2009년 10월 23일
Solr관련 책 출간
링크 : Solr 1.4 Enterprise Search Server
Adobe eBook PDF Version
Enhance your search with faceted navigation, result highlighting, fuzzy queries, ranked scoring, and more
- Deploy, embed, and integrate Solr with a host of programming languages
- Implement faceting in e-commerce and other sites to summarize and navigate the results of a text search
- Enhance your search by highlighting search results, offering spell-corrections, auto-suggest, finding “similar” records, boosting records and fields for scoring, phonetic matching
- Informative and practical approach to development with fully working examples of integrating a variety of technologies
- Written and tested for Solr 1.4 pre-release 2009.08
Language English
Paperback 336 pages [191mm x 235mm]
Release date August 2009
ISBN 1847195881
ISBN 13 978-1-847195-88-3
Author(s) David Smiley, Eric Pugh
Topics and Technologies Open Source
In Detail
If you are a developer building a high-traffic web site, you need to have a terrific search engine. Sites like Netflix.com and Zappos.com employ Solr, an open source enterprise search server, which uses and extends the Lucene search library. This is the first book in the market on Solr and it will show you how to optimize your web site for high volume web traffic with full-text search capabilities along with loads of customization options. So, let your users gain a terrific search experience.
This book is a comprehensive reference guide for every feature Solr has to offer. 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 it with other languages and frameworks.
This book first gives you a quick overview of Solr, and then gradually takes you from basic to advanced features that enhance your search. It starts off by discussing Solr and helping you understand how it fits into your architecture—where all databases and document/web crawlers fall short, and Solr shines. The main part of the book is a thorough exploration of nearly every feature that Solr offers. To keep this interesting and realistic, we use a large open source set of metadata about artists, releases, and tracks courtesy of the MusicBrainz.org project. Using this data as a testing ground for Solr, you will learn how to import this data in various ways from CSV to XML to database access. You will then learn how to search this data in a myriad of ways, including Solr's rich query syntax, "boosting" match scores based on record data and other means, about searching across multiple fields with different boosts, getting facets on the results, auto-complete user queries, spell-correcting searches, highlighting queried text in search results, and so on.
After this thorough tour, we'll demonstrate working examples of integrating a variety of technologies with Solr such as Java, JavaScript, Drupal, Ruby, XSLT, PHP, and Python.
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.
What you will learn from this book
Approach
- Blend structured data with real search features
- Import CSV formatted data, XML, common document formats, and from databases
- Deploy Solr and provide reference to Solr's query syntax from the basics to range queries
- Enhance search results with spell-checking, auto-completing queries, highlighting search results, and more.
- Secure Solr
- Integrate a host of technologies with Solr from the server side to client-side JavaScript, to frameworks like Drupal
- Scale Solr using replication, distributed searches, and tuning
The book takes a tutorial approach with fully working examples. It will show you how to implement a Solr-based search engine on your intranet or web site.Who this book is written for
This book is for developers who would like to use Solr for their applications. You only need to have basic programming skills to use Solr. Knowledge of Lucene is certainly a bonus.
Author(s)
David SmileyBorn to code, David Smiley is a senior software developer with 10 years of experience in the defense industry using Java and various web technologies. David is a strong believer in the open-source development model and has made small contributions to various projects over the years.
David began using Lucene way back in 2000 and was immediately excited by it and its future potential. Later on he went to use the Lucene-based "Compass" library to construct a very basic search server similar in spirit to Solr. Since then, David has used Solr for a larger search project and was able to contribute modifications back to the Solr community. Although preferring open-source solutions, David has also been trained on the commercial Endeca search platform and is currently using that product as well as Solr for a different project.
Eric Pugh
Fascinated by the "craft" of software development, Eric Pugh has been heavily involved in the open source world as a developer, committer, and user for the past 5 years. He is a member of the Apache Software Foundation and lately has been mulling over how we move from the read/write web to the read/write/share web.
In biotech, financial services, and defense IT, he has helped European and American companies develop coherent strategies for embracing open source software. As a speaker he has advocated the advantages of Agile practices in software development.
Eric became involved in Solr when he submitted the patch SOLR-284 for Parsing Rich Document types such as PDF and MS Office formats that became the single most popular patch as measured by votes! SOLR-284 became part of Solr version 1.4.
Table of Contents
Preface
Chapter 1: Quick Starting Solr
- An introduction to Solr
- Lucene, the underlying engine
- Solr, the Server-ization of Lucene
- Comparison to database technology
- Getting started
- The last official release or fresh code from source control
- Testing and building Solr
- Solr's installation directory structure
- Solr's home directory
- How Solr finds its home
- Deploying and running Solr
- A quick tour of Solr!
- Loading sample data
- A simple query
- Some statistics
- The schema and configuration files
- Solr resources outside this book
- Summary
Chapter 2: Schema and Text Analysis
- MusicBrainz.org
- One combined index or multiple indices
- Problems with using a single combined index
- Schema design
- Step 1: Determine which searches are going to be powered by Solr
- Step 2: Determine the entities returned from each search
- Step 3: Denormalize related data
- Denormalizing—"one-to-one" associated data
- Denormalizing—"one-to-many" associated data
- Step 4: (Optional) Omit the inclusion of fields only used in search results
- The schema.xml file
- Field types
- Field options
- Field definitions
- Sorting
- Dynamic fields
- Using copyField
- Remaining schema.xml settings
- Text analysis
- Configuration
- Experimenting with text analysis
- Tokenization
- WorkDelimiterFilterFactory
- Stemming
- Synonyms
- Index-time versus Query-time, and to expand or not
- Stop words
- Phonetic sounds-like analysis
- Partial/Substring indexing
- N-gramming costs
- Miscellaneous analyzers
- Summary
- Communicating with Solr
- Direct HTTP or a convenient client API
- Data streamed remotely or from Solr's filesystem
- Data formats
- Using curl to interact with Solr
- Remote streaming
- Sending XML to Solr
- Deleting documents
- Commit, optimize, and rollback
- Sending CSV to Solr
- Configuration options
- Direct database and XML import
- Getting started with DIH
- The DIH development console
- DIH documents, entities
- DIH fields and transformers
- Importing with DIH
- Getting started with DIH
- Indexing documents with Solr Cell
- Extracting binary content
- Configuring Solr
- Extracting karaoke lyrics
- Indexing richer documents
- Summary
- Your first search, a walk-through
- Solr's generic XML structured data representation
- Solr's XML response format
- Parsing the URL
- Query parameters
- Parameters affecting the query
- Result paging
- Output related parameters
- Diagnostic query parameters
- Query syntax
- Matching all the documents
- Mandatory, prohibited, and optional clauses
- Boolean operators
- Sub-expressions (aka sub-queries)
- Limitations of prohibited clauses in sub-expressions
- Field qualifier
- Phrase queries and term proximity
- Wildcard queries
- Fuzzy queries
- Range queries
- Date math
- Score boosting
- Existence (and non-existence) queries
- Escaping special characters
- Filtering
- Sorting
- Request handlers
- Scoring
- Query-time and index-time boosting
- Troubleshooting scoring
- Summary
- Function queries
- An example: Scores influenced by a lookupcount
- Field references
- Function reference
- Mathematical primitives
- Miscellaneous math
- ord and rord
- An example with scale() and lookupcount
- Using logarithms
- Using inverse reciprocals
- Using reciprocals and rord with dates
- Function query tips
- Dismax Solr request handler
- Lucene's DisjunctionMaxQuery
- Configuring queried fields and boosts
- Limited query syntax
- Boosting: Automatic phrase boosting
- Configuring automatic phrase boosting
- Phrase slop configuration
- Boosting: Boost queries
- Boosting: Boost functions
- Min-should-match
- Basic rules
- Multiple rules
- What to choose
- A default search
- Lucene's DisjunctionMaxQuery
- Faceting
- A quick example: Faceting release types
- MusicBrainz schema changes
- Field requirements
- Types of faceting
- Faceting text
- Alphabetic range bucketing (A-C, D-F, and so on)
- Faceting dates
- Date facet parameters
- Faceting on arbitrary queries
- Excluding filters
- The solution: Local Params
- Facet prefixing (term suggest)
- A quick example: Faceting release types
- Summary
- About components
- The highlighting component
- A highlighting example
- Highlighting configuration
- Query elevation
- Configuration
- Spell checking
- Schema configuration
- Configuration in solrconfig.xml
- Configuring spellcheckers (dictionaries)
- Processing of the q parameter
- Processing of the spellcheck.q parameter
- Building the dictionary from its source
- Issuing spellcheck requests
- Example usage for a mispelled query
- An alternative approach
- The more-like-this search component
- Configuration parameters
- Parameters specific to the MLT search component
- Parameters specific to the MLT request handler
- Common MLT parameters
- MLT results example
- Configuration parameters
- Stats component
- Configuring the stats component
- Statistics on track durations
- Field collapsing
- Configuring field collapsing
- Other components
- Terms component
- termVector component
- LocalSolr component
- Summary
- Implementation methodology
- Questions to ask
- Installing into a Servlet container
- Differences between Servlet containers
- Defining solr.home property
- Differences between Servlet containers
- Logging
- HTTP server request access logs
- Solr application logging
- Configuring logging output
- Logging to Log4j
- Jetty startup integration
- Managing log levels at runtime
- A SearchHandler per search interface
- Solr cores
- Configuring solr.xml
- Managing cores
- Why use multicore
- JMX
- Starting Solr with JMX
- Take a walk on the wild side! Use JRuby to extract JMX information
- Starting Solr with JMX
- Securing Solr
- Limiting server access
- Controlling JMX access
- Securing index data
- Controlling document access
- Other things to look at
- Limiting server access
- Summary
- Structure of included examples
- Inventory of examples
- SolrJ: Simple Java interface
- Using Heritrix to download artist pages
- Indexing HTML in Solr
- SolrJ client API
- Indexing POJOs
- When should I use Embedded Solr
- In-Process streaming
- Rich clients
- Upgrading from legacy Lucene
- Using JavaScript to integrate Solr
- Wait, what about security?
- Building a Solr powered artists autocomplete widget with jQuery and JSONP
- SolrJS: JavaScript interface to Solr
- Accessing Solr from PHP applications
- solr-php-client
- Drupal options
- Apache Solr Search integration module
- Hosted Solr by Acquia
- Ruby on Rails integrations
- acts_as_solr
- Setting up MyFaves project
- Populating MyFaves relational database from Solr
- Build Solr indexes from relational database
- Complete MyFaves web site
- Blacklight OPAC
- Indexing MusicBrainz data
- Customizing display
- solr-ruby versus rsolr
- acts_as_solr
- Summary
- Tuning complex systems
- Using Amazon EC2 to practice tuning
- Firing up Solr on Amazon EC2
- Using Amazon EC2 to practice tuning
- Optimizing a single Solr server (Scale High)
- JVM configuration
- HTTP caching
- Solr caching
- Tuning caches
- Schema design considerations
- Indexing strategies
- Disable unique document checking
- Commit/optimize factors
- Enhancing faceting performance
- Using term vectors
- Improving phrase search performance
- The solution: Shingling
- Moving to multiple Solr servers (Scale Wide)
- Script versus Java replication
- Starting multiple Solr servers
- Configuring replication
- Distributing searches across slaves
- Indexing into the master server
- Configuring slaves
- Distributing search queries across slaves
- Sharding indexes
- Assigning documents to shards
- Searching across shards
- Combining replication and sharding (Scale Deep)
- Summary
# by | 2009/10/23 10:18 | SOLR | 트랙백 | 덧글(0)
