Dr. Chuck Cartledge. 14 Sept. 2015

Size: px
Start display at page:

Download "Dr. Chuck Cartledge. 14 Sept. 2015"

Transcription

1 CS-695 NoSQL Database Riak (part 2 of 2) Dr. Chuck Cartledge 14 Sept /21

2 Table of contents I 5 Summary 1 Miscellanea 2 Assignment #2 3 Extensions 4 Break 6 Conclusion 7 References 8 Backup slides 2/21

3 Corrections and additions since last lecture. Be sure to look at assignment #02. 3/21

4 Words of explanation. The full text is available at: ~ccartled/ Teaching/2015-Fall/NoSQL/ Assignments/02/ Requires a YouTube video. In general terms: 1 Parse data 2 Create some key/value pair 3 Query database 4 Create movie 4/21

5 Things added to the Dynamo design Fast key/value can seen sort of limiting Connecting keys (links) Querying data Building on distribution (MapReduce) Image from [1]. 5/21

6 Links; or how to get from one key to another Links Links are exactly like links in web pages. Links have a name (tag) Links are unidirectional (i.e., there is a source and a target) Links are created when a node is created A node can have multiple links Links are sort of like edges in a graph. They are not hyper-edges. 6/21

7 Links; or how to get from one key to another Creating a link It all happens in an HTTP header. Link: <target bucket key>; riaktag=somethingmeannigfull Like: Link: </riak/bucket/key>; riaktag="whatever" A link is created from this source bucket key to the target bucket key. For example: curl -X PUT -H "Content-Type: application/json" -H "Link: </riak/animals/polly>; riaktag="contains" -d {"room" : 101} A link is created from cages/1 (source) to animals/polly (target). Link is unidirectional, so target has no knowledge of linkage. 7/21

8 Links; or how to get from one key to another Walking links Once unidirectional links are created, they can be walked, and the Riak graph can be traversed. curl URL to key/,, Where,, are bucket name, and link name respectively (may be blank). Where is 0, 1, or blank where 0 is last link only, 1 is all intermediate links, and blank is current link curl Returns links that are attached to the animals bucket. 8/21

9 Break time. Take about 10 minutes. 9/21

10 Riak s implementation of MapReduce Its not the same as Hadoop s MapReduce Somethings are the same: Application moved to data Map functions are run in parallel Somethings are different: No Map output sort No Map output shuffle Limited language support No STDIN, STDOUT support See Daniel Reverri s slide show at: 10/21

11 Searching Riak values Riak can organize values Requires changes to the default installation: Based on Lucene 1 search technology Enable searching in /etc/riak/riak.conf Change bucket properties curl curl curl -X PUT -H "Content-Type: application/json" -d {"props":{"precommit": [{"mod": "riak search kv hook","fun":"precommit"}]}} /21

12 Searching Riak values Search operators and terms: 2 Fields: title:"the Right Way" AND text:go Wildcards: te?t test* te* Fuzzy: roam~ roam~0.8 Proximity: jakarta apache ~10 Range: mod date:[ TO ] title:{aida TO Carmen} Booleans: AND and OR: "jakarta apache" OR jakarta "jakarta apache" AND "Apache Lucene" NOT: "jakarta apache" NOT "Apache Lucene" Grouping: (jakarta OR apache) AND website /queryparsersyntax.html 12/21

13 Searching Riak values Example searches (1 of 2) curl urltolocalhost/solr/bucket/select?q=query curl q=breed:shepherd 13/21

14 Searching Riak values Example searches (2 of 2) Depends on configuring Riak as per backup slides 3 : curl "$RIAK HOST/search/query/famousAlso? wt=json&q=leader b:true" curl "$RIAK HOST/search/query/famousAlso? wt=json&q=age i:%5b30%20to%20*%5d" curl "$RIAK HOST/search/query/famousAlso? wt=json&q=name s:lion*" /21

15 Searching Riak values JSON field naming requirements. The default search and indexer has special suffix requirements for JSON field names. Searchable fields must end in s Indexable fields must end in i Boolean fields must end in b Among lots of others. Image from [3]. The complete schema is fairly long default schema.xml 15/21

16 Strengths and weaknesses Good and not so good Strengths Designed for Internet scale operations Removes single points of failure Maximizes changing in size Supports about a dozen different languages Extensible (via Erlang) Weaknesses Queryability Complex data structures Some limitations using Javascript vs. Erlang...not every problem needs ACID compliance or the ability to enforce a schema. Redmond and Wilson [2] 16/21

17 Applicabilities Good for, and not so good for Good fit Storing session data User profile and preferences Shopping cart data When indices are not needed Not so good fit Relationships between data Multioperation transactions Query by data Operations on sets 17/21

18 What have we covered? Reviewed assignment #02 Covered Riak extensions Remember Assignment #02 due before next class Next time: CRUDy HBase 18/21

19 References I [1] Vlad Mackevic, Academic writing. why do you need to acknowledge your weaknesses?, [2] Eric Redmond and Jim R Wilson, Seven databases in seven weeks, Pragmatic Bookshelf, [3] Unknown Staff, Hatsu basho 2013, yokozuna dohyo iri, /21

20 These steps are needed to create and activate search capabilities. Setup 1 of 2 1 export RIAK HOST= 2 Created a bucket type called animals: riak-admin bucket-type create animals {"props":{} 3 Activated the new bucket type: riak-admin bucket-type activate animals 4 Created a default search index (search indices are global): curl -XPUT $RIAK HOST/search/index/famousAlso -H Content-Type: application/json -d {"schema":" yz default"} 20/21

21 These steps are needed to create and activate search capabilities. Setup 2 of 2 1 Tied the search index to the bucket type: curl -XPUT $RIAK HOST/types/animals/buckets/cats2/props -H Content-Type: application/json -d {"props":{"search index":"famousalso"}} 2 Populated the bucket type curl -XPUT $RIAK HOST/types/animals/buckets/cats2/keys/panthro -H Content-Type: application/json -d {"name s":"panthro", "age i":36} 3 Queried the index curl "$RIAK HOST/search/query/famousAlso?wt=json&q=leader b:true" 21/21

Dr. Chuck Cartledge. 3 Dec. 2015

Dr. Chuck Cartledge. 3 Dec. 2015 CS-695 NoSQL Database Redis (part 2 of 2) Dr. Chuck Cartledge 3 Dec. 2015 1/14 Table of contents I 1 Miscellanea 2 DB comparisons 3 Assgn. #7 4 Misc. things 6 Course review 7 Conclusion 8 References 5

More information

Dr. Chuck Cartledge. 5 Nov. 2015

Dr. Chuck Cartledge. 5 Nov. 2015 CS-695 NoSQL Database Neo4J (part 1 of 2) Dr. Chuck Cartledge 5 Nov. 2015 1/28 Table of contents I 1 Miscellanea 2 DB comparisons 3 Assgn. #6 4 Historical origins 6 CRUDy stuff 7 Conclusion 8 References

More information

Dr. Chuck Cartledge. 19 Nov. 2015

Dr. Chuck Cartledge. 19 Nov. 2015 CS-695 NoSQL Database Redis (part 1 of 2) Dr. Chuck Cartledge 19 Nov. 2015 1/21 Table of contents I 1 Miscellanea 2 DB comparisons 3 Assgn. #7 4 Historical origins 5 Data model 6 CRUDy stuff 7 Other operations

More information

Dr. Chuck Cartledge. 1 Oct. 2015

Dr. Chuck Cartledge. 1 Oct. 2015 CS-695 NoSQL Database HBase (part 2 of 2) Dr. Chuck Cartledge 1 Oct. 2015 1/14 Table of contents I 1 Miscellanea 2 Assignment #3 3 Extensions 5 Conclusion 6 References 4 Summary 2/14 Corrections and additions

More information

NDBI040: Big Data Management and NoSQL Databases. h p://

NDBI040: Big Data Management and NoSQL Databases. h p:// NDBI040: Big Data Management and NoSQL Databases h p://www.ksi.mff.cuni.cz/~svoboda/courses/171-ndbi040/ Prac cal Class 5 Riak Mar n Svoboda svoboda@ksi.mff.cuni.cz 13. 11. 2017 Charles University in Prague,

More information

Apache Lucene - Query Parser Syntax

Apache Lucene - Query Parser Syntax Peter Carlson Table of contents 1 Overview...2 2 Terms... 2 3 Fields...3 4 Term Modifiers... 3 4.1 Wildcard Searches... 3 4.2 Fuzzy Searches... 4 4.3 Proximity Searches...4 4.4 Range Searches...4 4.5 Boosting

More information

Key-Value Stores: RiakKV

Key-Value Stores: RiakKV B4M36DS2, BE4M36DS2: Database Systems 2 h p://www.ksi.m.cuni.cz/~svoboda/courses/181-b4m36ds2/ Lecture 7 Key-Value Stores: RiakKV Mar n Svoboda mar n.svoboda@fel.cvut.cz 12. 11. 2018 Charles University,

More information

NDBI040: Big Data Management and NoSQL Databases. h p:// svoboda/courses/ ndbi040/

NDBI040: Big Data Management and NoSQL Databases. h p://  svoboda/courses/ ndbi040/ NDBI040: Big Data Management and NoSQL Databases h p://www.ksi.mff.cuni.cz/ svoboda/courses/2016-1-ndbi040/ Prac cal Class 2 Riak Key-Value Store Mar n Svoboda svoboda@ksi.mff.cuni.cz 25. 10. 2016 Charles

More information

Key-Value Stores: RiakKV

Key-Value Stores: RiakKV NDBI040: Big Data Management and NoSQL Databases h p://www.ksi.mff.cuni.cz/ svoboda/courses/2016-1-ndbi040/ Lecture 4 Key-Value Stores: RiakKV Mar n Svoboda svoboda@ksi.mff.cuni.cz 25. 10. 2016 Charles

More information

Key-Value Stores: RiakKV

Key-Value Stores: RiakKV B4M36DS2: Database Systems 2 h p://www.ksi.mff.cuni.cz/ svoboda/courses/2016-1-b4m36ds2/ Lecture 4 Key-Value Stores: RiakKV Mar n Svoboda svoboda@ksi.mff.cuni.cz 24. 10. 2016 Charles University in Prague,

More information

Introduction to Big Data. NoSQL Databases. Instituto Politécnico de Tomar. Ricardo Campos

Introduction to Big Data. NoSQL Databases. Instituto Politécnico de Tomar. Ricardo Campos Instituto Politécnico de Tomar Introduction to Big Data NoSQL Databases Ricardo Campos Mestrado EI-IC Análise e Processamento de Grandes Volumes de Dados Tomar, Portugal, 2016 Part of the slides used in

More information

When does RDBMS representation make sense When do other representations make sense. Prerequisites: CS 450/550 Database Concepts

When does RDBMS representation make sense When do other representations make sense. Prerequisites: CS 450/550 Database Concepts CS-695 NoSQL Databases Fall 2015 Thursdays 1910 2150, Dragas Hall, room 2110 Instructor: Dr. Cartledge http://www.cs.odu.edu/ ccartled/teaching Big data is quadrupling every year!! Everyone is creating

More information

#IoT #BigData. 10/31/14

#IoT #BigData.  10/31/14 #IoT #BigData Seema Jethani @seemaj @basho 1 10/31/14 Why should we care? 2 11/2/14 Source: http://en.wikipedia.org/wiki/internet_of_things Motivation for Specialized Big Data Systems Rate of data capture

More information

Dr. Chuck Cartledge. 18 Feb. 2015

Dr. Chuck Cartledge. 18 Feb. 2015 CS-495/595 Pig Lecture #6 Dr. Chuck Cartledge 18 Feb. 2015 1/18 Table of contents I 1 Miscellanea 2 The Book 3 Chapter 11 4 Conclusion 5 References 2/18 Corrections and additions since last lecture. Completed

More information

Sources. P. J. Sadalage, M Fowler, NoSQL Distilled, Addison Wesley

Sources. P. J. Sadalage, M Fowler, NoSQL Distilled, Addison Wesley Big Data and NoSQL Sources P. J. Sadalage, M Fowler, NoSQL Distilled, Addison Wesley Very short history of DBMSs The seventies: IMS end of the sixties, built for the Apollo program (today: Version 15)

More information

Dr. Chuck Cartledge. 4 Feb. 2015

Dr. Chuck Cartledge. 4 Feb. 2015 CS-495/595 Hadoop (part 1) Lecture #3 Dr. Chuck Cartledge 4 Feb. 2015 1/23 Table of contents I 1 Miscellanea 2 Assignment 3 The Book 4 Chapter 1 5 Chapter 2 7 Break 8 Assignment #2 9 Conclusion 10 References

More information

Databases : Lecture 1 2: Beyond ACID/Relational databases Timothy G. Griffin Lent Term Apologies to Martin Fowler ( NoSQL Distilled )

Databases : Lecture 1 2: Beyond ACID/Relational databases Timothy G. Griffin Lent Term Apologies to Martin Fowler ( NoSQL Distilled ) Databases : Lecture 1 2: Beyond ACID/Relational databases Timothy G. Griffin Lent Term 2016 Rise of Web and cluster-based computing NoSQL Movement Relationships vs. Aggregates Key-value store XML or JSON

More information

relational Relational to Riak Why Move From Relational to Riak? Introduction High Availability Riak At-a-Glance

relational Relational to Riak Why Move From Relational to Riak? Introduction High Availability Riak At-a-Glance WHITEPAPER Relational to Riak relational Introduction This whitepaper looks at why companies choose Riak over a relational database. We focus specifically on availability, scalability, and the / data model.

More information

April Final Quiz COSC MapReduce Programming a) Explain briefly the main ideas and components of the MapReduce programming model.

April 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 information

CISC 7610 Lecture 4 Approaches to multimedia databases. Topics: Document databases Graph databases Metadata Column databases

CISC 7610 Lecture 4 Approaches to multimedia databases. Topics: Document databases Graph databases Metadata Column databases CISC 7610 Lecture 4 Approaches to multimedia databases Topics: Document databases Graph databases Metadata Column databases NoSQL architectures: different tradeoffs for different workloads Already seen:

More information

Non-Relational Databases. Pelle Jakovits

Non-Relational Databases. Pelle Jakovits Non-Relational Databases Pelle Jakovits 25 October 2017 Outline Background Relational model Database scaling The NoSQL Movement CAP Theorem Non-relational data models Key-value Document-oriented Column

More information

Databases : Lectures 11 and 12: Beyond ACID/Relational databases Timothy G. Griffin Lent Term 2013

Databases : Lectures 11 and 12: Beyond ACID/Relational databases Timothy G. Griffin Lent Term 2013 Databases : Lectures 11 and 12: Beyond ACID/Relational databases Timothy G. Griffin Lent Term 2013 Rise of Web and cluster-based computing NoSQL Movement Relationships vs. Aggregates Key-value store XML

More information

Study of NoSQL Database Along With Security Comparison

Study of NoSQL Database Along With Security Comparison Study of NoSQL Database Along With Security Comparison Ankita A. Mall [1], Jwalant B. Baria [2] [1] Student, Computer Engineering Department, Government Engineering College, Modasa, Gujarat, India ank.fetr@gmail.com

More information

Soir 1.4 Enterprise Search Server

Soir 1.4 Enterprise Search Server Soir 1.4 Enterprise Search Server Enhance your search with faceted navigation, result highlighting, fuzzy queries, ranked scoring, and more David Smiley Eric Pugh *- PUBLISHING -J BIRMINGHAM - MUMBAI Preface

More information

DEMYSTIFYING BIG DATA WITH RIAK USE CASES. Martin Schneider Basho Technologies!

DEMYSTIFYING BIG DATA WITH RIAK USE CASES. Martin Schneider Basho Technologies! DEMYSTIFYING BIG DATA WITH RIAK USE CASES Martin Schneider Basho Technologies! Agenda Defining Big Data in Regards to Riak A Series of Trade-Offs Use Cases Q & A About Basho & Riak Basho Technologies is

More information

Overview. * Some History. * What is NoSQL? * Why NoSQL? * RDBMS vs NoSQL. * NoSQL Taxonomy. *TowardsNewSQL

Overview. * Some History. * What is NoSQL? * Why NoSQL? * RDBMS vs NoSQL. * NoSQL Taxonomy. *TowardsNewSQL * Some History * What is NoSQL? * Why NoSQL? * RDBMS vs NoSQL * NoSQL Taxonomy * Towards NewSQL Overview * Some History * What is NoSQL? * Why NoSQL? * RDBMS vs NoSQL * NoSQL Taxonomy *TowardsNewSQL NoSQL

More information

Learning vrealize Orchestrator in action V M U G L A B

Learning vrealize Orchestrator in action V M U G L A B Learning vrealize Orchestrator in action V M U G L A B Lab Learning vrealize Orchestrator in action Code examples If you don t feel like typing the code you can download it from the webserver running on

More information

Rails on HBase. Zachary Pinter and Tony Hillerson RailsConf 2011

Rails on HBase. Zachary Pinter and Tony Hillerson RailsConf 2011 Rails on HBase Zachary Pinter and Tony Hillerson RailsConf 2011 What we will cover What is it? What are the tradeoffs that HBase makes? Why HBase is probably the wrong choice for your app Why HBase might

More information

COSC 416 NoSQL Databases. NoSQL Databases Overview. Dr. Ramon Lawrence University of British Columbia Okanagan

COSC 416 NoSQL Databases. NoSQL Databases Overview. Dr. Ramon Lawrence University of British Columbia Okanagan COSC 416 NoSQL Databases NoSQL Databases Overview Dr. Ramon Lawrence University of British Columbia Okanagan ramon.lawrence@ubc.ca Databases Brought Back to Life!!! Image copyright: www.dragoart.com Image

More information

From Relational to Riak

From Relational to Riak www.basho.com From Relational to Riak December 2012 Table of Contents Table of Contents... 1 Introduction... 1 Why Migrate to Riak?... 1 The Requirement of High Availability...1 Minimizing the Cost of

More information

Search Engines and Time Series Databases

Search Engines and Time Series Databases Università degli Studi di Roma Tor Vergata Dipartimento di Ingegneria Civile e Ingegneria Informatica Search Engines and Time Series Databases Corso di Sistemi e Architetture per Big Data A.A. 2017/18

More information

CIB Session 12th NoSQL Databases Structures

CIB Session 12th NoSQL Databases Structures CIB Session 12th NoSQL Databases Structures By: Shahab Safaee & Morteza Zahedi Software Engineering PhD Email: safaee.shx@gmail.com, morteza.zahedi.a@gmail.com cibtrc.ir cibtrc cibtrc 2 Agenda What is

More information

Delving Deep into Hadoop Course Contents Introduction to Hadoop and Architecture

Delving Deep into Hadoop Course Contents Introduction to Hadoop and Architecture Delving Deep into Hadoop Course Contents Introduction to Hadoop and Architecture Hadoop 1.0 Architecture Introduction to Hadoop & Big Data Hadoop Evolution Hadoop Architecture Networking Concepts Use cases

More information

Jargons, Concepts, Scope and Systems. Key Value Stores, Document Stores, Extensible Record Stores. Overview of different scalable relational systems

Jargons, Concepts, Scope and Systems. Key Value Stores, Document Stores, Extensible Record Stores. Overview of different scalable relational systems Jargons, Concepts, Scope and Systems Key Value Stores, Document Stores, Extensible Record Stores Overview of different scalable relational systems Examples of different Data stores Predictions, Comparisons

More information

Cassandra, MongoDB, and HBase. Cassandra, MongoDB, and HBase. I have chosen these three due to their recent

Cassandra, MongoDB, and HBase. Cassandra, MongoDB, and HBase. I have chosen these three due to their recent Tanton Jeppson CS 401R Lab 3 Cassandra, MongoDB, and HBase Introduction For my report I have chosen to take a deeper look at 3 NoSQL database systems: Cassandra, MongoDB, and HBase. I have chosen these

More information

LucidWorks: Searching with curl October 1, 2012

LucidWorks: Searching with curl October 1, 2012 LucidWorks: Searching with curl October 1, 2012 1. Module name: LucidWorks: Searching with curl 2. Scope: Utilizing curl and the Query admin to search documents 3. Learning objectives Students will be

More information

EPL660: Information Retrieval and Search Engines Lab 3

EPL660: Information Retrieval and Search Engines Lab 3 EPL660: Information Retrieval and Search Engines Lab 3 Παύλος Αντωνίου Γραφείο: B109, ΘΕΕ01 University of Cyprus Department of Computer Science Apache Solr Popular, fast, open-source search platform built

More information

Introduction to BigData, Hadoop:-

Introduction to BigData, Hadoop:- Introduction to BigData, Hadoop:- Big Data Introduction: Hadoop Introduction What is Hadoop? Why Hadoop? Hadoop History. Different types of Components in Hadoop? HDFS, MapReduce, PIG, Hive, SQOOP, HBASE,

More information

Distributed Non-Relational Databases. Pelle Jakovits

Distributed Non-Relational Databases. Pelle Jakovits Distributed Non-Relational Databases Pelle Jakovits Tartu, 7 December 2018 Outline Relational model NoSQL Movement Non-relational data models Key-value Document-oriented Column family Graph Non-relational

More information

MapReduce Algorithms

MapReduce Algorithms Large-scale data processing on the Cloud Lecture 3 MapReduce Algorithms Satish Srirama Some material adapted from slides by Jimmy Lin, 2008 (licensed under Creation Commons Attribution 3.0 License) Outline

More information

Intro To Big Data. John Urbanic Parallel Computing Scientist Pittsburgh Supercomputing Center. Copyright 2017

Intro To Big Data. John Urbanic Parallel Computing Scientist Pittsburgh Supercomputing Center. Copyright 2017 Intro To Big Data John Urbanic Parallel Computing Scientist Pittsburgh Supercomputing Center Copyright 2017 Big data is a broad term for data sets so large or complex that traditional data processing applications

More information

Eric Redmond. Seven Databases in Seven Weeks. Pragmatic Press Out by the end of Summer

Eric Redmond. Seven Databases in Seven Weeks. Pragmatic Press Out by the end of Summer Eric Redmond Seven Databases in Seven Weeks Pragmatic Press Out by the end of Summer Complexity is a symptom of confusion, not a cause. -- Jeff Hawkins (of Palm Pilot fame) Database a system intended

More information

Web Engineering (CC 552)

Web Engineering (CC 552) Web Engineering (CC 552) Introduction Dr. Mohamed Magdy mohamedmagdy@gmail.com Room 405 (CCIT) Course Goals n A general understanding of the fundamentals of the Internet programming n Knowledge and experience

More information

CISC 7610 Lecture 4 Approaches to multimedia databases. Topics: Graph databases Neo4j syntax and examples Document databases

CISC 7610 Lecture 4 Approaches to multimedia databases. Topics: Graph databases Neo4j syntax and examples Document databases CISC 7610 Lecture 4 Approaches to multimedia databases Topics: Graph databases Neo4j syntax and examples Document databases NoSQL architectures: different tradeoffs for different workloads Already seen:

More information

CISC 7610 Lecture 2b The beginnings of NoSQL

CISC 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 information

CS / Cloud Computing. Recitation 3 September 9 th & 11 th, 2014

CS / Cloud Computing. Recitation 3 September 9 th & 11 th, 2014 CS15-319 / 15-619 Cloud Computing Recitation 3 September 9 th & 11 th, 2014 Overview Last Week s Reflection --Project 1.1, Quiz 1, Unit 1 This Week s Schedule --Unit2 (module 3 & 4), Project 1.2 Questions

More information

Dr. Chuck Cartledge. 4 Mar. 2015

Dr. Chuck Cartledge. 4 Mar. 2015 CS-495/595 Pig (part 2) Lecture #8 Dr. Chuck Cartledge 4 Mar. 2015 1/23 Table of contents I 1 Miscellanea 2 The Book 3 Chapter 11 4 Assignment #3. 5 Project 6 Conclusion 7 References 2/23 Corrections and

More information

CSE 336. Introduction to Programming. for Electronic Commerce. Why You Need CSE336

CSE 336. Introduction to Programming. for Electronic Commerce. Why You Need CSE336 CSE 336 Introduction to Programming for Electronic Commerce Why You Need CSE336 Concepts like bits and bytes, domain names, ISPs, IPAs, RPCs, P2P protocols, infinite loops, and cloud computing are strictly

More information

LAB 7: Search engine: Apache Nutch + Solr + Lucene

LAB 7: Search engine: Apache Nutch + Solr + Lucene LAB 7: Search engine: Apache Nutch + Solr + Lucene Apache Nutch Apache Lucene Apache Solr Crawler + indexer (mainly crawler) indexer + searcher indexer + searcher Lucene vs. Solr? Lucene = library, more

More information

Big Data Management and NoSQL Databases

Big Data Management and NoSQL Databases NDBI040 Big Data Management and NoSQL Databases Lecture 1. Introduction Doc. RNDr. Irena Holubova, Ph.D. holubova@ksi.mff.cuni.cz http://www.ksi.mff.cuni.cz/~holubova/ndbi040/ What is Big Data? buzzword?

More information

Dr. Chuck Cartledge. 11 Feb. 2015

Dr. Chuck Cartledge. 11 Feb. 2015 CS-495/595 Hadoop (part 2) Lecture #5 Dr. Chuck Cartledge 11 Feb. 2015 1/32 Table of contents I 1 Miscellanea 2 The Book 3 Chapter 3 4 Chapter 4 5 Chapter 6 6 Chapter 8 7 Break 8 Assignment #2 9 Exam 10

More information

Text search. CSE 392, Computers Playing Jeopardy!, Fall

Text search. CSE 392, Computers Playing Jeopardy!, Fall Text search CSE 392, Computers Playing Jeopardy!, Fall 2011 Stony Brook University http://www.cs.stonybrook.edu/~cse392 1 Today 2 parts: theoretical: costs of searching substrings, data structures for

More information

Astra Schedule User Guide - Reports

Astra Schedule User Guide - Reports California State University Long Beach Astra Schedule User Guide - Reports Run reports, see room usage, and room information Revised 11/04/2016 Contents Astra Schedule User Guide - Reports... 1 Overview...

More information

Dr. Chuck Cartledge. 18 Mar. 2015

Dr. Chuck Cartledge. 18 Mar. 2015 CS-495/595 Hive Lecture #9 Dr. Chuck Cartledge 18 Mar. 2015 1/25 Table of contents I 1 Miscellanea 2 Assignment #3 3 The Book 4 Chapter 12 6 Project 7 Conclusion 8 References 5 Break 2/25 Corrections and

More information

CSE 344 Final Review. August 16 th

CSE 344 Final Review. August 16 th CSE 344 Final Review August 16 th Final In class on Friday One sheet of notes, front and back cost formulas also provided Practice exam on web site Good luck! Primary Topics Parallel DBs parallel join

More information

Document stores using CouchDB

Document stores using CouchDB 2018 Document stores using CouchDB ADVANCED DATABASE PROJECT APARNA KHIRE, MINGRUI DONG aparna.khire@vub.be, mingdong@ulb.ac.be 1 Table of Contents 1. Introduction... 3 2. Background... 3 2.1 NoSQL Database...

More information

Release Presentation. ODS Web Services Version Open Data Services Via Web Services. Release Date: 2014/09/30

Release Presentation. ODS Web Services Version Open Data Services Via Web Services. Release Date: 2014/09/30 Release Presentation ODS Web Services Version 1.1.1 Open Data Services Via Web Services Release Date: 2014/09/30 Deliverables The document represents a companion standard recommendation for interacting

More information

Microsoft. Exam Questions Perform Data Engineering on Microsoft Azure HDInsight (beta) Version:Demo

Microsoft. Exam Questions Perform Data Engineering on Microsoft Azure HDInsight (beta) Version:Demo Microsoft Exam Questions 70-775 Perform Data Engineering on Microsoft Azure HDInsight (beta) Version:Demo NEW QUESTION 1 You have an Azure HDInsight cluster. You need to store data in a file format that

More information

NoSQL systems. Lecture 21 (optional) Instructor: Sudeepa Roy. CompSci 516 Data Intensive Computing Systems

NoSQL systems. Lecture 21 (optional) Instructor: Sudeepa Roy. CompSci 516 Data Intensive Computing Systems CompSci 516 Data Intensive Computing Systems Lecture 21 (optional) NoSQL systems Instructor: Sudeepa Roy Duke CS, Spring 2016 CompSci 516: Data Intensive Computing Systems 1 Key- Value Stores Duke CS,

More information

Map Reduce and Design Patterns Lecture 4

Map Reduce and Design Patterns Lecture 4 Map Reduce and Design Patterns Lecture 4 Fang Yu Software Security Lab. Department of Management Information Systems College of Commerce, National Chengchi University http://soslab.nccu.edu.tw Cloud Computation,

More information

Review - Relational Model Concepts

Review - Relational Model Concepts Lecture 25 Overview Last Lecture Query optimisation/query execution strategies This Lecture Non-relational data models Source: web pages, textbook chapters 20-22 Next Lecture Revision Review - Relational

More information

Hadoop Online Training

Hadoop Online Training Hadoop Online Training IQ training facility offers Hadoop Online Training. Our Hadoop trainers come with vast work experience and teaching skills. Our Hadoop training online is regarded as the one of the

More information

ADVANCED DATABASES CIS 6930 Dr. Markus Schneider. Group 5 Ajantha Ramineni, Sahil Tiwari, Rishabh Jain, Shivang Gupta

ADVANCED DATABASES CIS 6930 Dr. Markus Schneider. Group 5 Ajantha Ramineni, Sahil Tiwari, Rishabh Jain, Shivang Gupta ADVANCED DATABASES CIS 6930 Dr. Markus Schneider Group 5 Ajantha Ramineni, Sahil Tiwari, Rishabh Jain, Shivang Gupta WHAT IS ELASTIC SEARCH? Elastic Search Elasticsearch is a search engine based on Lucene.

More information

Introduction to Hadoop. High Availability Scaling Advantages and Challenges. Introduction to Big Data

Introduction to Hadoop. High Availability Scaling Advantages and Challenges. Introduction to Big Data Introduction to Hadoop High Availability Scaling Advantages and Challenges Introduction to Big Data What is Big data Big Data opportunities Big Data Challenges Characteristics of Big data Introduction

More information

CSE 544 Principles of Database Management Systems. Magdalena Balazinska Winter 2015 Lecture 14 NoSQL

CSE 544 Principles of Database Management Systems. Magdalena Balazinska Winter 2015 Lecture 14 NoSQL CSE 544 Principles of Database Management Systems Magdalena Balazinska Winter 2015 Lecture 14 NoSQL References Scalable SQL and NoSQL Data Stores, Rick Cattell, SIGMOD Record, December 2010 (Vol. 39, No.

More information

RethinkDB. Niharika Vithala, Deepan Sekar, Aidan Pace, and Chang Xu

RethinkDB. Niharika Vithala, Deepan Sekar, Aidan Pace, and Chang Xu RethinkDB Niharika Vithala, Deepan Sekar, Aidan Pace, and Chang Xu Content Introduction System Features Data Model ReQL Applications Introduction Niharika Vithala What is a NoSQL Database Databases that

More information

Principles of Data Management. Lecture #16 (MapReduce & DFS for Big Data)

Principles of Data Management. Lecture #16 (MapReduce & DFS for Big Data) Principles of Data Management Lecture #16 (MapReduce & DFS for Big Data) Instructor: Mike Carey mjcarey@ics.uci.edu Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Today s News Bulletin

More information

CS639: Data Management for Data Science. Lecture 1: Intro to Data Science and Course Overview. Theodoros Rekatsinas

CS639: Data Management for Data Science. Lecture 1: Intro to Data Science and Course Overview. Theodoros Rekatsinas CS639: Data Management for Data Science Lecture 1: Intro to Data Science and Course Overview Theodoros Rekatsinas 1 2 Big science is data driven. 3 Increasingly many companies see themselves as data driven.

More information

Advanced Databases ( CIS 6930) Fall Instructor: Dr. Markus Schneider. Group 17 Anirudh Sarma Bhaskara Sreeharsha Poluru Ameya Devbhankar

Advanced Databases ( CIS 6930) Fall Instructor: Dr. Markus Schneider. Group 17 Anirudh Sarma Bhaskara Sreeharsha Poluru Ameya Devbhankar Advanced Databases ( CIS 6930) Fall 2016 Instructor: Dr. Markus Schneider Group 17 Anirudh Sarma Bhaskara Sreeharsha Poluru Ameya Devbhankar BEFORE WE BEGIN NOSQL : It is mechanism for storage & retrieval

More information

VK Multimedia Information Systems

VK Multimedia Information Systems VK Multimedia Information Systems Mathias Lux, mlux@itec.uni-klu.ac.at This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Results Exercise 01 Exercise 02 Retrieval

More information

ExtraHop 7.3 ExtraHop Trace REST API Guide

ExtraHop 7.3 ExtraHop Trace REST API Guide ExtraHop 7.3 ExtraHop Trace REST API Guide 2018 ExtraHop Networks, Inc. All rights reserved. This manual in whole or in part, may not be reproduced, translated, or reduced to any machinereadable form without

More information

Administration Guide

Administration Guide NetXposure Core 5.0 Administration Guide Administration Guide Page 2 Chapter 01 Introduction... 5 Industry... 5 Digital Asset Management (DAM)... 5 Dynamic Imaging... 6 Content Management... 6 Customer

More information

Big Data Management and NoSQL Databases

Big Data Management and NoSQL Databases NDBI040 Big Data Management and NoSQL Databases Lecture 5. Key-value stores Doc. RNDr. Irena Holubova, Ph.D. holubova@ksi.mff.cuni.cz http://www.ksi.mff.cuni.cz/~holubova/ndbi040/ Key-value store Basic

More information

Seven Databases In Seven Weeks 2e

Seven Databases In Seven Weeks 2e We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with seven databases in seven

More information

CS November 2018

CS November 2018 Bigtable Highly available distributed storage Distributed Systems 19. Bigtable Built with semi-structured data in mind URLs: content, metadata, links, anchors, page rank User data: preferences, account

More information

Chuck Cartledge, PhD. 24 September 2017

Chuck Cartledge, PhD. 24 September 2017 Introduction Basics Hands-on Q&A Conclusion References Files Big Data: Data Analysis Boot Camp Hadoop and R Chuck Cartledge, PhD 24 September 2017 1/26 Table of contents (1 of 1) 1 Introduction 2 Basics

More information

CS105 Perl: Perl CGI. Nathan Clement 24 Feb 2014

CS105 Perl: Perl CGI. Nathan Clement 24 Feb 2014 CS105 Perl: Perl CGI Nathan Clement 24 Feb 2014 Agenda We will cover some CGI basics, including Perl-specific CGI What is CGI? Server Architecture GET vs POST Preserving State in CGI URL Rewriting, Hidden

More information

Relational Model, Key Constraints

Relational Model, Key Constraints Relational Model, Key Constraints PDBM 6.1 Dr. Chris Mayfield Department of Computer Science James Madison University Jan 23, 2019 What is a data model? Notation for describing data or information Structure

More information

This tutorial helps the professionals aspiring to make a career in Big Data and NoSQL databases, especially the documents store.

This tutorial helps the professionals aspiring to make a career in Big Data and NoSQL databases, especially the documents store. About the Tutorial This tutorial provides a brief knowledge about CouchDB, the procedures to set it up, and the ways to interact with CouchDB server using curl and Futon. It also tells how to create, update

More information

rpaf ktl Pen Apache Solr 3 Enterprise Search Server J community exp<= highlighting, relevancy ranked sorting, and more source publishing""

rpaf ktl Pen Apache Solr 3 Enterprise Search Server J community exp<= highlighting, relevancy ranked sorting, and more source publishing Apache Solr 3 Enterprise Search Server Enhance your search with faceted navigation, result highlighting, relevancy ranked sorting, and more David Smiley Eric Pugh rpaf ktl Pen I I riv IV I J community

More information

Lecture 25 Overview. Last Lecture Query optimisation/query execution strategies

Lecture 25 Overview. Last Lecture Query optimisation/query execution strategies Lecture 25 Overview Last Lecture Query optimisation/query execution strategies This Lecture Non-relational data models Source: web pages, textbook chapters 20-22 Next Lecture Revision COSC344 Lecture 25

More information

SEARCHING BILLIONS OF PRODUCT LOGS IN REAL TIME. Ryan Tabora - Think Big Analytics NoSQL Search Roadshow - June 6, 2013

SEARCHING BILLIONS OF PRODUCT LOGS IN REAL TIME. Ryan Tabora - Think Big Analytics NoSQL Search Roadshow - June 6, 2013 SEARCHING BILLIONS OF PRODUCT LOGS IN REAL TIME Ryan Tabora - Think Big Analytics NoSQL Search Roadshow - June 6, 2013 1 WHO AM I? Ryan Tabora Think Big Analytics - Senior Data Engineer Lover of dachshunds,

More information

LabCollector Web Service API

LabCollector Web Service API LabCollector Web Service API The LabCollector Web Service Application Programming Interface (API) allows third-party applications to interact with LabCollector's database (modules). The API is based on

More information

Big Data Management and NoSQL Databases

Big Data Management and NoSQL Databases NDBI040 Big Data Management and NoSQL Databases Lecture 10. Graph databases Doc. RNDr. Irena Holubova, Ph.D. holubova@ksi.mff.cuni.cz http://www.ksi.mff.cuni.cz/~holubova/ndbi040/ Graph Databases Basic

More information

Technical Deep Dive: Cassandra + Solr. Copyright 2012, Think Big Analy7cs, All Rights Reserved

Technical Deep Dive: Cassandra + Solr. Copyright 2012, Think Big Analy7cs, All Rights Reserved Technical Deep Dive: Cassandra + Solr Confiden7al Business case 2 Super scalable realtime analytics Hadoop is fantastic at performing batch analytics Cassandra is an advanced column family oriented system

More information

OPEN SOURCE DB SYSTEMS TYPES OF DBMS

OPEN SOURCE DB SYSTEMS TYPES OF DBMS OPEN SOURCE DB SYSTEMS Anna Topol 1 TYPES OF DBMS Relational Key-Value Document-oriented Graph 2 DBMS SELECTION Multi-platform or platform-agnostic Offers persistent storage Fairly well known Actively

More information

NoSQL Databases. Amir H. Payberah. Swedish Institute of Computer Science. April 10, 2014

NoSQL Databases. Amir H. Payberah. Swedish Institute of Computer Science. April 10, 2014 NoSQL Databases Amir H. Payberah Swedish Institute of Computer Science amir@sics.se April 10, 2014 Amir H. Payberah (SICS) NoSQL Databases April 10, 2014 1 / 67 Database and Database Management System

More information

Big Data. Big Data Analyst. Big Data Engineer. Big Data Architect

Big Data. Big Data Analyst. Big Data Engineer. Big Data Architect Big Data Big Data Analyst INTRODUCTION TO BIG DATA ANALYTICS ANALYTICS PROCESSING TECHNIQUES DATA TRANSFORMATION & BATCH PROCESSING REAL TIME (STREAM) DATA PROCESSING Big Data Engineer BIG DATA FOUNDATION

More information

CS 564: DATABASE MANAGEMENT SYSTEMS

CS 564: DATABASE MANAGEMENT SYSTEMS Spring 2017 CS 564: DATABASE MANAGEMENT SYSTEMS 1/17/17 CS 564: Database Management Systems, Jignesh M. Patel 1 Teaching Staff Instructor: Jignesh Patel, Office Hours: Mon, Wed 9:15-10:30AM, CS 4357 Class:

More information

Course Content MongoDB

Course Content MongoDB Course Content MongoDB 1. Course introduction and mongodb Essentials (basics) 2. Introduction to NoSQL databases What is NoSQL? Why NoSQL? Difference Between RDBMS and NoSQL Databases Benefits of NoSQL

More information

DrRobert N. M. Watson

DrRobert N. M. Watson Distributed systems Lecture 15: Replication, quorums, consistency, CAP, and Amazon/Google case studies DrRobert N. M. Watson 1 Last time General issue of consensus: How to get processes to agree on something

More information

Battle of the Giants Apache Solr 4.0 vs ElasticSearch 0.20 Rafał Kuć sematext.com

Battle of the Giants Apache Solr 4.0 vs ElasticSearch 0.20 Rafał Kuć  sematext.com Battle of the Giants Apache Solr 4.0 vs ElasticSearch 0.20 Rafał Kuć Sematext International @kucrafal @sematext sematext.com Who Am I Solr 3.1 Cookbook author (4.0 inc) Sematext consultant & engineer Solr.pl

More information

Arena: Membership Lists Foundations (Hands On)

Arena: Membership Lists Foundations (Hands On) Arena: Membership Lists Foundations (Hands On) [Course #A118] Presented by: Linda Johnson Shelby Contract Trainer 2017 Shelby Systems, Inc. Other brand and product names are trademarks or registered trademarks

More information

Databases and Big Data Today. CS634 Class 22

Databases and Big Data Today. CS634 Class 22 Databases and Big Data Today CS634 Class 22 Current types of Databases SQL using relational tables: still very important! NoSQL, i.e., not using relational tables: term NoSQL popular since about 2007.

More information

NOSQL DATABASE SYSTEMS: DECISION GUIDANCE AND TRENDS. Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe

NOSQL DATABASE SYSTEMS: DECISION GUIDANCE AND TRENDS. Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe NOSQL DATABASE SYSTEMS: DECISION GUIDANCE AND TRENDS h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe 2017 163 Performance / Benchmarks Traditional database benchmarks

More information

Senior Executive. Training Guide

Senior Executive. Training Guide Senior Executive Training Guide Table of Contents 1 Introduction... 5 1.1 Who is this Guide for?... 5 1.2 Pre-Requisites... 5 1.3 Suggested Exercises... 5 1.4 System Requirements... 5 2 Getting Started...

More information

microsoft

microsoft 70-775.microsoft Number: 70-775 Passing Score: 800 Time Limit: 120 min Exam A QUESTION 1 Note: This question is part of a series of questions that present the same scenario. Each question in the series

More information

Why NoSQL? Why Riak?

Why NoSQL? Why Riak? Why NoSQL? Why Riak? Justin Sheehy justin@basho.com 1 What's all of this NoSQL nonsense? Riak Voldemort HBase MongoDB Neo4j Cassandra CouchDB Membase Redis (and the list goes on...) 2 What went wrong with

More information

User Guide. Creating and Varying Sessional Schedule (Course Convenors, School Admin/Secretaries)

User Guide. Creating and Varying Sessional Schedule (Course Convenors, School Admin/Secretaries) User Guide Creating and Varying Sessional Schedule (Course Convenors, School Admin/Secretaries) Brief Document Description Overview Sessional staff are required to submit their agreed Work Schedule for

More information

HBase vs Neo4j. Technical overview. Name: Vladan Jovičić CR09 Advanced Scalable Data (Fall, 2017) Ecolé Normale Superiuere de Lyon

HBase vs Neo4j. Technical overview. Name: Vladan Jovičić CR09 Advanced Scalable Data (Fall, 2017) Ecolé Normale Superiuere de Lyon HBase vs Neo4j Technical overview Name: Vladan Jovičić CR09 Advanced Scalable Data (Fall, 2017) Ecolé Normale Superiuere de Lyon 12th October 2017 1 Contents 1 Introduction 3 2 Overview of HBase and Neo4j

More information