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APACHE SOLR 3 ENTERPRISE SEARCH SERVER PDF

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Apache Solr 3 Enterprise Search ServerEnhance your search with faceted navigation, result highlighting, relevancy ran. Apache Solr 3 Enterprise Search Server Apache Solr Cookbook. Read more Professional Microsoft Search: SharePoint and Search Server . Solr is a standalone enterprise search server with a web-services like API. Indexing (PDF, Word, HTML, etc) using Apache Tika. • Multiple search indices. 3.


Apache Solr 3 Enterprise Search Server Pdf

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If you are a developer building an app today then you know how important a good search experience is. Apache Solr, built on Apache Lucene. Enhance your searches with faceted navigation, result highlighting, relevancy- ranked sorting, and much more with this comprehensive guide to Apache Solr 4 In. Selection from Apache Solr 3 Enterprise Search Server [Book] messier world of binary formats such as PDF, Microsoft Office, or even images and music files.

You can reach him at his LinkedIn profile here: and view Fascinated by the "craft" of software development, Eric Pugh has been involved in the open source world as a developer, committer, and user for the past decade.

He is an emeritus member of the Apache Software Foundation. In biotech, financial services, and defense IT, he has helped European and American companies develop coherent strategies to embrace open source software. As a speaker, he has advocated the advantages of Agile practices in search, discovery, and analytics projects.

Reference Documents

He blogs at Kranti Parisa has more than a decade of software development expertise and a deep understanding of open source, enterprise software, and the execution required to build successful products. He has fallen in love with enterprise search technologies, especially Lucene and Solr, after his initial implementations and customizations carried out in early to build a legal search engine for bankruptcy court documents, docket entries, and cases.

He is an active contributor to the Apache Solr community. One of his recent contributions, along with Joel Bernstein, SOLR, includes scalable and nested join implementations. Kranti is currently working at Apple. An entrepreneur by DNA, he is the cofounder and technical advisor of multiple start-ups focusing on cloud computing, SaaS, big data, and enterprise search based products and services.

He holds a master's degree in computer integrated manufacturing from the National Institute of Technology, Warangal, India. You can reach him on LinkedIn: Matt Mitchell studied music synthesis and performance at Boston's Berklee College of Music, but his experiences with computers and programming in his younger years inspired him to pursue a career in software engineering.

A passionate technologist, he has worked in many areas of software development, is active in several open source communities, and now has over 15 years of professional experience. He had his first experiences with Lucene and Solr in at the University of Virginia Library, where he became a core contributor to an open source search platform called Backlight.

Matt is the author of many open source projects, including a Solr client library called RSolr, which has had over 1 million downloads from rubygems. He has been responsible for the design and implementation of search systems at several tech companies, and he is currently a senior member of the engineering team at LucidWorks, where he's working on a next generation search, discovery, and analytics platform.

Apache Solr, built on Apache Lucene, is a wildly popular open source enterprise search server that easily delivers the powerful search and faceted navigation features that are elusive with databases.

Solr supports complex search criteria, faceting, result highlighting, query-completion, query spellcheck, relevancy tuning, and more. It serves the reader right from initiation to development to deployment. It also comes with complete running examples to demonstrate its use and show how to integrate Solr with other languages and frameworks—even Hadoop.

By using a large set of metadata, including artists, releases, and tracks, courtesy of the MusicBrainz. You will then learn how to search this data in different ways, including Solr's rich query syntax and boosting match scores based on record data.

Finally, we'll cover various deployment considerations to include indexing strategies and performance-oriented configuration that will enable you to scale Solr to meet the needs of a high-volume site.

This chapter is oriented to Solr 5, but the majority of content applies to Solr 4 too. Chapter 2, Schema Design, guides you through an approach to modeling your data within Solr into one or more Solr indices and schemas. It covers the schema thoroughly and explores some of Solr's field types.

Chapter 3, Text Analysis, covers how to customize text tokenization, stemming, synonyms, and related matters to have fine control over keyword search matching. It also covers multilingual strategies. This includes important information on commits, atomic updates, and real-time search.

Chapter 5, Searching, covers the query syntax, from the basics to Boolean options to more advanced wildcard and fuzzy searches, join queries, and geospatial search. Chapter 6, Search Relevancy, explains how Solr scores documents for relevancy ranking.

Enterprise Search using Solr and Lucene

We'll review different options to influence the score, called boosting, and apply it to common examples such as boosting recent documents and boosting by a user vote.

Chapter 7, Faceting, shows you how to use Solr's killer feature—faceting. You can use it to implement everything from a simple list to a fully-featured full text search on the whole website. OpenCms has already preconfigured most things, so you only have to search.

There's a suitable schema that is what tells which fields can be part of a document for the indexes - it fits to the information of resources in the VFS. We've also taken care to support multi-lingual setups properly. Permission checks are performed before search results are returned. You can search with nearly no Solr knowledge: Build lists with the integrated list type that provides an intuitive configuration interface for basic searches.

Solr search integration

You can query Solr from the client using the Solr handler - this you get a close to "native" Solr experience but still have permissions checked. You can configure indexing of XML contents very flexible via search settings to allow for advanced search features when searching over your contents.

If the default configuration is not sufficient for your purpose, you can manipulate it with many configuration options: Add other indexes Feed an external Solr server with data from OpenCms Change the document extractors for certain resource types to index the documents differently. The default configuration will be sufficient for most scenarios.

So play with it before thinking about reconfiguration. How can I search? Partly for the first suggestion and necessarily for the second one, you should know how a Solr query is constructed in general. The best way to play with Solr on your OpenCms instance is the Solr handler. Here you can type plain Solr queries and get responses immediately. We explain a search with a concrete example that you may vary a bit depending on your OpenCms installation : Show articles in the default site that have been changed in the last 24 hours and sort them by the english title ascending.

Below, we show different solutions to perform that search. Using the Solr handler Log in to your local OpenCms installation and play with the handler. It's the best place to test queries. Solr is evolving very fast and query options become more and more. The OpenCms Solr handler supports most of the options and you can play with it. Notes on the response The handler directly returns a response with the search results.The module has had significant adoption and is the basis of some other Drupal search related modules.

Packt Logo. How can I search? Here are the results of the previous example search with nothing unusual about it: The boost is added. You can do this by using the existing logic, and then by just listing your higher capacity servers in the SHARDS array multiple times. It also comes with complete running examples to demonstrate its use and show how to integrate Solr with other languages and frameworks—even Hadoop.

Moving the load of performing searches off one's Drupal server into the cloud drastically reduces the load of indexing and performing searches on Drupal.