The document discusses Apache Solr, an open-source search server built on top of the Apache Lucene project. It covers its internal concepts, features such as full-text search, relevancy ranking, geospatial search, and the flexible schema configurations available. Additionally, the document provides usage guidelines, use cases, and resources for integrating Solr with PHP and other applications.

1. Find it,
possibly also near you!
Paul Borgermans

2. About me
● Currently employed by eZ Systems http://ez.no
● Active in open source community for a while
– Squid http proxy server (about 15 y ago)
– PHP based CMS solutions (mostly eZ Publish)
– executive committee
● Currently fancying :
– PHP as the master glue language for almost everything
– Apache Lucene family of projects (mainly Solr)
– NoSQL (Not only SQL) and scalable architectures
– CMS systems & information management

3. Outline
● Overview of Apache Solr
● Concepts & internals
● How to use it with PHP
● Use cases & tips
● Resources

4. Overview of Apache Solr

5. Apache Solr Curriculum Vitae
● Open source Apache Lucene project,
started by Yonik Seeley
● Standalone, enterprise grade search
server built on top of Lucene
● Lives in a Java servlet container
● Access through a REST-ful API
– HTTP
– Primary payload in requests: XML
– Other response formats: PHP, JSON, …

6. Used by ..
And many more ...

7. Solr in a nutshell
● State of the art, advanced full text search and
information retrieval
● Fast, scalable with native replication features
● Flexible configuration
● Document oriented storage
● Geospatial search
● Native cloud features

8. Full text search main features
● Tuneable relevancy ranking on top of internal
similarity algorithms
● Highlighting
● Sorting
● Filtering
● “Drill-down” navigation (facets)
● Automatic related content
● Spell checking
● Multilingual text analysis

9. At a glance ..

10. Tunable relevancy ranking
● “Boosting” at index and query time
– certain types of content
– certain parts of content (“fields”)
– page-rank like if the content has relations
● Elevate request component
– predefined “pages/documents” to the top when certain
keywords are entered
● With customised functions
– more recent articles
– proximity (geolocations)

11. Filtering
● Does not influence the relevancy
● Narrows down the scope
● Very powerful: full boolean, wildcards,
fuzzy, and unlimited combinations
● Ranges (dates, numbers,
alphanumeric, ...)
Also for implementing security!

12. Facets
● Along the main query, “facet fields” may be defined,
usually operating on meta-data:
– Type of content
– Publication year
– Keywords
– Author ....
● The result set is returned offering the number hits
within each “facet”
● You can use the selected facet as a subsequent filter

13. Facets: example

14. Automatic related content
(“More Like This”)
● Search engine determines itself which are the
important terms of a page and performs a query
● All other normal features can be used
– Filtering
– Sorting
– Facets

16. Spell checking
● Two possible strategies
– Dictionary look-up
– Using the indexed words itself (recommended)
● Possible “Google” approach using the “best guess”
– Search for “Grein botle“
=> suggests “Green bottle”
● Let Solr return individual keyword suggestions
=> more client side processing required

17. Multilingual features
● Adapted tokenizers
● Stemming (reducing words to common form)
– Reduces some spelling errors too!
– May decrease accuracy
● Different algorithms per language
● Normalisation (“latin 1 characters”)
– élève = eleve, Spaß = spass, ...

18. Geospatial search

19. Performance
● Solr employs intelligent caches
– filters
– queries
– internal indexes
● Optimized for search/retrieval
● Possible autowarming on start up
● When updates are done, caches are
reconstructed on the fly in the background

20. Performance (2)
● Replication
– master-slave for now
– works across platforms with same configuration
– no native OS features needed (or rsync)
– more cloud features under development
● Sharding (client driven)

21. Concepts and internals

22. The Solr/Lucene index
● Inverted index
● Holds a collection of “documents” (hello NoSQL)
● Document
– Collection of fields
– Flexible schema!
– Unique ID (user defined)
● Solr uses a XML based config file:
schema.xml

23. Fields
● Various field types, derived from base classes
● Indexed
– contains the inverted index
– usually analyzed & tokenized
– makes it searchable and sortable
● Stored
– contains also the original content
– content can be part of the request response
● Can be multi-valued!
– opens possibilities beyond full text search

24. Field definitions: schema.xml
● Field types
– text
– numerical
– dates
– location
– … (about 25 in total)
● Actual fields (name, definition, properties)
● Dynamic fields
● Copy fields (as aggregators)

25. schema.xml: simple field type examples
<fieldType name="string" class="solr.StrField"
sortMissingLast="true" omitNorms="true"/>
<!-- boolean type: "true" or "false" -->
<fieldType name="boolean" class="solr.BoolField"
sortMissingLast="true" omitNorms="true"/>
<!-- A Trie based date field for faster date range
queries and date faceting. -->
<fieldType name="tdate" class="solr.TrieDateField"
omitNorms="true" precisionStep="6"
positionIncrementGap="0"/>
<!-- A text field that only splits on whitespace for exact matching
of words -->
<fieldType name="text_ws" class="solr.TextField"
positionIncrementGap="100">
<analyzer>
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
</analyzer>
</fieldType>

26. schema.xml: more complex field type
<!-- A general unstemmed text field - good if one does not know the language of the field -->
<fieldType name="textgen" class="solr.TextField" positionIncrementGap="100">
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="stopwords.txt"
enablePositionIncrements="false" />
<filter class="solr.WordDelimiterFilterFactory" generateWordParts="1"
generateNumberParts="1" catenateWords="1" catenateNumbers="1" catenateAll="0"
splitOnCaseChange="0"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.SynonymFilterFactory" synonyms="synonyms.txt" ignoreCase="true"
expand="true"/>
<filter class="solr.StopFilterFactory"
ignoreCase="true"
words="stopwords.txt"
enablePositionIncrements="true"
/>
<filter class="solr.WordDelimiterFilterFactory" generateWordParts="1"
generateNumberParts="1" catenateWords="0" catenateNumbers="0" catenateAll="0"
splitOnCaseChange="0"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>

27. Huh?

28. Analysis
● Solr does not really search your text, but rather
the terms that result from the analysis of text
● Typically a chain of
– Character filter(s)
– Tokenisation
– Filter A
– Filter B
– …

29. Solr comes with many tokenizers and
filters
● Some are language specific
● Others are very specialised
● It is very important to get this right
otherwise, you may not get what you expect!

30. Text analysis examples
String Field term term
type position position
“text” 1 2
iPad => i pad
ipad
élève. => elev
PowerS => power shot
hot powershot

31. Character filters
● Used to cleanup text before tokenizing
– HTMLStripCharFilter (strips html, xml, js, css)
– MappingCharFilter (normalisation of characters,
removing accents)
– Regular expression filter

32. Tokenizers
● Convert text to tokens (terms)
● You can define only one per field/analyzer
● Examples
– WhitespaceTokenizer (splits on white space)
– StandardTokenizer
– CJK variants

33. Additional filters
● Many possible per field/analyzer
● Many delivered with Solr out of the box
● If not enough, write a tiny bit of Java or look for
contributions
● Examples ...

34. Phonetic filters
● PhoneticFilterFactory
● “sounds like” transformations and matching
● Algorithms:
– Metaphone
– Double Metaphone
– Soundex
– Refined Soundex

35. Reversing Filter
● Reverses the order of characters
● Use: allow “leading wildcards”
● *thing => gniht*
● A lot faster (prefixes)

36. Synonyms
● Inject synonyms for certain terms
● Language specific
● Best used for query time analysis
– may inflate the search index too much
– decreases relevancy

37. Stemming
● Reduce terms to their root form
– Plural forms
– Conjugations
● Language specific (or not relevant, CJK)
● Many specialised stemmers available
– Most european languages
– Dutch (!)

38. Copy fields
● Analysis is done differently for
– searching/filtering
– faceting/sorting
● Stemming and not stemming in different fields
can increase relevance of results
● Use copy fields in schema.xml or do it client
side

39. Geospatial search
● Solr dedicated fields
– Latitude Longitude type
● Special geospatial functions in filtering &
boosting
– Haversine distance (geosphere)
– Simple ranges (squares in 2-D)
– Special query constructs (upcoming)

40. How to use it with

41. Get the data and feed it
● Most *AMP applications have databases
● Map your data to a “document model”
– denormalization, flattening
– most DB fields can be fed unaltered, Solr takes
care of the rest
● Send it through HTTP as XML
● One constraint: it must be UTF-8!

42. Searching
● Construct a GET/POST query
● Base parameters
– “q” for query text
– “start” for offset
– “rows” for max number of results to return
Example:
http://localhost:8983/solr/select/?q=content&version=2.2&start=0&rows=10&indent=on&wt=php

43. Searching (2)
● Additional parameters
– response format (wt)
●php = array(), json, ...
– type of search handler (qt)
– highlighting (hl.*)
– facets (f.<fieldName>.<FacetParam>=<value>)
– spellcheck (spellcheck)
– …

44. PHP client side
● Roll your own classes & functions
– Not difficult, it's REST after all
– Some Curl, XML, Json or native PHP array parsing
● Use existing libraries
– PECL: http://pecl.php.net/package/solr
– http://code.google.com/p/solr-php-client/
(follows ZF coding standards)
– eZ Components: ezcSearch
● PHP CMS's usually come with their own
– eZ Publish, Drupal, Symfony ...

45. Use-cases & tips

46. Indexing binary files
● Solr includes the Apache Tika libraries
– convert about any format to plain text
– you can activate a dedicated requesthandler for it
OR
● Use it standalone (command line) for integration into
existing code
See: http://lucene.apache.org/tika/

47. Integrate legacy data
● Use the Solr Data Import Handler
● Able to index DB's directly
– define the schema to use (including possible
joins)
– fire simple requests to Solr to actually
index/update
● Also XML feeds, files (csv), ...

48. e-Commerce
● If you want so sell, make sure users find the products
they want
– Use facets (categories, drill-down, …)
– Push high margin / hot / new products with elevation
– Pay a lot of attention to index and query time analysis
● Feed additional meta-data and use it to tune
– Ratings
– Analytics (Google, Omniture, ...)

49. Have multilingual content?
● Multi-core configuration
– Setup a dedicated Solr core per language
– Each has its own schema definitions, while you
can still use common field names
● If using one index
– Use dynamic fields and create language specific
analyzers for dedicate language
suffixes/prefixes

50. Resources
● Solr: wiki, mailing lists, downloads
http://lucene.apache.org/solr/
● Free book, articles (by core Solr devs)
http://www.lucidimagination.com/
● Bother me ;)

51. Thank you!
Questions?
email: paul dot borgermans at gmail dot com
http://twitter.com/paulborgermans
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