DOCSLIB.ORG

Apache Solr 3 1 Cookbook.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Apache Solr 3 1 Cookbook.Pdf Apache Solr 3.1 Cookbook Over 100 recipes to discover new ways to work with Apache's Enterprise Search Server Rafał Kuć BIRMINGHAM - MUMBAI Apache Solr 3.1 Cookbook Copyright © 2011 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: July 2011 Production Reference: 1140711 Published by Packt Publishing Ltd. 32 Lincoln Road Olton Birmingham, B27 6PA, UK. ISBN 978-1-849512-18-3 www.packtpub.com Cover Image by Rakesh Shejwal ([email protected]) Credits Author Project Coordinator Rafał Kuć Srimoyee Ghoshal Reviewers Proofreader Ravindra Bharathi Samantha Lyon Juan Grande Indexer Acquisition Editor Hemangini Bari Sarah Cullington Production Coordinators Development Editor Adline Swetha Jesuthas Meeta Rajani Kruthika Bangera Technical Editors Cover Work Manasi Poonthottam Kruthika Bangera Sakina Kaydawala Copy Editors Kriti Sharma Leonard D'Silva About the Author Rafał Kuć is a born team leader and software developer. Right now, he holds the position of Software Architect, and Solr and Lucene specialist. He has eight years of experience in various software branches, from banking software to the e-commerce products. He is mainly focused on Java, but is open to every tool and programming language that will make the achievement of his goal easier and faster. Rafał is also one of the founders of the solr.pl site where he tries to share his knowledge and help people with their problems with Solr and Lucene. Rafał began his journey with Lucene in 2002 and it wasn't love at first sight. When he came back to Lucene in late 2003, he revised his thoughts about the framework and saw the potential in search technologies. Then Solr came and that was it. From then, Rafał has concentrated on search technologies and data analysis. Right now, Lucene and Solr are his main points of interest. Many Polish e-commerce sites use Solr deployments that were made with the guidance of Rafał and with the use of tools developed by him. The writing of this book was a big thing for me. It took almost all of my free time for the last six months. Of course, this was my sacrifice and if I were to make that decision today, it would be the same. However, the ones that suffered from this the most was my family—my wife Agnes and our son Philip. Without their patience for the always busy husband and father, the writing of this book wouldn't have been possible. Furthermore, the last drafts and corrections were made, when we were waiting for our daughter to be born, so it was even harder for them. I would like to thank all the people involved in creating, developing, and maintaining Lucene and Solr projects for their work and passion. Without them, this book wouldn't be written. Last, but not least, I would like to thank all the people who made the writing of this book possible, and who listened to my endless doubts and thoughts, especially Marek Rogoziński. Your point-of-view helped me a lot. I'm not able to mention you all, but you all know who you are. Once again, thank you. About the Reviewers Ravindra Bharathi holds a master's degree in Structural Engineering and started his career in the construction industry. He took up software development in 2001 and has since worked in domains such as education, digital media marketing/advertising, enterprise search, and energy management systems. He has a keen interest in search- based applications that involve data visualization, mashups, and dashboards. He blogs at http://ravindrabharathi.blogspot.com. Juan Grande is an Informatics Engineering student at Universidad de Buenos Aires, Argentina. He has worked with Java-related technologies since 2005, when he started as web applications developer, gaining experience in technologies such as Struts, Hibernate, and Spring. Since 2010, he has worked as search consultant for Plug Tree. He also works as an undergraduate teaching assistant in programming-related subjects since 2006. His other topic of interest is experimental physics, where he has a paper published. I want to acknowledge Plug Tree for letting me get involved in this project, and specially Tomás Fernández Löbbe for introducing me to the fascinating world of Solr. www.PacktPub.com Support files, eBooks, discount offers, and more You might want to visit www.PacktPub.com for support files and downloads related to your book. Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.PacktPub.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at [email protected] for more details. At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters and receive exclusive discounts and offers on Packt books and eBooks. http://PacktLib.PacktPub.com Do you need instant solutions to your IT questions? PacktLib is Packt's online digital book library. Here, you can access, read and search across Packt's entire library of books. Why Subscribe? f Fully searchable across every book published by Packt f Copy and paste, print and bookmark content f On demand and accessible via web browser Free Access for Packt account holders If you have an account with Packt at www.PacktPub.com, you can use this to access PacktLib today and view nine entirely free books. Simply use your login credentials for immediate access. Table of Contents Preface 1 Chapter 1: Apache Solr Configuration 5 Introduction 5 Running Solr on Jetty 6 Running Solr on Apache Tomcat 9 Using the Suggester component 11 Handling multiple languages in a single index 15 Indexing fields in a dynamic way 17 Making multilingual data searchable with multicore deployment 19 Solr cache configuration 23 How to fetch and index web pages 28 Getting the most relevant results with early query termination 31 How to set up Extracting Request Handler 33 Chapter 2: Indexing your Data 35 Introduction 35 Indexing data in CSV format 36 Indexing data in XML format 38 Indexing data in JSON format 40 Indexing PDF files 43 Indexing Microsoft Office files 45 Extracting metadata from binary files 47 How to properly configure Data Import Handler with JDBC 49 Indexing data from a database using Data Import Handler 52 How to import data using Data Import Handler and delta query 55 How to use Data Import Handler with URL Data Source 57 How to modify data while importing with Data Import Handler 60 Table of Contents Chapter 3: Analyzing your Text Data 63 Introduction 64 Storing additional information using payloads 64 Eliminating XML and HTML tags from the text 66 Copying the contents of one field to another 68 Changing words to other words 70 Splitting text by camel case 72 Splitting text by whitespace only 74 Making plural words singular, but without stemming 75 Lowercasing the whole string 77 Storing geographical points in the index 78 Stemming your data 81 Preparing text to do efficient trailing wildcard search 83 Splitting text by numbers and non-white space characters 86 Chapter 4: Solr Administration 89 Introduction 89 Monitoring Solr via JMX 90 How to check the cache status 93 How to check how the data type or field behave 95 How to check Solr query handler usage 98 How to check Solr update handler usage 100 How to change Solr instance logging configuration 102 How to check the Java based replication status 104 How to check the script based replication status 106 Setting up a Java based index replication 108 Setting up script based replication 110 How to manage Java based replication status using HTTP commands 113 How to analyze your index structure 116 Chapter 5: Querying Solr 121 Introduction 121 Asking for a particular field value 122 Sorting results by a field value 123 Choosing a different query parser 125 How to search for a phrase, not a single word 126 Boosting phrases over words 128 Positioning some documents over others on a query 131 Positioning documents with words closer to each other first 136 Sorting results by a distance from a point 138 Getting documents with only a partial match 141 Affecting scoring with function 143 ii Table of Contents Nesting queries 147 Chapter 6: Using Faceting Mechanism 151 Introduction 151 Getting the number of documents with the same field value 152 Getting the number of documents with the same date range 155 Getting the number of documents with the same value range 158 Getting the number of documents matching the query and sub query 161 How to remove filters from faceting results 164 How to name different faceting results 167 How to sort faceting results in an alphabetical order 170 How to implement the autosuggest feature using faceting 173 How to get the number of documents that
Load more

Recommended publications
  • Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected]
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by St. Cloud State University St. Cloud State University theRepository at St. Cloud State Culminating Projects in Information Assurance Department of Information Systems 3-2017 Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected] Follow this and additional works at: https://repository.stcloudstate.edu/msia_etds Recommended Citation Annangi, Harikrishna, "Security Log Analysis Using Hadoop" (2017). Culminating Projects in Information Assurance. 19. https://repository.stcloudstate.edu/msia_etds/19 This Starred Paper is brought to you for free and open access by the Department of Information Systems at theRepository at St. Cloud State. It has been accepted for inclusion in Culminating Projects in Information Assurance by an authorized administrator of theRepository at St. Cloud State. For more information, please contact [email protected]. Security Log Analysis Using Hadoop by Harikrishna Annangi A Starred Paper Submitted to the Graduate Faculty of St. Cloud State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Assurance April, 2016 Starred Paper Committee: Dr. Dennis Guster, Chairperson Dr. Susantha Herath Dr. Sneh Kalia 2 Abstract Hadoop is used as a general-purpose storage and analysis platform for big data by industries. Commercial Hadoop support is available from large enterprises, like EMC, IBM, Microsoft and Oracle and Hadoop companies like Cloudera, Hortonworks, and Map Reduce. Hadoop is a scheme written in Java that allows distributed processes of large data sets across clusters of computers using programming models. A Hadoop frame work application works in an environment that provides storage and computation across clusters of computers.
    [Show full text]
  • Building a Scalable Index and a Web Search Engine for Music on the Internet Using Open Source Software
    Department of Information Science and Technology Building a Scalable Index and a Web Search Engine for Music on the Internet using Open Source software André Parreira Ricardo Thesis submitted in partial fulfillment of the requirements for the degree of Master in Computer Science and Business Management Advisor: Professor Carlos Serrão, Assistant Professor, ISCTE-IUL September, 2010 Acknowledgments I should say that I feel grateful for doing a thesis linked to music, an art which I love and esteem so much. Therefore, I would like to take a moment to thank all the persons who made my accomplishment possible and hence this is also part of their deed too. To my family, first for having instigated in me the curiosity to read, to know, to think and go further. And secondly for allowing me to continue my studies, providing the environment and the financial means to make it possible. To my classmate André Guerreiro, I would like to thank the invaluable brainstorming, the patience and the help through our college years. To my friend Isabel Silva, who gave me a precious help in the final revision of this document. Everyone in ADETTI-IUL for the time and the attention they gave me. Especially the people over Caixa Mágica, because I truly value the expertise transmitted, which was useful to my thesis and I am sure will also help me during my professional course. To my teacher and MSc. advisor, Professor Carlos Serrão, for embracing my will to master in this area and for being always available to help me when I needed some advice.
    [Show full text]
  • Natural Language Processing Technique for Information Extraction and Analysis
    International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 8, August 2015, PP 32-40 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Natural Language Processing Technique for Information Extraction and Analysis T. Sri Sravya1, T. Sudha2, M. Soumya Harika3 1 M.Tech (C.S.E) Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] 2 Head (I/C) of C.S.E & IT Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] 3 M. Tech C.S.E, Assistant Professor, Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] Abstract: In the current internet era, there are a large number of systems and sensors which generate data continuously and inform users about their status and the status of devices and surroundings they monitor. Examples include web cameras at traffic intersections, key government installations etc., seismic activity measurement sensors, tsunami early warning systems and many others. A Natural Language Processing based activity, the current project is aimed at extracting entities from data collected from various sources such as social media, internet news articles and other websites and integrating this data into contextual information, providing visualization of this data on a map and further performing co-reference analysis to establish linkage amongst the entities. Keywords: Apache Nutch, Solr, crawling, indexing 1. INTRODUCTION In today’s harsh global business arena, the pace of events has increased rapidly, with technological innovations occurring at ever-increasing speed and considerably shorter life cycles.
    [Show full text]
  • Building a Search Engine for the Cuban Web
    Building a Search Engine for the Cuban Web Jorge Luis Betancourt Search/Crawl Engineer NOVEMBER 16-18, 2016 • SEVILLE, SPAIN Who am I 01 Jorge Luis Betancourt González Search/Crawl Engineer Apache Nutch Committer & PMC Apache Solr/ES enthusiast 2 Agenda • Introduction & motivation • Technologies used • Customizations • Conclusions and future work 3 Introduction / Motivation Cuba Internet Intranet Global search engines can’t access documents hosted the Cuban Intranet 4 Writing your own web search engine from scratch? or … 5 Common search engine features 1 Web search: HTML & documents (PDF, DOC) • highlighting • suggestions • filters (facets) • autocorrection 2 Image search (size, format, color, objects) • thumbnails • show metadata • filters (facets) • match text with images 3 News search (alerting, notifications) • near real time • email, push, SMS 6 How to fulfill these requirements? store query At the core a search engine: stores some information a retrieve this information when a question is received 7 Open Source to the rescue … crawler 1 Index Server 2 web interface 3 8 Apache Nutch Nutch is a well matured, production ready “ Web crawler. Enables fine grained configuration, relying on Apache Hadoop™ data structures, which are great for batch processing. 9 Apache Nutch • Highly scalable • Highly extensible • Pluggable parsing protocols, storage, indexing, scoring, • Active community • Apache License 10 Apache Solr TOTAL DOWNLOADS 8M+ MONTHLY DOWNLOADS 250,000+ • Apache License • Great community • Highly modular • Stability / Scalability
    [Show full text]
  • Full-Graph-Limited-Mvn-Deps.Pdf
    org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version}
    [Show full text]
  • Chapter 2 Introduction to Big Data Technology
    Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media.
    [Show full text]
  • Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
    Appendix to the paper "Code smell prediction employing machine learning meets emerging Java language constructs" Hanna Grodzicka, Michał Kawa, Zofia Łakomiak, Arkadiusz Ziobrowski, Lech Madeyski (B) The Appendix includes two tables containing the dataset used in the paper "Code smell prediction employing machine learning meets emerging Java lan- guage constructs". The first table contains information about 792 projects selected for R package reproducer [Madeyski and Kitchenham(2019)]. Projects were the base dataset for cre- ating the dataset used in the study (Table I). The second table contains information about 281 projects filtered by Java version from build tool Maven (Table II) which were directly used in the paper. TABLE I: Base projects used to create the new dataset # Orgasation Project name GitHub link Commit hash Build tool Java version 1 adobe aem-core-wcm- www.github.com/adobe/ 1d1f1d70844c9e07cd694f028e87f85d926aba94 other or lack of unknown components aem-core-wcm-components 2 adobe S3Mock www.github.com/adobe/ 5aa299c2b6d0f0fd00f8d03fda560502270afb82 MAVEN 8 S3Mock 3 alexa alexa-skills- www.github.com/alexa/ bf1e9ccc50d1f3f8408f887f70197ee288fd4bd9 MAVEN 8 kit-sdk-for- alexa-skills-kit-sdk- java for-java 4 alibaba ARouter www.github.com/alibaba/ 93b328569bbdbf75e4aa87f0ecf48c69600591b2 GRADLE unknown ARouter 5 alibaba atlas www.github.com/alibaba/ e8c7b3f1ff14b2a1df64321c6992b796cae7d732 GRADLE unknown atlas 6 alibaba canal www.github.com/alibaba/ 08167c95c767fd3c9879584c0230820a8476a7a7 MAVEN 7 canal 7 alibaba cobar www.github.com/alibaba/
    [Show full text]
  • Deploying Arch
    Deploying Arch 1. Introduction This document is written for webmasters. It provides simple step-by-by step instructions for the installation of Arch. Some basic familiarity with managing websites is assumed. Arch is a multi-component package: it includes Arch indexer (based on Apache Nutch) and Arch search server (based on Apache Solr) with, optionally, one or more PHP front-ends. See Arch White Paper for more details on Arch architecture. Arch also requires a number of freely available software packages: Java, a Web server, PHP and a relational database management system (RDBMS) such as MySQL. These are widely used applications, and some, or all, of these may already be installed. Note: Arch 1.7 and later versions use own Jetty server and embedded RDBMS (H2) by default. This significantly simplifies deployment. All you have to do to get it going is run Ant build script and insert your seed URLs into Arch script. For a quick start, see Arch Quick Start Guide. The document below describes Arch installation for advanced users. 2. System requirements • A Linux/Unix Operating System, or Windows Vista/7 or later with Cygwin installed; • Java 9, or later version; • Apache Ant and Ant-options packages. These are required for building Arch binaries (section 3.7); • Apache Ivy; • Optionally, for PHP front-ends, a Web server, such as Apache, with PHP; • At least 2 GB RAM. The amount of memory required will depend on the size of the web site(s) and the number of web pages to crawl at one iteration. As a very rough guide 2 GB of RAM should be enough to crawl approximately 10,000 web pages at one iteration.
    [Show full text]
  • A Software Architecture for Progressive Scanning of On-Line Communities
    A Software Architecture for Progressive Scanning of On-line Communities Roberto Baldoni, Fabrizio d’Amore, Massimo Mecella, Daniele Ucci Cyber Intelligence and Information Security Center Dipartimento di Ingegneria Informatica, Automatica e Gestionale Sapienza Universita` di Roma, Italy Email: fbaldoni|damore|[email protected] , [email protected] Abstract—We consider a set of on-line communities (e.g., news, U1,1 U1,2 U1,3 U1,4 U1,5 U1,6 blogs, Google groups, Web sites, etc.). The content of a community C1 t is continuously updated by users and such updates can be seen U2,1 U2,2 U2,3 U2,4 U2,5 U2,6 by users of other communities. Thus, when creating an update, C2 a user could be influenced by one or more updates creating a t U U U U U U semantic causal relationship among updates. This transitively will 3,1 3,2 3,3 3,4 3,5 3,6 C3 allow to trace how an information flows across communities. The t paper presents a software architecture that progressively scan a seman8c causal relaonship community update set of on-line communities in order to detect such semantic causal relationships. The architecture includes a crawler, a large scale storage, a distributed indexing system and a mining system. The paper mainly focuses on crawling and indexing. (a) Evolution along the time of a set of social communities showing how an update is causally influenced by other updates Keywords—On-line communities, progressive scanning, MapR, Nutch I. INTRODUCTION U1,1 U1,2 U1,3 U1,4 U1,5 U1,6 C1 On-line communities are nowadays a fundamental source U U U U U of information in business and information security intelligence.
    [Show full text]
  • Enterprise Data Warehouse Optimization with Hadoop on Power
    Front cover Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers Helen Lu Maciej Olejniczak In partnership with IBM Academy of Technology Redpaper International Technical Support Organization Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers January 2018 REDP-5476-00 Note: Before using this information and the product it supports, read the information in “Notices” on page v. First Edition (January 2018) This edition applies to Hortonworks Data Platform (HDP) Version 2.6 running on IBM Power Systems servers. © Copyright International Business Machines Corporation 2018. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . .v Trademarks . vi Preface . vii Authors. viii Now you can become a published author, too! . viii Comments welcome. ix Stay connected to IBM Redbooks . ix Chapter 1. Enterprise Data Warehouse overview. 1 1.1 Traditional Enterprise Data Warehouse . 2 1.2 Enterprise Data Warehouse on Hadoop . 2 1.3 Hadoop technology overview . 3 1.3.1 Advantages of using Hadoop . 4 1.3.2 Apache Hadoop components . 5 1.3.3 IBM and Hadoop technology. 7 1.3.4 The Hortonworks Data Platform on IBM Power Systems . 7 1.3.5 Hortonworks DataFlow . 10 Chapter 2. IBM Power Systems overview . 11 2.1 IBM Power Systems overview. 12 2.1.1 POWER8 server highlights . 12 2.1.2 POWER9 server highlights . 14 2.2 POWER versus Intel x86 performance . 16 2.2.1 System performance comparison . 18 2.3 NVIDIA GPU accelerators. 18 2.4 Linux on Power advantages .
    [Show full text]
  • Tracking Down the Bad Guys Tom Barber - NASA JPL Big Data Conference - Vilnius Nov 2017 Who Am I?
    Tracking Down The Bad Guys Tom Barber - NASA JPL Big Data Conference - Vilnius Nov 2017 Who am I? Tom Barber Data Nerd Open Source Business Intelligence Developer Director of Meteorite BI and Spicule LTD Software Developer at NASA JPL in the Computer Science for Data Intensive Applications Group Some stuff I’ve worked on Saiku Analytics - OLAP Browser Apache OODT - Data Processing Toolkit originally by NASA Pentaho Business Intelligence Suite - Business Intelligence Suite Apache Tika - Metadata Extraction Apache DRAT - Release Auditing Sparkler - Spark Crawler How I ended up working at NASA It’s an easy 6 step process!! 1. Want to be an astronaut 2. Learn about data 3. Volunteer some time on open source code and meet new people 4. Decide to change job direction 5. Ask random people for new job 6. Get offered a job at NASA See…. easy! Work we do at NASA JPL We build applications for high volume data processing and visualisation. ● Genomics Search and data extraction ● Polar data discovery ● Data processing for a number of satellite programs ● Storage and archive of planetary data ● Track down criminals!!! What is DARPA? Defense Advanced Research Projects Agency Founded in response to Sputnik Invests in technology for (US) National Security Work with external partners to bring knowledge to programs The Challenge Memex seeks to develop software that advances online search capabilities far beyond the current state of the art. The goal is to invent better methods for interacting with and sharing information, so users can quickly and thoroughly organize and search subsets of information relevant to their individual interests.
    [Show full text]
  • Low Latency Scalable Web Crawling on Apache Storm
    Storm Crawler Low latency scalable web crawling on Apache Storm Julien Nioche [email protected] digitalpebble Berlin Buzzwords 01/06/2015 About myself . DigitalPebble Ltd, Bristol (UK) . Specialised in Text Engineering – Web Crawling – Natural Language Processing – Information Retrieval – Machine Learning . Strong focus on Open Source & Apache ecosystem – Nutch – Tika – GATE, UIMA – SOLR, Elasticearch – Behemoth 2 Storm-Crawler : what is it? . Collection of resources (SDK) for building web crawlers on Apache Storm . https://github.com/DigitalPebble/storm-crawler . Apache License v2 . Artefacts available from Maven Central . Active and growing fast . Version 0.5 just released! => Can scale => Can do low latency => Easy to use and extend => Nice features (politeness, scraping, sitemaps etc...) 3 What it is not . A well packaged application – It's a SDK : it requires some minimal programming and configuration . No global processing of the pages e.g.PageRank . No fancy UI, dashboards, etc... – Build your own or integrate in your existing ones – But can use Storm UI + metrics to various backends (Librato, ElasticSearch) 4 Comparison with Apache Nutch 5 StormCrawler vs Nutch . Nutch is batch driven : little control on when URLs are fetched – Potential issue for use cases where need sessions – latency++ . Fetching only one of the steps in Nutch – SC : 'always fetching' ; better use of resources . More flexible – Typical case : few custom classes (at least a Topology) the rest are just dependencies and standard SC components – Logical crawls : multiple crawlers with their own scheduling easier with SC via queues . Not ready-to use as Nutch : it's a SDK . Would not have existed without it – Borrowed some code and concepts 6 – Contributed some stuff back Apache Storm 7 Apache Storm .
    [Show full text]