The Future of Search in Plone

The Future of Search in
Plone
Sally Kleinfeldt
and friends
Plone Conference, San Francisco
November 3, 2011

Tuesday, November 29, 2011

Motivation

• Raise awareness

• Promote discussion

• Forge consensus


Agenda

• Introduction to IR concepts

• Description of Solr and ZCatalog

• Discussion


IR 101


IR 101

• Transformations

• Terms

• Models

• Measures


IR 101
Transformations
• Turn binary, HTML, or other document
formats into ﬁelds and strings

• Parse the strings into a set of terms

• Build indexes of the terms speciﬁc to the IR
model used

• Queries are parsed into query operators and
strings, which are parsed into terms


IR 101
String => Terms
• Tokenization - locate word boundaries

• Normalization - remove capitals and diacritics

• Stopping - remove stop words (a, of, on,
the...)

• Stemming - reduce to word stems (walks,
walking => walk)

• Recognizers - concepts, parts of speech,
names, locations...

• Must be identical for documents and queries


IR 101
Terms

• Application speciﬁc

• Words or phrases

• IR models assign weights to terms in
documents


IR 101
Term Weighting
• Simplest:Yes/No Boolean value

• Better: Term Frequency - # occurrences

• More meaningful: tf-idf

• Term Freq * Inverse Document Freq

• How many documents contain the term?

• Increase weight of rare terms and vice
versa


IR 101
Boolean Model
• First and most adopted

• Based on Boolean logic + set theory

• Does a document contain query terms - Y/N

• Intuitive, easy to implement

• No ranking, special query language, too many
or too few results

• Typical for library systems


IR 101
Vector Space Models
• Represent documents and queries as vectors
of terms

• Term values are weighted - by count or tf-idf

• Use vector operations to compare
documents with queries

• Relevance score based on cosine of angle
between doc/query vectors


IR 101
Probabilistic Models
• Compute probability that a document is
relevant to a query

• Relevance ranking functions range from
simple to complex

• Sophisticated ranking functions include

• Okapi BM25 (uses tf and idf)

• Machine learning formulas (use training
data)


IR 101
Extending the Models
• Many many reﬁnements possible

• Term interdependencies

• Fuzzy sets

• Semantic analysis, link analysis

• Combining models (Extended Boolean)

• The best search engines represent thousands
of engineering hours


IR 101
Measures
• Search engine results are measured against:

• Precision - Percent of results that are
relevant

• Recall - Percent of relevant results that are
returned

• F-Score - Harmonic mean of precision and
recall


ZCatalog and Solr


ZCatalog
• Zope/Plone search engine

• Full text and ﬁeld searching

• Probabilistic model using Okapi BM25

• OOTB ZCTextIndex very simple

• TextIndexNG adds multilingual, better parsing
components, binary transforms, synonyms


Solr
• Popular open source enterprise search
platform

• Eliminating smaller commercial search
companies

• Java, based on Lucene Java search library,
sophisticated vector space ++ model

• RESTful APIs

• Large, active community

• Powers Twitter, Wikipedia, Netﬂix...


What does Solr have
that ZCatalog Doesn’t?
• Better relevance ranking

• More search features: snippets, hit
highlighting, spelling suggestions, synonyms,
more like this, faceted search

• More conﬁgurable: stop words, ﬁeld
boosting, parsing components

• An army of engineers working on it


Plone + Solr
Today
• Two add-ons available

• collective.solr - Intercepts catalog queries
and dispatches them to Solr

• alm.solrindex - adds a new index type to
the catalog, SolrIndex

• Plus a buildout recipe:
collective.recipe.solrinstance


Conclusions from
Conference Discussion


Why Does Plone
Need Solr?
• Certain types of projects need it, for features
or because ZCatalog can’t scale to very large
sites

• We need it to keep up with the enterprise
CMS pack


Points of Agreement
• It will be impossible to completely replace
ZCatalog with Solr

• Solr indexing will never be transactional

• Removing ZCatalog from Zope would be
very difﬁcult

• Tackle small, focused ZCatalog
improvements when possible - like
improving indexing interface


Points of Agreement

• Navigation and search should be handled
separately

• Navigation needs to be transactional,
search does not

• Split out a catalog used for navigation from
the general catalog

• Explore a non-catalog utility to support
navigation, optimize for speed


Points of Agreement
• Treating Solr integration simply as ZCatalog
replacement does not take best advantage of
Solr features

• ZCatalog can’t represent the richness of
Solr, focus on the Solr API

• Take advantage of spelling suggestions,
facets, results snippets with hit highlighting,
synonyms, more like this, etc.

• Provide Solr indexing, ﬁeld weighting, etc.
conﬁguration choices in the control panel


Points of Agreement
• Neither of the current Solr add-ons provides
the best foundation for the future

• But they’ve taught us how to do things
better

• Non-Solr approaches to improved Plone
search should be deprecated

• Andreas Jung is not planning improvements
to TextIndexNG!


Points of Agreement

• Stop investing in ZCatalog as a search engine,
Solr is the future


Plone + Solr
Roadmap
• Short term: Make Solr integration easy with
an approved add-on (like LDAP)

• Build on what we’ve learned and create a
better add-on to replace collective.solr and
alm.solrindex

• Who wants to sponsor a sprint?


Plone + Solr
Roadmap
• Long term: Ship Solr integration with Plone,
but don’t require Solr

• Solr has a lot of overhead and is not always
needed

• But using it should be as easy as answering
yes to a “Build with Solr?” installation
option


The Future of Search in Plone

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