks..............................

More information

Write a technical report Present your results Write a workshop/conference paper (optional) Could be a real system, simulation and/or theoretical

Write a technical report Present your results Write a workshop/conference paper (optional) Could be a real system, simulation and/or theoretical Identify a problem Review approaches to the problem Propose a novel approach to the problem Define, design, prototype an implementation to evaluate your approach Could be a real system, simulation and/or

More information

FPGA Accelerator Virtualization in an OpenPOWER cloud. Fei Chen, Yonghua Lin IBM China Research Lab

FPGA Accelerator Virtualization in an OpenPOWER cloud. Fei Chen, Yonghua Lin IBM China Research Lab FPGA Accelerator Virtualization in an OpenPOWER cloud Fei Chen, Yonghua Lin IBM China Research Lab Trend of Acceleration Technology Acceleration in Cloud is Taking Off Used FPGA to accelerate Bing search

More information

Optimizing the Performance of Your Longview Application

Optimizing the Performance of Your Longview Application Optimizing the Performance of Your Longview Application François Lalonde, Director Application Support May 15, 2013 Disclaimer This presentation is provided to you solely for information purposes, is not

More information

DSS. Diskpool and cloud storage benchmarks used in IT-DSS. Data & Storage Services. Geoffray ADDE

DSS. Diskpool and cloud storage benchmarks used in IT-DSS. Data & Storage Services. Geoffray ADDE DSS Data & Diskpool and cloud storage benchmarks used in IT-DSS CERN IT Department CH-1211 Geneva 23 Switzerland www.cern.ch/it Geoffray ADDE DSS Outline I- A rational approach to storage systems evaluation

More information

Big Data. Value, use cases and architectures. Petar Torre Lead Architect Service Provider Group. Dubrovnik, Croatia, South East Europe 20-22 May, 2013

Big Data. Value, use cases and architectures. Petar Torre Lead Architect Service Provider Group. Dubrovnik, Croatia, South East Europe 20-22 May, 2013 Dubrovnik, Croatia, South East Europe 20-22 May, 2013 Big Data Value, use cases and architectures Petar Torre Lead Architect Service Provider Group 2011 2013 Cisco and/or its affiliates. All rights reserved.

More information

Load Testing Analysis Services Gerhard Brückl

Load Testing Analysis Services Gerhard Brückl Load Testing Analysis Services Gerhard Brückl About Me Gerhard Brückl Working with Microsoft BI since 2006 Mainly focused on Analytics and Reporting Analysis Services / Reporting Services Power BI / O365

More information

Operating System for the K computer

Operating System for the K computer Operating System for the K computer Jun Moroo Masahiko Yamada Takeharu Kato For the K computer to achieve the world s highest performance, Fujitsu has worked on the following three performance improvements

More information

OpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC

OpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC OpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC Driving industry innovation The goal of the OpenPOWER Foundation is to create an open ecosystem, using the POWER Architecture to share expertise,

More information

HDFS Under the Hood. Sanjay Radia. Sradia@yahoo-inc.com Grid Computing, Hadoop Yahoo Inc.

HDFS Under the Hood. Sanjay Radia. Sradia@yahoo-inc.com Grid Computing, Hadoop Yahoo Inc. HDFS Under the Hood Sanjay Radia Sradia@yahoo-inc.com Grid Computing, Hadoop Yahoo Inc. 1 Outline Overview of Hadoop, an open source project Design of HDFS On going work 2 Hadoop Hadoop provides a framework

More information

DEPLOYING AND MONITORING HADOOP MAP-REDUCE ANALYTICS ON SINGLE-CHIP CLOUD COMPUTER

DEPLOYING AND MONITORING HADOOP MAP-REDUCE ANALYTICS ON SINGLE-CHIP CLOUD COMPUTER DEPLOYING AND MONITORING HADOOP MAP-REDUCE ANALYTICS ON SINGLE-CHIP CLOUD COMPUTER ANDREAS-LAZAROS GEORGIADIS, SOTIRIOS XYDIS, DIMITRIOS SOUDRIS MICROPROCESSOR AND MICROSYSTEMS LABORATORY ELECTRICAL AND

More information

OPTIMIZE DMA CONFIGURATION IN ENCRYPTION USE CASE. Guillène Ribière, CEO, System Architect

OPTIMIZE DMA CONFIGURATION IN ENCRYPTION USE CASE. Guillène Ribière, CEO, System Architect OPTIMIZE DMA CONFIGURATION IN ENCRYPTION USE CASE Guillène Ribière, CEO, System Architect Problem Statement Low Performances on Hardware Accelerated Encryption: Max Measured 10MBps Expectations: 90 MBps

More information

COURSE CONTENT Big Data and Hadoop Training

COURSE CONTENT Big Data and Hadoop Training COURSE CONTENT Big Data and Hadoop Training 1. Meet Hadoop Data! Data Storage and Analysis Comparison with Other Systems RDBMS Grid Computing Volunteer Computing A Brief History of Hadoop Apache Hadoop

More information

Distributed File Systems

Distributed File Systems Distributed File Systems Paul Krzyzanowski Rutgers University October 28, 2012 1 Introduction The classic network file systems we examined, NFS, CIFS, AFS, Coda, were designed as client-server applications.

More information

Using MySQL for Big Data Advantage Integrate for Insight Sastry Vedantam sastry.vedantam@oracle.com

Using MySQL for Big Data Advantage Integrate for Insight Sastry Vedantam sastry.vedantam@oracle.com Using MySQL for Big Data Advantage Integrate for Insight Sastry Vedantam sastry.vedantam@oracle.com Agenda The rise of Big Data & Hadoop MySQL in the Big Data Lifecycle MySQL Solutions for Big Data Q&A

More information

Running a Workflow on a PowerCenter Grid

Running a Workflow on a PowerCenter Grid Running a Workflow on a PowerCenter Grid 2010-2014 Informatica Corporation. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise)

More information

Supercomputing and Big Data: Where are the Real Boundaries and Opportunities for Synergy?

Supercomputing and Big Data: Where are the Real Boundaries and Opportunities for Synergy? HPC2012 Workshop Cetraro, Italy Supercomputing and Big Data: Where are the Real Boundaries and Opportunities for Synergy? Bill Blake CTO Cray, Inc. The Big Data Challenge Supercomputing minimizes data

More information

SQL Server Performance Tuning and Optimization

SQL Server Performance Tuning and Optimization 3 Riverchase Office Plaza Hoover, Alabama 35244 Phone: 205.989.4944 Fax: 855.317.2187 E-Mail: rwhitney@discoveritt.com Web: www.discoveritt.com SQL Server Performance Tuning and Optimization Course: MS10980A

More information

Hadoop Cluster Applications

Hadoop Cluster Applications Hadoop Overview Data analytics has become a key element of the business decision process over the last decade. Classic reporting on a dataset stored in a database was sufficient until recently, but yesterday

More information

Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics

Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics Juwei Shi, Yunjie Qiu, Umar Farooq Minhas, Limei Jiao, Chen Wang, Berthold Reinwald, and Fatma Özcan IBM Research China IBM Almaden

More information

Building All-Flash Software Defined Storages for Datacenters. Ji Hyuck Yun (dr.jhyun@sk.com) Storage Tech. Lab SK Telecom

Building All-Flash Software Defined Storages for Datacenters. Ji Hyuck Yun (dr.jhyun@sk.com) Storage Tech. Lab SK Telecom Building All-Flash Software Defined Storages for Datacenters Ji Hyuck Yun (dr.jhyun@sk.com) Storage Tech. Lab SK Telecom Introduction R&D Motivation Synergy between SK Telecom and SK Hynix Service & Solution

More information

Dell Reference Configuration for Hortonworks Data Platform

Dell Reference Configuration for Hortonworks Data Platform Dell Reference Configuration for Hortonworks Data Platform A Quick Reference Configuration Guide Armando Acosta Hadoop Product Manager Dell Revolutionary Cloud and Big Data Group Kris Applegate Solution

More information

Datacenter Software and Client Software 廖 世 偉

Datacenter Software and Client Software 廖 世 偉 Datacenter Software and Client Software 廖 世 偉 November 2009 Announcement 12/7: Quiz to help recap MapReduce (Greg Malewicz) 12/14: Originally should be Harvard Professor HT Kung s special lecture. Now

More information

Virtualizing a Virtual Machine

Virtualizing a Virtual Machine Virtualizing a Virtual Machine Azeem Jiva Shrinivas Joshi AMD Java Labs TS-5227 Learn best practices for deploying Java EE applications in virtualized environment 2008 JavaOne SM Conference java.com.sun/javaone

More information

Jun Liu, Senior Software Engineer Bianny Bian, Engineering Manager SSG/STO/PAC

Jun Liu, Senior Software Engineer Bianny Bian, Engineering Manager SSG/STO/PAC Jun Liu, Senior Software Engineer Bianny Bian, Engineering Manager SSG/STO/PAC Agenda Quick Overview of Impala Design Challenges of an Impala Deployment Case Study: Use Simulation-Based Approach to Design

More information

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Hadoop MapReduce and Spark Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Outline Hadoop Hadoop Import data on Hadoop Spark Spark features Scala MLlib MLlib

More information

PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE

PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE Sudha M 1, Harish G M 2, Nandan A 3, Usha J 4 1 Department of MCA, R V College of Engineering, Bangalore : 560059, India sudha.mooki@gmail.com 2 Department

More information

ZooKeeper. Table of contents

ZooKeeper. Table of contents by Table of contents 1 ZooKeeper: A Distributed Coordination Service for Distributed Applications... 2 1.1 Design Goals...2 1.2 Data model and the hierarchical namespace...3 1.3 Nodes and ephemeral nodes...

More information

Apache Hadoop. Alexandru Costan

Apache Hadoop. Alexandru Costan 1 Apache Hadoop Alexandru Costan Big Data Landscape No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard, except Hadoop 2 Outline What is Hadoop? Who uses it? Architecture HDFS MapReduce Open

More information

Winning the J2EE Performance Game Presented to: JAVA User Group-Minnesota

Winning the J2EE Performance Game Presented to: JAVA User Group-Minnesota Winning the J2EE Performance Game Presented to: JAVA User Group-Minnesota Michelle Pregler Ball Emerging Markets Account Executive Shahrukh Niazi Sr.System Consultant Java Solutions Quest Background Agenda

More information

Stingray Traffic Manager Sizing Guide

Stingray Traffic Manager Sizing Guide STINGRAY TRAFFIC MANAGER SIZING GUIDE 1 Stingray Traffic Manager Sizing Guide Stingray Traffic Manager version 8.0, December 2011. For internal and partner use. Introduction The performance of Stingray

More information

Chukwa, Hadoop subproject, 37, 131 Cloud enabled big data, 4 Codd s 12 rules, 1 Column-oriented databases, 18, 52 Compression pattern, 83 84

Chukwa, Hadoop subproject, 37, 131 Cloud enabled big data, 4 Codd s 12 rules, 1 Column-oriented databases, 18, 52 Compression pattern, 83 84 Index A Amazon Web Services (AWS), 50, 58 Analytics engine, 21 22 Apache Kafka, 38, 131 Apache S4, 38, 131 Apache Sqoop, 37, 131 Appliance pattern, 104 105 Application architecture, big data analytics

More information

Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays. Red Hat Performance Engineering

Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays. Red Hat Performance Engineering Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays Red Hat Performance Engineering Version 1.0 August 2013 1801 Varsity Drive Raleigh NC

More information

Performance Architect Remote Storage (Intern)

Performance Architect Remote Storage (Intern) Performance Architect Remote Storage (Intern) Samsung Semiconductor, Inc. is a world leader in Memory, System LSI and LCD technologies. We are currently looking for a Performance Architect (Intern) to

More information

Introduction. Various user groups requiring Hadoop, each with its own diverse needs, include:

Introduction. Various user groups requiring Hadoop, each with its own diverse needs, include: Introduction BIG DATA is a term that s been buzzing around a lot lately, and its use is a trend that s been increasing at a steady pace over the past few years. It s quite likely you ve also encountered

More information

VMware vsphere 4.1 with ESXi and vcenter

VMware vsphere 4.1 with ESXi and vcenter VMware vsphere 4.1 with ESXi and vcenter This powerful 5-day class is an intense introduction to virtualization using VMware s vsphere 4.1 including VMware ESX 4.1 and vcenter. Assuming no prior virtualization

More information

Violin: A Framework for Extensible Block-level Storage

Violin: A Framework for Extensible Block-level Storage Violin: A Framework for Extensible Block-level Storage Michail Flouris Dept. of Computer Science, University of Toronto, Canada flouris@cs.toronto.edu Angelos Bilas ICS-FORTH & University of Crete, Greece

More information

PERFORMANCE TESTING. New Batches Info. We are ready to serve Latest Testing Trends, Are you ready to learn.?? START DATE : TIMINGS : DURATION :

PERFORMANCE TESTING. New Batches Info. We are ready to serve Latest Testing Trends, Are you ready to learn.?? START DATE : TIMINGS : DURATION : PERFORMANCE TESTING We are ready to serve Latest Testing Trends, Are you ready to learn.?? New Batches Info START DATE : TIMINGS : DURATION : TYPE OF BATCH : FEE : FACULTY NAME : LAB TIMINGS : Performance

More information

Workload Dependent Hadoop MapReduce Application Performance Modeling

Workload Dependent Hadoop MapReduce Application Performance Modeling Workload Dependent Hadoop MapReduce Application Performance Modeling Introduction In any distributed computing environment, performance optimization, job runtime predictions, or capacity and scalability

More information

Automating Big Data Benchmarking for Different Architectures with ALOJA

Automating Big Data Benchmarking for Different Architectures with ALOJA www.bsc.es Jan 2016 Automating Big Data Benchmarking for Different Architectures with ALOJA Nicolas Poggi, Postdoc Researcher Agenda 1. Intro on Hadoop performance 1. Current scenario and problematic 2.

More information

Performance Comparison of SQL based Big Data Analytics with Lustre and HDFS file systems

Performance Comparison of SQL based Big Data Analytics with Lustre and HDFS file systems Performance Comparison of SQL based Big Data Analytics with Lustre and HDFS file systems Rekha Singhal and Gabriele Pacciucci * Other names and brands may be claimed as the property of others. Lustre File

More information

Above the clouds: A Berkeley View of Cloud Computing

Above the clouds: A Berkeley View of Cloud Computing Partial Review-2 On the paper Above the clouds: A Berkeley View of Cloud Computing By Nikhil Ramteke Sr. No.- 07125 6. Cloud Computing Economics Observation in Cloud Economics mainly concerns with following

More information

Big Data: A Storage Systems Perspective Muthukumar Murugan Ph.D. HP Storage Division

Big Data: A Storage Systems Perspective Muthukumar Murugan Ph.D. HP Storage Division Big Data: A Storage Systems Perspective Muthukumar Murugan Ph.D. HP Storage Division In this talk Big data storage: Current trends Issues with current storage options Evolution of storage to support big

More information

Task Scheduling in Hadoop

Task Scheduling in Hadoop Task Scheduling in Hadoop Sagar Mamdapure Munira Ginwala Neha Papat SAE,Kondhwa SAE,Kondhwa SAE,Kondhwa Abstract Hadoop is widely used for storing large datasets and processing them efficiently under distributed

More information

Cloud Operating Systems for Servers

Cloud Operating Systems for Servers Cloud Operating Systems for Servers Mike Day Distinguished Engineer, Virtualization and Linux August 20, 2014 mdday@us.ibm.com 1 What Makes a Good Cloud Operating System?! Consumes Few Resources! Fast

More information

Chapter 3 Operating-System Structures

Chapter 3 Operating-System Structures Contents 1. Introduction 2. Computer-System Structures 3. Operating-System Structures 4. Processes 5. Threads 6. CPU Scheduling 7. Process Synchronization 8. Deadlocks 9. Memory Management 10. Virtual

More information

Tomcat Tuning. Mark Thomas April 2009

Tomcat Tuning. Mark Thomas April 2009 Tomcat Tuning Mark Thomas April 2009 Who am I? Apache Tomcat committer Resolved 1,500+ Tomcat bugs Apache Tomcat PMC member Member of the Apache Software Foundation Member of the ASF security committee

More information

Petascale Software Challenges. Piyush Chaudhary piyushc@us.ibm.com High Performance Computing

Petascale Software Challenges. Piyush Chaudhary piyushc@us.ibm.com High Performance Computing Petascale Software Challenges Piyush Chaudhary piyushc@us.ibm.com High Performance Computing Fundamental Observations Applications are struggling to realize growth in sustained performance at scale Reasons

More information

Duke University http://www.cs.duke.edu/starfish

Duke University http://www.cs.duke.edu/starfish Herodotos Herodotou, Harold Lim, Fei Dong, Shivnath Babu Duke University http://www.cs.duke.edu/starfish Practitioners of Big Data Analytics Google Yahoo! Facebook ebay Physicists Biologists Economists

More information

An Oracle White Paper July 2011. Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide

An Oracle White Paper July 2011. Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide An Oracle White Paper July 2011 1 Disclaimer The following is intended to outline our general product direction.

More information

Architectures for Big Data Analytics A database perspective

Architectures for Big Data Analytics A database perspective Architectures for Big Data Analytics A database perspective Fernando Velez Director of Product Management Enterprise Information Management, SAP June 2013 Outline Big Data Analytics Requirements Spectrum

More information

On- Prem MongoDB- as- a- Service Powered by the CumuLogic DBaaS Platform

On- Prem MongoDB- as- a- Service Powered by the CumuLogic DBaaS Platform On- Prem MongoDB- as- a- Service Powered by the CumuLogic DBaaS Platform Page 1 of 16 Table of Contents Table of Contents... 2 Introduction... 3 NoSQL Databases... 3 CumuLogic NoSQL Database Service...

More information

Maximizing Hadoop Performance with Hardware Compression

Maximizing Hadoop Performance with Hardware Compression Maximizing Hadoop Performance with Hardware Compression Robert Reiner Director of Marketing Compression and Security Exar Corporation November 2012 1 What is Big? sets whose size is beyond the ability

More information

Microsoft SQL Server: MS-10980 Performance Tuning and Optimization Digital

Microsoft SQL Server: MS-10980 Performance Tuning and Optimization Digital coursemonster.com/us Microsoft SQL Server: MS-10980 Performance Tuning and Optimization Digital View training dates» Overview This course is designed to give the right amount of Internals knowledge and

More information

MySQL performance in a cloud. Mark Callaghan

MySQL performance in a cloud. Mark Callaghan MySQL performance in a cloud Mark Callaghan Special thanks Eric Hammond (http://www.anvilon.com) provided documentation that made all of my work much easier. What is this thing called a cloud? Deployment

More information

COS 318: Operating Systems. Virtual Machine Monitors

COS 318: Operating Systems. Virtual Machine Monitors COS 318: Operating Systems Virtual Machine Monitors Kai Li and Andy Bavier Computer Science Department Princeton University http://www.cs.princeton.edu/courses/archive/fall13/cos318/ Introduction u Have

More information