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												  Introduction to Hbase Schema DesignIntroduction to HBase Schema Design AmANDeeP KHURANA Amandeep Khurana is The number of applications that are being developed to work with large amounts a Solutions Architect at of data has been growing rapidly in the recent past . To support this new breed of Cloudera and works on applications, as well as scaling up old applications, several new data management building solutions using the systems have been developed . Some call this the big data revolution . A lot of these Hadoop stack. He is also a co-author of HBase new systems that are being developed are open source and community driven, in Action. Prior to Cloudera, Amandeep worked deployed at several large companies . Apache HBase [2] is one such system . It is at Amazon Web Services, where he was part an open source distributed database, modeled around Google Bigtable [5] and is of the Elastic MapReduce team and built the becoming an increasingly popular database choice for applications that need fast initial versions of their hosted HBase product. random access to large amounts of data . It is built atop Apache Hadoop [1] and is [email protected] tightly integrated with it . HBase is very different from traditional relational databases like MySQL, Post- greSQL, Oracle, etc . in how it’s architected and the features that it provides to the applications using it . HBase trades off some of these features for scalability and a flexible schema . This also translates into HBase having a very different data model . Designing HBase tables is a different ballgame as compared to relational database systems . I will introduce you to the basics of HBase table design by explaining the data model and build on that by going into the various concepts at play in designing HBase tables through an example .
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												  Apache Cassandra on AWS WhitepaperApache Cassandra on AWS Guidelines and Best Practices January 2016 Amazon Web Services – Apache Cassandra on AWS January 2016 © 2016, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Page 2 of 52 Amazon Web Services – Apache Cassandra on AWS January 2016 Notices 2 Abstract 4 Introduction 4 NoSQL on AWS 5 Cassandra: A Brief Introduction 6 Cassandra: Key Terms and Concepts 6 Write Request Flow 8 Compaction 11 Read Request Flow 11 Cassandra: Resource Requirements 14 Storage and IO Requirements 14 Network Requirements 15 Memory Requirements 15 CPU Requirements 15 Planning Cassandra Clusters on AWS 16 Planning Regions and Availability Zones 16 Planning an Amazon Virtual Private Cloud 18 Planning Elastic Network Interfaces 19 Planning
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												  Apache Cassandra and Apache Spark Integration a Detailed ImplementationApache Cassandra and Apache Spark Integration A detailed implementation Integrated Media Systems Center USC Spring 2015 Supervisor Dr. Cyrus Shahabi Student Name Stripelis Dimitrios 1 Contents 1. Introduction 2. Apache Cassandra Overview 3. Apache Cassandra Production Development 4. Apache Cassandra Running Requirements 5. Apache Cassandra Read/Write Requests using the Python API 6. Types of Cassandra Queries 7. Apache Spark Overview 8. Building the Spark Project 9. Spark Nodes Configuration 10. Building the Spark Cassandra Integration 11. Running the Spark-Cassandra Shell 12. Summary 2 1. Introduction This paper can be used as a reference guide for a detailed technical implementation of Apache Spark v. 1.2.1 and Apache Cassandra v. 2.0.13. The integration of both systems was deployed on Google Cloud servers using the RHEL operating system. The same guidelines can be easily applied to other operating systems (Linux based) as well with insignificant changes. Cluster Requirements: Software Java 1.7+ installed Python 2.7+ installed Ports A number of at least 7 ports in each node of the cluster must be constantly opened. For Apache Cassandra the following ports are the default ones and must be opened securely: 9042 - Cassandra native transport for clients 9160 - Cassandra Port for listening for clients 7000 - Cassandra TCP port for commands and data 7199 - JMX Port Cassandra For Apache Spark any 4 random ports should be also opened and secured, excluding ports 8080 and 4040 which are used by default from apache Spark for creating the Web UI of each application. It is highly advisable that one of the four random ports should be the port 7077, because it is the default port used by the Spark Master listening service.
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												  Index Selection for In-Memory DatabasesInternational Journal of Computer Sciences &&& Engineering Open Access Research Paper Volume-3, Issue-7 E-ISSN: 2347-2693 Index Selection for in-Memory Databases Pratham L. Bajaj 1* and Archana Ghotkar 2 1*,2 Dept. of Computer Engineering, Pune University, India, www.ijcseonline.org Received: Jun/09/2015 Revised: Jun/28/2015 Accepted: July/18/2015 Published: July/30/ 2015 Abstract —Index recommendation is an active research area in query performance tuning and optimization. Designing efficient indexes is paramount to achieving good database and application performance. In association with database engine, index recommendation technique need to adopt for optimal results. Different searching methods used for Index Selection Problem (ISP) on various databases and resemble knapsack problem and traveling salesman. The query optimizer reliably chooses the most effective indexes in the vast majority of cases. Loss function calculated for every column and probability is assign to column. Experimental results presented to evidence of our contributions. Keywords— Query Performance, NPH Analysis, Index Selection Problem provides rules for index selections, data structures and I. INTRODUCTION materialized views. Physical ordering of tuples may vary from index table. In cluster indexing, actual physical Relational Database System is very complex and it is design get change. If particular column is hits frequently continuously evolving from last thirty years. To fetch data then clustering index drastically improves performance. from millions of dataset is time consuming job and new In in-memory databases, memory is divided among searching technologies need to develop. Query databases and indexes. Different data structures are used performance tuning and optimization was an area of for indexes like hash, tree, bitmap, etc.
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												  Apache Hbase, the Scaling Machine Jean-Daniel Cryans Software Engineer at Cloudera @JdcryansApache HBase, the Scaling Machine Jean-Daniel Cryans Software Engineer at Cloudera @jdcryans Tuesday, June 18, 13 Agenda • Introduction to Apache HBase • HBase at StumbleUpon • Overview of other use cases 2 Tuesday, June 18, 13 About le Moi • At Cloudera since October 2012. • At StumbleUpon for 3 years before that. • Committer and PMC member for Apache HBase since 2008. • Living in San Francisco. • From Québec, Canada. 3 Tuesday, June 18, 13 4 Tuesday, June 18, 13 What is Apache HBase Apache HBase is an open source distributed scalable consistent low latency random access non-relational database built on Apache Hadoop 5 Tuesday, June 18, 13 Inspiration: Google BigTable (2006) • Goal: Low latency, consistent, random read/ write access to massive amounts of structured data. • It was the data store for Google’s crawler web table, gmail, analytics, earth, blogger, … 6 Tuesday, June 18, 13 HBase is in Production • Inbox • Storage • Web • Search • Analytics • Monitoring 7 Tuesday, June 18, 13 HBase is in Production • Inbox • Storage • Web • Search • Analytics • Monitoring 7 Tuesday, June 18, 13 HBase is in Production • Inbox • Storage • Web • Search • Analytics • Monitoring 7 Tuesday, June 18, 13 HBase is in Production • Inbox • Storage • Web • Search • Analytics • Monitoring 7 Tuesday, June 18, 13 HBase is Open Source • Apache 2.0 License • A Community project with committers and contributors from diverse organizations: • Facebook, Cloudera, Salesforce.com, Huawei, eBay, HortonWorks, Intel, Twitter … • Code license means anyone can modify and use the code. 8 Tuesday, June 18, 13 So why use HBase? 9 Tuesday, June 18, 13 10 Tuesday, June 18, 13 Old School Scaling => • Find a scaling problem.
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												  Amazon Aurora Mysql Database Administrator's HandbookAmazon Aurora MySQL Database Administrator’s Handbook Connection Management March 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 DNS Endpoints .................................................................................................................... 2 Connection Handling in Aurora MySQL and MySQL ......................................................... 3 Common Misconceptions .................................................................................................... 5 Best Practices ...................................................................................................................... 6 Using Smart Drivers ........................................................................................................
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												  Elasticsearch Update Multiple DocumentsElasticsearch Update Multiple Documents Noah is heaven-sent and shovel meanwhile while self-appointed Jerrome shack and squeeze. Jean-Pierre is adducible: she salvings dynastically and fluidizes her penalty. Gaspar prevaricate conformably while verbal Reynold yip clamorously or decides injunctively. Currently we start with multiple elasticsearch documents can explore the process has a proxy on enter request that succeed or different schemas across multiple data Needed for a has multiple documents that step api is being paired with the clients. Remember that it would update the single document only, not all. Ghar mein ghuskar maarta hai. So based on this I would try adding those values like the following. The update interface only deletes documents by term while delete supports deletion by term and by query. Enforce this limit in your application via a rate limiter. Adding the REST client in your dependencies will drag the entire Elasticsearch milkyway into your JAR Hell. Why is it a chain? When the alias switches to this new index it will be incomplete. Explain a search query. Can You Improve This Article? This separation means that a synchronous API considers only synchronous entity callbacks and a reactive implementation considers only reactive entity callbacks. Sets the child document index. For example, it is possible that another process might have already updated the same document in between the get and indexing phases of the update. Some store modules may define their own result wrapper types. It is an error to index to an alias which points to more than one index. The following is a simple List.
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												  15 Minutes Introduction to ELK (Elastic Search,Logstash,Kibana) Kickstarter SeriesKickStarter Series 15 Minutes Introduction to ELK 15 Minutes Introduction to ELK (Elastic Search,LogStash,Kibana) KickStarter Series Karun Subramanian www.karunsubramanian.com ©Karun Subramanian 1 KickStarter Series 15 Minutes Introduction to ELK Contents What is ELK and why is all the hoopla? ................................................................................................................................................ 3 The plumbing – How does it work? ...................................................................................................................................................... 4 How can I get started, really? ............................................................................................................................................................... 5 Installation process ........................................................................................................................................................................... 5 The Search ............................................................................................................................................................................................ 7 Most important Plugins ........................................................................................................................................................................ 8 Marvel ..............................................................................................................................................................................................
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												  Final Report CS 5604: Information Storage and RetrievalFinal Report CS 5604: Information Storage and Retrieval Solr Team Abhinav Kumar, Anand Bangad, Jeff Robertson, Mohit Garg, Shreyas Ramesh, Siyu Mi, Xinyue Wang, Yu Wang January 16, 2018 Instructed by Professor Edward A. Fox Virginia Polytechnic Institute and State University Blacksburg, VA 24061 1 Abstract The Digital Library Research Laboratory (DLRL) has collected over 1.5 billion tweets and millions of webpages for the Integrated Digital Event Archiving and Library (IDEAL) and Global Event Trend Archive Research (GETAR) projects [6]. We are using a 21 node Cloudera Hadoop cluster to store and retrieve this information. One goal of this project is to expand the data collection to include more web archives and geospatial data beyond what previously had been collected. Another important part in this project is optimizing the current system to analyze and allow access to the new data. To accomplish these goals, this project is separated into 6 parts with corresponding teams: Classification (CLA), Collection Management Tweets (CMT), Collection Management Webpages (CMW), Clustering and Topic Analysis (CTA), Front-end (FE), and SOLR. This report describes the work completed by the SOLR team which improves the current searching and storage system. We include the general architecture and an overview of the current system. We present the part that Solr plays within the whole system with more detail. We talk about our goals, procedures, and conclusions on the improvements we made to the current Solr system. This report also describes how we coordinate with other teams to accomplish the project at a higher level. Additionally, we provide manuals for future readers who might need to replicate our experiments.
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												  Apache Hbase: the Hadoop Database Yuanru Qian, Andrew Sharp, Jiuling WangApache HBase: the Hadoop Database Yuanru Qian, Andrew Sharp, Jiuling Wang 1 Agenda ● Motivation ● Data Model ● The HBase Distributed System ● Data Operations ● Access APIs ● Architecture 2 Motivation ● Hadoop is a framework that supports operations on a large amount of data. ● Hadoop includes the Hadoop Distributed File System (HDFS) ● HDFS does a good job of storing large amounts of data, but lacks quick random read/write capability. ● That’s where Apache HBase comes in. 3 Introduction ● HBase is an open source, sparse, consistent distributed, sorted map modeled after Google’s BigTable. ● Began as a project by Powerset to process massive amounts of data for natural language processing. ● Developed as part of Apache’s Hadoop project and runs on top of Hadoop Distributed File System. 4 Big Picture MapReduce Thrift/REST Your Java Hive/Pig Gateway Application Java Client ZooKeeper HBase HDFS 5 An Example Operation The Job: The Table: A MapReduce job row key column 1 column 2 needs to operate on a series of webpages “com.cnn.world” 13 4.5 matching *.cnn.com “com.cnn.tech” 46 7.8 “com.cnn.money” 44 1.2 6 The HBase Data Model 7 Data Model, Groups of Tables RDBMS Apache HBase database namespace table table 8 Data Model, Single Table RDBMS table col1 col2 col3 col4 row1 row2 row3 9 Data Model, Single Table Apache HBase columns are table fam1 fam2 grouped into fam1:col1 fam1:col2 fam2:col1 fam2:col2 Column Families row1 row2 row3 10 Sparse example Row Key fam1:contents fam1:anchor “com.cnn.www” contents:html = “<html>...” contents:html = “<html>...”
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												  Innodb 1.1 for Mysql 5.5 User's Guide Innodb 1.1 for Mysql 5.5 User's GuideInnoDB 1.1 for MySQL 5.5 User's Guide InnoDB 1.1 for MySQL 5.5 User's Guide Abstract This is the User's Guide for the InnoDB storage engine 1.1 for MySQL 5.5. Beginning with MySQL version 5.1, it is possible to swap out one version of the InnoDB storage engine and use another (the “plugin”). This manual documents the latest InnoDB plugin, version 1.1, which works with MySQL 5.5 and features cutting-edge improvements in performance and scalability. This User's Guide documents the procedures and features that are specific to the InnoDB storage engine 1.1 for MySQL 5.5. It supplements the general InnoDB information in the MySQL Reference Manual. Because InnoDB 1.1 is integrated with MySQL 5.5, it is generally available (GA) and production-ready. WARNING: Because the InnoDB storage engine 1.0 and above introduces a new file format, restrictions apply to the use of a database created with the InnoDB storage engine 1.0 and above, with earlier versions of InnoDB, when using mysqldump or MySQL replication and if you use the older InnoDB Hot Backup product rather than the newer MySQL Enterprise Backup product. See Section 1.4, “Compatibility Considerations for Downgrade and Backup”. For legal information, see the Legal Notices. Document generated on: 2014-01-30 (revision: 37565) Table of Contents Preface and Legal Notices .................................................................................................................. v 1 Introduction to InnoDB 1.1 ............................................................................................................... 1 1.1 Features of the InnoDB Storage Engine ................................................................................ 1 1.2 Obtaining and Installing the InnoDB Storage Engine ............................................................... 3 1.3 Viewing the InnoDB Storage Engine Version Number ............................................................
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												  Red Hat Openstack Platform 9 Red Hat Openstack Platform Operational ToolsRed Hat OpenStack Platform 9 Red Hat OpenStack Platform Operational Tools Centralized Logging and Monitoring of an OpenStack Environment OpenStack Team Red Hat OpenStack Platform 9 Red Hat OpenStack Platform Operational Tools Centralized Logging and Monitoring of an OpenStack Environment OpenStack Team [email protected] Legal Notice Copyright © 2017 Red Hat, Inc. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/ . In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat Software Collections is not formally related to or endorsed by the official Joyent Node.js open source or commercial project.