Working with Solr

Recomended Reading

• Thinking like Solr-its not an rdms
• http://boxesandarrows.com/faceted-finding-with-super-powered-breadcrumbs/

Normalization of data

Fields we need: - Name - Technology (multi value) - Company (multi value) - Intustry (mulit value) - Experience (multi value) - (TODO)Competence Entry - Rating - Used for relevant boosting and/or sorting.

Boost = input enhancement. Rank = output enhancement.

We start by havning no boost. Do enhancement on search results (vekting) rank.

Add

http://wiki.apache.org/solr/UpdateXmlMessages

curl http://localhost:8983/solr/update?commit=true -H "Content-Type: text/xml" --data-binary '<add><doc><field name="id">testdoc</field></doc></add>'

Query

http://wiki.apache.org/solr/SolrQuerySyntax

• http://altubuntu01.cloudapp.net/solr/select?q=totto
• http://altubuntu01.cloudapp.net/solr/collection1/select?q=%3A&wt=json&indent=true

Faceting

http://searchhub.org/2009/09/02/faceted-search-with-solr/

• http://altubuntu01.cloudapp.net/solr/select?q=java&facet=true&facet.field=technology

Boosting

simple boosts by popularity

  defType=lucene&df=text&q=%2Bsupervillians+_val_:"popularity"
  defType=dismax&qf=text&q=supervillians&bf=popularity
  q={!boost b=popularity}text:supervillians

boosts based on complex functions of the popularity field

  defType=lucene&q=%2Bsupervillians+_val_:"sqrt(popularity)"
  defType=dismax&qf=text&q=supervillians&bf=sqrt(popularity)
  q={!boost b=sqrt(popularity)}text:supervillians