Data Structures for Big Data: Bloom Filter. Vinicius Vielmo Cogo Smalltalks, DI, FC/UL. October 16, 2014.

Size: px
Start display at page:

Download "Data Structures for Big Data: Bloom Filter. Vinicius Vielmo Cogo Smalltalks, DI, FC/UL. October 16, 2014."

Transcription

1 Data Structures for Big Data: Bloom Filter Vinicius Vielmo Cogo Smalltalks, DI, FC/UL. October 16, 2014.

2 is relative is not defined by a specific number of TB, PB, EB is when it becomes big for you is when your solutions become inefficient/impractical 2 / 30

3 Data Structures for Big Data Traditional DSs are subject to the same problems e.g., lists, trees or (e.g., YARN, NoSQL) (e.g., index, metadata) reached the point of thinking in new DSs for BD 3 / 30

4 Outline Bloom Filter Use Cases Implementations Other Filters Other Data Structures for Big Data 4 / 30

5 Membership testing Does my collection contain this element? 5 / 30

6 City Coimbra Leiria 6 / 30

7 Index i bf[i] / 30

8 City Coimbra Leiria Hash Function Fnv Murmur Index i bf[i] / 30

9 City Coimbra Leiria Hash Function Fnv Murmur i=4 i=7 Index i bf[i] / 30

10 City Coimbra Leiria Hash Function Fnv Murmur i=4 i=7 Index i bf[i] / 30

11 City Coimbra Leiria Hash Function Fnv Murmur Index i bf[i] / 30

12 City Coimbra Leiria Hash Function Fnv Murmur i=2 i=9 Index i bf[i] / 30

13 City Coimbra Leiria Hash Function Fnv Murmur i=2 i=9 Index i bf[i] / 30

14 City Coimbra Leiria Hash Function Fnv Murmur Index i bf[i] / 30

15 City Braga Guarda Coimbra Lisboa 15 / 30

16 City Braga Guarda Coimbra Lisboa Hash Function Fnv Murmur i=10 i=14 Index i bf[i] Result: false 16 / 30

17 City Braga Guarda Coimbra Lisboa Hash Function Fnv Murmur i=2 i=12 Index i bf[i] Result: false 17 / 30

18 City Braga Guarda Coimbra Lisboa Hash Function Fnv Murmur i=4 i=7 Index i bf[i] Result: true 18 / 30

19 City Braga Guarda Coimbra Lisboa Hash Function Fnv Murmur i=7 i=9 Index i bf[i] Result: true (but it is a false positive) 19 / 30

20 DS proposed by Burton Howard Bloom in 1970 Design principles Space-efficient Smaller than the original dataset Time-efficient Low latency R/W O(k), which is much smaller than O(n) High throughput Probabilistic E.g., mycollection.mightcontain(myobject) False positives happen (but in a configurable way) 20 / 30

21 Important variables = Expected collection size City Coimbra Leiria = False positive rate (e.g., % or 1 in 1M) = Bitmap size = Optimal number of hash functions Hash Function Fnv Murmur 21 / 30

22 Important variables 22 / 30

23 Users define two of them (normally n and any other) The other two are calculated with those equations Interesting relations: Bigger collection ( ) Larger bitmap ( ) Bigger collection ( ) More false positives ( ) Larger bitmap ( Less false positives ( ) Larger bitmap ( ) Less hash functions ( ) Less hash functions ( ) 23 / 30

24 Bloom filter size vs. False positive rate 24 / 30

25 Use Cases Reducing unnecessary disk reads Client BloomFilter Dataset 1 1? No F F 2 2? 2 T necessary read(2) T 3 3? No T unnecessary read(3) F RAM Hard Disk 25 / 30

26 Use Cases Google BigTable, Apache Cassandra and HBase Reducing disk lookups Google Chrome Lookup a list of known malicious URLs Bitcoin Get only the transactions relevant to your wallet Others In my Ph.D. work Lookup a list of known privacy-sensitive DNA sequences 26 / 30

27 Implementations -libraries https://code.google.com/p/guava-libraries/ Orestes-Bloomfilter https://github.com/baqend/orestes-bloomfilter java-bloomfilter https://github.com/magnuss/java-bloomfilter java-longfastbloomfilter https://code.google.com/p/java-longfastbloomfilter/ 27 / 30

28 Other Filters Counting Bloom filters Allow deletions (use a 4-bit counter instead of 1 bit) Buffered Bloom filters Sub-filters in SSD with buffered R/W exploring bit locality Quotient and Cascade filters Uses an SSD, instead of the main memory, for scalability 28 / 30

29 Other DSs (and techniques) for Big Data Locality-sensitive hashing (LSH) Hashing similar elements into the same bucket with high probability HyperLogLog for computing cardinality Counting the number of distinct elements in a collection Log Structured Merge (LSM) trees Indexed access to files with high insert volume and background batch synchronization 29 / 30

30 Thank you! Vinicius Vielmo Cogo Smalltalks, DI, FC/UL. October 16, 2014.

Kafka & Redis for Big Data Solutions

Kafka & Redis for Big Data Solutions Kafka & Redis for Big Data Solutions Christopher Curtin Head of Technical Research @ChrisCurtin About Me 25+ years in technology Head of Technical Research at Silverpop, an IBM Company (14 + years at Silverpop)

More information

Hypertable Architecture Overview

Hypertable Architecture Overview WHITE PAPER - MARCH 2012 Hypertable Architecture Overview Hypertable is an open source, scalable NoSQL database modeled after Bigtable, Google s proprietary scalable database. It is written in C++ for

More information

CS435 Introduction to Big Data

CS435 Introduction to Big Data CS435 Introduction to Big Data Final Exam Date: May 11 6:20PM 8:20PM Location: CSB 130 Closed Book, NO cheat sheets Topics covered *Note: Final exam is NOT comprehensive. 1. NoSQL Impedance mismatch Scale-up

More information

MAD2: A Scalable High-Throughput Exact Deduplication Approach for Network Backup Services

MAD2: A Scalable High-Throughput Exact Deduplication Approach for Network Backup Services MAD2: A Scalable High-Throughput Exact Deduplication Approach for Network Backup Services Jiansheng Wei, Hong Jiang, Ke Zhou, Dan Feng School of Computer, Huazhong University of Science and Technology,

More information

FAST 11. Yongseok Oh University of Seoul. Mobile Embedded System Laboratory

FAST 11. Yongseok Oh <ysoh@uos.ac.kr> University of Seoul. Mobile Embedded System Laboratory CAFTL: A Content-Aware Flash Translation Layer Enhancing the Lifespan of flash Memory based Solid State Drives FAST 11 Yongseok Oh University of Seoul Mobile Embedded System Laboratory

More information

Hadoop Ecosystem B Y R A H I M A.

Hadoop Ecosystem B Y R A H I M A. Hadoop Ecosystem B Y R A H I M A. History of Hadoop Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open

More information

HBase Schema Design. NoSQL Ma4ers, Cologne, April 2013. Lars George Director EMEA Services

HBase Schema Design. NoSQL Ma4ers, Cologne, April 2013. Lars George Director EMEA Services HBase Schema Design NoSQL Ma4ers, Cologne, April 2013 Lars George Director EMEA Services About Me Director EMEA Services @ Cloudera ConsulFng on Hadoop projects (everywhere) Apache Commi4er HBase and Whirr

More information

Big Data With Hadoop

Big Data With Hadoop With Saurabh Singh singh.903@osu.edu The Ohio State University February 11, 2016 Overview 1 2 3 Requirements Ecosystem Resilient Distributed Datasets (RDDs) Example Code vs Mapreduce 4 5 Source: [Tutorials

More information

Non-Stop for Apache HBase: Active-active region server clusters TECHNICAL BRIEF

Non-Stop for Apache HBase: Active-active region server clusters TECHNICAL BRIEF Non-Stop for Apache HBase: -active region server clusters TECHNICAL BRIEF Technical Brief: -active region server clusters -active region server clusters HBase is a non-relational database that provides

More information

NoSQL Data Base Basics

NoSQL Data Base Basics NoSQL Data Base Basics Course Notes in Transparency Format Cloud Computing MIRI (CLC-MIRI) UPC Master in Innovation & Research in Informatics Spring- 2013 Jordi Torres, UPC - BSC www.jorditorres.eu HDFS

More information

Facebook: Cassandra. Smruti R. Sarangi. Department of Computer Science Indian Institute of Technology New Delhi, India. Overview Design Evaluation

Facebook: Cassandra. Smruti R. Sarangi. Department of Computer Science Indian Institute of Technology New Delhi, India. Overview Design Evaluation Facebook: Cassandra Smruti R. Sarangi Department of Computer Science Indian Institute of Technology New Delhi, India Smruti R. Sarangi Leader Election 1/24 Outline 1 2 3 Smruti R. Sarangi Leader Election

More information

Cassandra vs MySQL. SQL vs NoSQL database comparison

Cassandra vs MySQL. SQL vs NoSQL database comparison Cassandra vs MySQL SQL vs NoSQL database comparison 19 th of November, 2015 Maxim Zakharenkov Maxim Zakharenkov Riga, Latvia Java Developer/Architect Company Goals Explore some differences of SQL and NoSQL

More information

Yahoo! Cloud Serving Benchmark

Yahoo! Cloud Serving Benchmark Yahoo! Cloud Serving Benchmark Overview and results March 31, 2010 Brian F. Cooper cooperb@yahoo-inc.com Joint work with Adam Silberstein, Erwin Tam, Raghu Ramakrishnan and Russell Sears System setup and

More information

THE HADOOP DISTRIBUTED FILE SYSTEM

THE HADOOP DISTRIBUTED FILE SYSTEM THE HADOOP DISTRIBUTED FILE SYSTEM Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler Presented by Alexander Pokluda October 7, 2013 Outline Motivation and Overview of Hadoop Architecture,

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Hadoop IST 734 SS CHUNG

Hadoop IST 734 SS CHUNG Hadoop IST 734 SS CHUNG Introduction What is Big Data?? Bulk Amount Unstructured Lots of Applications which need to handle huge amount of data (in terms of 500+ TB per day) If a regular machine need to

More information

Apache HBase. Crazy dances on the elephant back

Apache HBase. Crazy dances on the elephant back Apache HBase Crazy dances on the elephant back Roman Nikitchenko, 16.10.2014 YARN 2 FIRST EVER DATA OS 10.000 nodes computer Recent technology changes are focused on higher scale. Better resource usage

More information

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB Planet Size Data!? Gartner s 10 key IT trends for 2012 unstructured data will grow some 80% over the course of the next

More information

Quanqing XU Quanqing.Xu@nicta.com.au. YuruBackup: A Highly Scalable and Space-Efficient Incremental Backup System in the Cloud

Quanqing XU Quanqing.Xu@nicta.com.au. YuruBackup: A Highly Scalable and Space-Efficient Incremental Backup System in the Cloud Quanqing XU Quanqing.Xu@nicta.com.au YuruBackup: A Highly Scalable and Space-Efficient Incremental Backup System in the Cloud Outline Motivation YuruBackup s Architecture Backup Client File Scan, Data

More information

Hadoop: Embracing future hardware

Hadoop: Embracing future hardware Hadoop: Embracing future hardware Suresh Srinivas @suresh_m_s Page 1 About Me Architect & Founder at Hortonworks Long time Apache Hadoop committer and PMC member Designed and developed many key Hadoop

More information

A programming model in Cloud: MapReduce

A programming model in Cloud: MapReduce A programming model in Cloud: MapReduce Programming model and implementation developed by Google for processing large data sets Users specify a map function to generate a set of intermediate key/value

More information

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com Lambda Architecture Near Real-Time Big Data Analytics Using Hadoop January 2015 Contents Overview... 3 Lambda Architecture: A Quick Introduction... 4 Batch Layer... 4 Serving Layer... 4 Speed Layer...

More information

The Apache Cassandra storage engine

The Apache Cassandra storage engine The Apache Cassandra storage engine Sylvain Lebresne (sylvain@.com) FOSDEM 12, Brussels 1. What is Apache Cassandra 2. Data Model 3. The storage engine 1. What is Apache Cassandra 2. Data Model 3. The

More information

A Deduplication File System & Course Review

A Deduplication File System & Course Review A Deduplication File System & Course Review Kai Li 12/13/12 Topics A Deduplication File System Review 12/13/12 2 Traditional Data Center Storage Hierarchy Clients Network Server SAN Storage Remote mirror

More information

Xiaoming Gao Hui Li Thilina Gunarathne

Xiaoming Gao Hui Li Thilina Gunarathne Xiaoming Gao Hui Li Thilina Gunarathne Outline HBase and Bigtable Storage HBase Use Cases HBase vs RDBMS Hands-on: Load CSV file to Hbase table with MapReduce Motivation Lots of Semi structured data Horizontal

More information

Hadoop2, Spark Big Data, real time, machine learning & use cases. Cédric Carbone Twitter : @carbone

Hadoop2, Spark Big Data, real time, machine learning & use cases. Cédric Carbone Twitter : @carbone Hadoop2, Spark Big Data, real time, machine learning & use cases Cédric Carbone Twitter : @carbone Agenda Map Reduce Hadoop v1 limits Hadoop v2 and YARN Apache Spark Streaming : Spark vs Storm Machine

More information

Applying Apache Hadoop to NASA s Big Climate Data!

Applying Apache Hadoop to NASA s Big Climate Data! National Aeronautics and Space Administration Applying Apache Hadoop to NASA s Big Climate Data! Use Cases and Lessons Learned! Glenn Tamkin (NASA/CSC)!! Team: John Schnase (NASA/PI), Dan Duffy (NASA/CO),!

More information

Big Data & Scripting Part II Streaming Algorithms

Big Data & Scripting Part II Streaming Algorithms Big Data & Scripting Part II Streaming Algorithms 1, Counting Distinct Elements 2, 3, counting distinct elements problem formalization input: stream of elements o from some universe U e.g. ids from a set

More information

Can the Elephants Handle the NoSQL Onslaught?

Can the Elephants Handle the NoSQL Onslaught? Can the Elephants Handle the NoSQL Onslaught? Avrilia Floratou, Nikhil Teletia David J. DeWitt, Jignesh M. Patel, Donghui Zhang University of Wisconsin-Madison Microsoft Jim Gray Systems Lab Presented

More information

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat ESS event: Big Data in Official Statistics Antonino Virgillito, Istat v erbi v is 1 About me Head of Unit Web and BI Technologies, IT Directorate of Istat Project manager and technical coordinator of Web

More information

Practical Cassandra. Vitalii Tymchyshyn tivv00@gmail.com @tivv00

Practical Cassandra. Vitalii Tymchyshyn tivv00@gmail.com @tivv00 Practical Cassandra NoSQL key-value vs RDBMS why and when Cassandra architecture Cassandra data model Life without joins or HDD space is cheap today Hardware requirements & deployment hints Vitalii Tymchyshyn

More information

Architectural patterns for building real time applications with Apache HBase. Andrew Purtell Committer and PMC, Apache HBase

Architectural patterns for building real time applications with Apache HBase. Andrew Purtell Committer and PMC, Apache HBase Architectural patterns for building real time applications with Apache HBase Andrew Purtell Committer and PMC, Apache HBase Who am I? Distributed systems engineer Principal Architect in the Big Data Platform

More information

Informatica Cloud Connector for SharePoint 2010/2013 User Guide

Informatica Cloud Connector for SharePoint 2010/2013 User Guide Informatica Cloud Connector for SharePoint 2010/2013 User Guide Contents 1. Introduction 3 2. SharePoint Plugin 4 3. Objects / Operation Matrix 4 4. Filter fields 4 5. SharePoint Configuration: 6 6. Data

More information

Big Data Patterns. Ron Bodkin Founder and President, Think Big

Big Data Patterns. Ron Bodkin Founder and President, Think Big Big Data Patterns Ron Bodkin Founder and President, Think Big 1 About Me Ron Bodkin Founder and President, Think Big I have 9 years experience working with Big Data and Hadoop. In 2010, I founded Think

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

CS 2112 Spring 2014. 0 Instructions. Assignment 3 Data Structures and Web Filtering. 0.1 Grading. 0.2 Partners. 0.3 Restrictions

CS 2112 Spring 2014. 0 Instructions. Assignment 3 Data Structures and Web Filtering. 0.1 Grading. 0.2 Partners. 0.3 Restrictions CS 2112 Spring 2014 Assignment 3 Data Structures and Web Filtering Due: March 4, 2014 11:59 PM Implementing spam blacklists and web filters requires matching candidate domain names and URLs very rapidly

More information

Distributed Data Management Summer Semester 2015 TU Kaiserslautern

Distributed Data Management Summer Semester 2015 TU Kaiserslautern Distributed Data Management Summer Semester 2015 TU Kaiserslautern Prof. Dr.-Ing. Sebastian Michel Databases and Information Systems Group (AG DBIS) http://dbis.informatik.uni-kl.de/ Distributed Data Management,

More information

brief contents PART 1 BACKGROUND AND FUNDAMENTALS...1 PART 2 PART 3 BIG DATA PATTERNS...253 PART 4 BEYOND MAPREDUCE...385

brief contents PART 1 BACKGROUND AND FUNDAMENTALS...1 PART 2 PART 3 BIG DATA PATTERNS...253 PART 4 BEYOND MAPREDUCE...385 brief contents PART 1 BACKGROUND AND FUNDAMENTALS...1 1 Hadoop in a heartbeat 3 2 Introduction to YARN 22 PART 2 DATA LOGISTICS...59 3 Data serialization working with text and beyond 61 4 Organizing and

More information

Hadoop@LaTech ATLAS Tier 3

Hadoop@LaTech ATLAS Tier 3 Cerberus Hadoop Hadoop@LaTech ATLAS Tier 3 David Palma DOSAR Louisiana Tech University January 23, 2013 Cerberus Hadoop Outline 1 Introduction Cerberus Hadoop 2 Features Issues Conclusions 3 Cerberus Hadoop

More information

DataStax Enterprise Reference Architecture

DataStax Enterprise Reference Architecture DataStax Enterprise Reference Architecture DataStax Enterprise Reference Architecture 7.8.15 1 Table of Contents ABSTRACT... 3 INTRODUCTION... 3 DATASTAX ENTERPRISE... 3 ARCHITECTURE... 3 OPSCENTER: EASY-

More information

Speeding Up Cloud/Server Applications Using Flash Memory

Speeding Up Cloud/Server Applications Using Flash Memory Speeding Up Cloud/Server Applications Using Flash Memory Sudipta Sengupta Microsoft Research, Redmond, WA, USA Contains work that is joint with B. Debnath (Univ. of Minnesota) and J. Li (Microsoft Research,

More information

A SCALABLE DEDUPLICATION AND GARBAGE COLLECTION ENGINE FOR INCREMENTAL BACKUP

A SCALABLE DEDUPLICATION AND GARBAGE COLLECTION ENGINE FOR INCREMENTAL BACKUP A SCALABLE DEDUPLICATION AND GARBAGE COLLECTION ENGINE FOR INCREMENTAL BACKUP Dilip N Simha (Stony Brook University, NY & ITRI, Taiwan) Maohua Lu (IBM Almaden Research Labs, CA) Tzi-cker Chiueh (Stony

More information

DEXT3: Block Level Inline Deduplication for EXT3 File System

DEXT3: Block Level Inline Deduplication for EXT3 File System DEXT3: Block Level Inline Deduplication for EXT3 File System Amar More M.A.E. Alandi, Pune, India ahmore@comp.maepune.ac.in Zishan Shaikh M.A.E. Alandi, Pune, India zishan366shaikh@gmail.com Vishal Salve

More information

FAQs. This material is built based on. Lambda Architecture. Scaling with a queue. 8/27/2015 Sangmi Pallickara

FAQs. This material is built based on. Lambda Architecture. Scaling with a queue. 8/27/2015 Sangmi Pallickara CS535 Big Data - Fall 2015 W1.B.1 CS535 Big Data - Fall 2015 W1.B.2 CS535 BIG DATA FAQs Wait list Term project topics PART 0. INTRODUCTION 2. A PARADIGM FOR BIG DATA Sangmi Lee Pallickara Computer Science,

More information

Future Prospects of Scalable Cloud Computing

Future Prospects of Scalable Cloud Computing Future Prospects of Scalable Cloud Computing Keijo Heljanko Department of Information and Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 7.3-2012 1/17 Future Cloud Topics Beyond

More information

Accelerating Cassandra Workloads using SanDisk Solid State Drives

Accelerating Cassandra Workloads using SanDisk Solid State Drives WHITE PAPER Accelerating Cassandra Workloads using SanDisk Solid State Drives February 2015 951 SanDisk Drive, Milpitas, CA 95035 2015 SanDIsk Corporation. All rights reserved www.sandisk.com Table of

More information

Trends in Enterprise Backup Deduplication

Trends in Enterprise Backup Deduplication Trends in Enterprise Backup Deduplication Shankar Balasubramanian Architect, EMC 1 Outline Protection Storage Deduplication Basics CPU-centric Deduplication: SISL (Stream-Informed Segment Layout) Data

More information

Bigdata High Availability (HA) Architecture

Bigdata High Availability (HA) Architecture Bigdata High Availability (HA) Architecture Introduction This whitepaper describes an HA architecture based on a shared nothing design. Each node uses commodity hardware and has its own local resources

More information

Benchmarking Cassandra on Violin

Benchmarking Cassandra on Violin Technical White Paper Report Technical Report Benchmarking Cassandra on Violin Accelerating Cassandra Performance and Reducing Read Latency With Violin Memory Flash-based Storage Arrays Version 1.0 Abstract

More information

Cloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman

Cloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman Cloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman To motivate the Bloom-filter idea, consider a web crawler. It keeps, centrally, a list of all the URL s it has found so far. It

More information

Bigtable is a proven design Underpins 100+ Google services:

Bigtable is a proven design Underpins 100+ Google services: Mastering Massive Data Volumes with Hypertable Doug Judd Talk Outline Overview Architecture Performance Evaluation Case Studies Hypertable Overview Massively Scalable Database Modeled after Google s Bigtable

More information

Oracle Database In- Memory Op4on in Ac4on

Oracle Database In- Memory Op4on in Ac4on Oracle Database In- Memory Op4on in Ac4on Tanel Põder & Kerry Osborne Accenture Enkitec Group h4p:// 1 Tanel Põder Intro: About Consultant, Trainer, Troubleshooter Oracle Database Performance geek Exadata

More information

THE ATLAS DISTRIBUTED DATA MANAGEMENT SYSTEM & DATABASES

THE ATLAS DISTRIBUTED DATA MANAGEMENT SYSTEM & DATABASES THE ATLAS DISTRIBUTED DATA MANAGEMENT SYSTEM & DATABASES Vincent Garonne, Mario Lassnig, Martin Barisits, Thomas Beermann, Ralph Vigne, Cedric Serfon Vincent.Garonne@cern.ch ph-adp-ddm-lab@cern.ch XLDB

More information

Introduction to Hbase Gkavresis Giorgos 1470

Introduction to Hbase Gkavresis Giorgos 1470 Introduction to Hbase Gkavresis Giorgos 1470 Agenda What is Hbase Installation About RDBMS Overview of Hbase Why Hbase instead of RDBMS Architecture of Hbase Hbase interface Summarise What is Hbase Hbase

More information

Cassandra. Jonathan Ellis

Cassandra. Jonathan Ellis Cassandra Jonathan Ellis Motivation Scaling reads to a relational database is hard Scaling writes to a relational database is virtually impossible and when you do, it usually isn't relational anymore The

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

W I S E. SQL Server 2008/2008 R2 Advanced DBA Performance & WISE LTD.

W I S E. SQL Server 2008/2008 R2 Advanced DBA Performance & WISE LTD. SQL Server 2008/2008 R2 Advanced DBA Performance & Tuning COURSE CODE: COURSE TITLE: AUDIENCE: SQSDPT SQL Server 2008/2008 R2 Advanced DBA Performance & Tuning SQL Server DBAs, capacity planners and system

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

Cassandra A Decentralized, Structured Storage System

Cassandra A Decentralized, Structured Storage System Cassandra A Decentralized, Structured Storage System Avinash Lakshman and Prashant Malik Facebook Published: April 2010, Volume 44, Issue 2 Communications of the ACM http://dl.acm.org/citation.cfm?id=1773922

More information

SMALL INDEX LARGE INDEX (SILT)

SMALL INDEX LARGE INDEX (SILT) Wayne State University ECE 7650: Scalable and Secure Internet Services and Architecture SMALL INDEX LARGE INDEX (SILT) A Memory Efficient High Performance Key Value Store QA REPORT Instructor: Dr. Song

More information

Probabilistic Deduplication for Cluster-Based Storage Systems

Probabilistic Deduplication for Cluster-Based Storage Systems Probabilistic Deduplication for Cluster-Based Storage Systems Davide Frey, Anne-Marie Kermarrec, Konstantinos Kloudas INRIA Rennes, France Motivation Volume of data stored increases exponentially. Provided

More information

Open source large scale distributed data management with Google s MapReduce and Bigtable

Open source large scale distributed data management with Google s MapReduce and Bigtable Open source large scale distributed data management with Google s MapReduce and Bigtable Ioannis Konstantinou Email: ikons@cslab.ece.ntua.gr Web: http://www.cslab.ntua.gr/~ikons Computing Systems Laboratory

More information

MADOCA II Data Logging System Using NoSQL Database for SPring-8

MADOCA II Data Logging System Using NoSQL Database for SPring-8 MADOCA II Data Logging System Using NoSQL Database for SPring-8 A.Yamashita and M.Kago SPring-8/JASRI, Japan NoSQL WED3O03 OR: How I Learned to Stop Worrying and Love Cassandra Outline SPring-8 logging

More information

Cuckoo Filter: Practically Better Than Bloom

Cuckoo Filter: Practically Better Than Bloom Cuckoo Filter: Practically Better Than Bloom Bin Fan, David G. Andersen, Michael Kaminsky, Michael D. Mitzenmacher Carnegie Mellon University, Intel Labs, Harvard University {binfan,dga}@cs.cmu.edu, michael.e.kaminsky@intel.com,

More information

LARGE-SCALE DATA STORAGE APPLICATIONS

LARGE-SCALE DATA STORAGE APPLICATIONS BENCHMARKING AVAILABILITY AND FAILOVER PERFORMANCE OF LARGE-SCALE DATA STORAGE APPLICATIONS Wei Sun and Alexander Pokluda December 2, 2013 Outline Goal and Motivation Overview of Cassandra and Voldemort

More information

Development of nosql data storage for the ATLAS PanDA Monitoring System

Development of nosql data storage for the ATLAS PanDA Monitoring System Development of nosql data storage for the ATLAS PanDA Monitoring System M.Potekhin Brookhaven National Laboratory, Upton, NY11973, USA E-mail: potekhin@bnl.gov Abstract. For several years the PanDA Workload

More information

Large scale processing using Hadoop. Ján Vaňo

Large scale processing using Hadoop. Ján Vaňo Large scale processing using Hadoop Ján Vaňo What is Hadoop? Software platform that lets one easily write and run applications that process vast amounts of data Includes: MapReduce offline computing engine

More information

Case Study : 3 different hadoop cluster deployments

Case Study : 3 different hadoop cluster deployments Case Study : 3 different hadoop cluster deployments Lee moon soo moon@nflabs.com HDFS as a Storage Last 4 years, our HDFS clusters, stored Customer 1500 TB+ data safely served 375,000 TB+ data to customer

More information

Moving From Hadoop to Spark

Moving From Hadoop to Spark + Moving From Hadoop to Spark Sujee Maniyam Founder / Principal @ www.elephantscale.com sujee@elephantscale.com Bay Area ACM meetup (2015-02-23) + HI, Featured in Hadoop Weekly #109 + About Me : Sujee

More information

Department of Computer Science University of Cyprus EPL646 Advanced Topics in Databases. Lecture 14

Department of Computer Science University of Cyprus EPL646 Advanced Topics in Databases. Lecture 14 Department of Computer Science University of Cyprus EPL646 Advanced Topics in Databases Lecture 14 Big Data Management IV: Big-data Infrastructures (Background, IO, From NFS to HFDS) Chapter 14-15: Abideboul

More information

Bloom Filters. Christian Antognini Trivadis AG Zürich, Switzerland

Bloom Filters. Christian Antognini Trivadis AG Zürich, Switzerland Bloom Filters Christian Antognini Trivadis AG Zürich, Switzerland Oracle Database uses bloom filters in various situations. Unfortunately, no information about their usage is available in Oracle documentation.

More information

Workshop on Hadoop with Big Data

Workshop on Hadoop with Big Data Workshop on Hadoop with Big Data Hadoop? Apache Hadoop is an open source framework for distributed storage and processing of large sets of data on commodity hardware. Hadoop enables businesses to quickly

More information

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Storage Systems Autumn 2009 Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Scaling RAID architectures Using traditional RAID architecture does not scale Adding news disk implies

More information

Hinky: Defending Against Text-based Message Spam on Smartphones

Hinky: Defending Against Text-based Message Spam on Smartphones Hinky: Defending Against Text-based Message Spam on Smartphones Abdelkader Lahmadi, Laurent Delosière, Olivier Festor To cite this version: Abdelkader Lahmadi, Laurent Delosière, Olivier Festor. Hinky:

More information

Hadoop implementation of MapReduce computational model. Ján Vaňo

Hadoop implementation of MapReduce computational model. Ján Vaňo Hadoop implementation of MapReduce computational model Ján Vaňo What is MapReduce? A computational model published in a paper by Google in 2004 Based on distributed computation Complements Google s distributed

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

SkimpyStash: RAM Space Skimpy Key-Value Store on Flash-based Storage

SkimpyStash: RAM Space Skimpy Key-Value Store on Flash-based Storage SkimpyStash: RAM Space Skimpy Key-Value Store on Flash-based Storage Biplob Debnath,1 Sudipta Sengupta Jin Li Microsoft Research, Redmond, WA, USA EMC Corporation, Santa Clara, CA, USA ABSTRACT We present

More information

Apache HBase: the Hadoop Database

Apache HBase: the Hadoop Database Apache HBase: the Hadoop Database Yuanru Qian, Andrew Sharp, Jiuling Wang Today we will discuss Apache HBase, the Hadoop Database. HBase is designed specifically for use by Hadoop, and we will define Hadoop

More information

File Management. Chapter 12

File Management. Chapter 12 Chapter 12 File Management File is the basic element of most of the applications, since the input to an application, as well as its output, is usually a file. They also typically outlive the execution

More information

NoSQL: Going Beyond Structured Data and RDBMS

NoSQL: Going Beyond Structured Data and RDBMS NoSQL: Going Beyond Structured Data and RDBMS Scenario Size of data >> disk or memory space on a single machine Store data across many machines Retrieve data from many machines Machine = Commodity machine

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

Firebird meets NoSQL (Apache HBase) Case Study

Firebird meets NoSQL (Apache HBase) Case Study Firebird meets NoSQL (Apache HBase) Case Study Firebird Conference 2011 Luxembourg 25.11.2011 26.11.2011 Thomas Steinmaurer DI +43 7236 3343 896 thomas.steinmaurer@scch.at www.scch.at Michael Zwick DI

More information

STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA. Processing billions of events every day

STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA. Processing billions of events every day STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA Processing billions of events every day Neha Narkhede Co-founder and Head of Engineering @ Stealth Startup Prior to this Lead, Streams Infrastructure

More information

High Throughput Computing on P2P Networks. Carlos Pérez Miguel carlos.perezm@ehu.es

High Throughput Computing on P2P Networks. Carlos Pérez Miguel carlos.perezm@ehu.es High Throughput Computing on P2P Networks Carlos Pérez Miguel carlos.perezm@ehu.es Overview High Throughput Computing Motivation All things distributed: Peer-to-peer Non structured overlays Structured

More information

Hypertable Goes Realtime at Baidu. Yang Dong yangdong01@baidu.com Sherlock Yang(http://weibo.com/u/2624357843)

Hypertable Goes Realtime at Baidu. Yang Dong yangdong01@baidu.com Sherlock Yang(http://weibo.com/u/2624357843) Hypertable Goes Realtime at Baidu Yang Dong yangdong01@baidu.com Sherlock Yang(http://weibo.com/u/2624357843) Agenda Motivation Related Work Model Design Evaluation Conclusion 2 Agenda Motivation Related

More information

Reference Architecture, Requirements, Gaps, Roles

Reference Architecture, Requirements, Gaps, Roles Reference Architecture, Requirements, Gaps, Roles The contents of this document are an excerpt from the brainstorming document M0014. The purpose is to show how a detailed Big Data Reference Architecture

More information

Estimate Performance and Capacity Requirements for Workflow in SharePoint Server 2010

Estimate Performance and Capacity Requirements for Workflow in SharePoint Server 2010 Estimate Performance and Capacity Requirements for Workflow in SharePoint Server 2010 This document is provided as-is. Information and views expressed in this document, including URL and other Internet

More information

This material is built based on, Patterns covered in this class FILTERING PATTERNS. Filtering pattern

This material is built based on, Patterns covered in this class FILTERING PATTERNS. Filtering pattern 2/23/15 CS480 A2 Introduction to Big Data - Spring 2015 1 2/23/15 CS480 A2 Introduction to Big Data - Spring 2015 2 PART 0. INTRODUCTION TO BIG DATA PART 1. MAPREDUCE AND THE NEW SOFTWARE STACK 1. DISTRIBUTED

More information

Putting Apache Kafka to Use!

Putting Apache Kafka to Use! Putting Apache Kafka to Use! Building a Real-time Data Platform for Event Streams! JAY KREPS, CONFLUENT! A Couple of Themes! Theme 1: Rise of Events! Theme 2: Immutability Everywhere! Level! Example! Immutable

More information

Chapter 13. Disk Storage, Basic File Structures, and Hashing

Chapter 13. Disk Storage, Basic File Structures, and Hashing Chapter 13 Disk Storage, Basic File Structures, and Hashing Chapter Outline Disk Storage Devices Files of Records Operations on Files Unordered Files Ordered Files Hashed Files Dynamic and Extendible Hashing

More information

Amazon Cloud Storage Options

Amazon Cloud Storage Options Amazon Cloud Storage Options Table of Contents 1. Overview of AWS Storage Options 02 2. Why you should use the AWS Storage 02 3. How to get Data into the AWS.03 4. Types of AWS Storage Options.03 5. Object

More information

A client side persistent block cache for the data center. Vault Boston 2015 - Luis Pabón - Red Hat

A client side persistent block cache for the data center. Vault Boston 2015 - Luis Pabón - Red Hat PBLCACHE A client side persistent block cache for the data center Vault Boston 2015 - Luis Pabón - Red Hat ABOUT ME LUIS PABÓN Principal Software Engineer, Red Hat Storage IRC, GitHub: lpabon QUESTIONS:

More information

Big Table A Distributed Storage System For Data

Big Table A Distributed Storage System For Data Big Table A Distributed Storage System For Data OSDI 2006 Fay Chang, Jeffrey Dean, Sanjay Ghemawat et.al. Presented by Rahul Malviya Why BigTable? Lots of (semi-)structured data at Google - - URLs: Contents,

More information

CS2510 Computer Operating Systems

CS2510 Computer Operating Systems CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction

More information

CS2510 Computer Operating Systems

CS2510 Computer Operating Systems CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction

More information

Big Data on AWS. Services Overview. Bernie Nallamotu Principle Solutions Architect

Big Data on AWS. Services Overview. Bernie Nallamotu Principle Solutions Architect on AWS Services Overview Bernie Nallamotu Principle Solutions Architect \ So what is it? When your data sets become so large that you have to start innovating around how to collect, store, organize, analyze

More information

Scalable Prefix Matching for Internet Packet Forwarding

Scalable Prefix Matching for Internet Packet Forwarding Scalable Prefix Matching for Internet Packet Forwarding Marcel Waldvogel Computer Engineering and Networks Laboratory Institut für Technische Informatik und Kommunikationsnetze Background Internet growth

More information

Real Time Analytics for Big Data. NtiSh Nati Shalom @natishalom

Real Time Analytics for Big Data. NtiSh Nati Shalom @natishalom Real Time Analytics for Big Data A Twitter Inspired Case Study NtiSh Nati Shalom @natishalom Big Data Predictions Overthe next few years we'll see the adoption of scalable frameworks and platforms for

More information

Intro to Map/Reduce a.k.a. Hadoop

Intro to Map/Reduce a.k.a. Hadoop Intro to Map/Reduce a.k.a. Hadoop Based on: Mining of Massive Datasets by Ra jaraman and Ullman, Cambridge University Press, 2011 Data Mining for the masses by North, Global Text Project, 2012 Slides by

More information

Advanced Oracle SQL Tuning

Advanced Oracle SQL Tuning Advanced Oracle SQL Tuning Seminar content technical details 1) Understanding Execution Plans In this part you will learn how exactly Oracle executes SQL execution plans. Instead of describing on PowerPoint

More information