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Retrieving Information From Solr

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Retrieving Information From Solr

  1. 1. Retrieving Information from Solr JOSA Data Science Bootcamp
  2. 2. ● Head of Technology @ OpenSooq.com ● Technical Reviewer for “Scaling Apache Solr” and “Apache Solr Search Patterns” (Books) ● Contributor in Apache Solr ● Built 10 search engines in the last 2 years Ramzi Alqrainy
  3. 3. Topics to be covered ● Exploring Solr’s Query Form ● Basic Queries and Parameters ● Matching Multiple Terms ● Fuzzy Matching ● Range Searches ● Sorting ● Pseudo Fields ● Geospatial Searches ● Filter Queries ● Faceting and Stats ● Tuning Relevance
  4. 4. Detailed Architectural Diagram
  5. 5. Basic Queries and Parameters
  6. 6. Exploring Solr’s Query Form
  7. 7. Basic Queries and Parameters
  8. 8. Matching Multiple Terms
  9. 9. Boolean Queries ● Search for two different terms, new and house,requiring both to match ● Search for two different terms, new and house, requiring only one to match ● Default operator is OR, can be changed using the q.op query parameter.
  10. 10. Negation ● Exclude documents containing specific terms
  11. 11. Inverted Index—Revisited ● All terms in the index map to 1 or more documents. ● Terms in inverted index are stored in ascending lexicographical order ● When searching for multiple terms/ expressions, Solr (and Lucene) returns multiple document result sets corresponding to the various terms in the query and then does the specified binary operations on these result sets in order to generate the final result set. ● Scoring is performed on the result set o generate final result
  12. 12. Grouped Expressions ● Represent arbitrarily complex queries
  13. 13. Exact Phrase Queries ● Search for exact phrase “new house” ● Can Combine with Boolean Queries
  14. 14. Proximity Searches ● Represent arbitrarily complex queries ● Solr/Lucene not only stores the documents that contain the terms, but also their positions within a document (term positions), which is used to provide phrase and proximity search functionality ● The number of the “~” is called a slop factor and has a hard limit of 2, above which the number of permutations get too large to provide results within a reasonable time
  15. 15. Fuzzy Matching
  16. 16. Fuzzy Edit-Distance Searching ● Flexibility to handle misspellings and different spellings of a word ● Character variations based on Damerau-Levenshtein distances ● Accounts for 80% of human misspellings
  17. 17. Wildcard Matching ● Robust functionality, but can be expensive if not properly used. ○ First all terms that match parts of the term before wildcard expression are extracted ○ Then all those terms are inspected to see if they match the entire wildcard expression ○ Expensive if your expression matches a large number of terms (for example the query e*)
  18. 18. Range Searches
  19. 19. Query on a Range ● Solr Date Time uses a format that is a restricted form of the canonical representation of dateTime in the XML Schema specification (inspired by ISO 8601). All times are assumed to be UTC (no timezone specification) ● Based on a lexicographically sorted order for the field being queried ● Solr has Trie field types (tint, tdate, etc.) that should be used when you are doing a large number of range queries ● Various field types will be covered later in the course
  20. 20. Solr Date Syntax ● Uses UTC and Restricted DateTime format ● Allows rounding down by YEAR, MONTH, WEEK, DAY, MINUTE, SECOND ● NOW represents current time and using DateMath, we can specify yesterday, tomorrow, last year, etc.
  21. 21. Sorting
  22. 22. Sorting ● Sort by score ● Values of Fields ● Ascending or Descending ● Multiple Fields
  23. 23. Pseudo Fields ● Dynamically added at query time and calculated from fields in the schema using in- built functions ● Through functions, you can manipulate the values of any field before it is returned ● Can also be used to modify the order of documents by sorting on the pseudo field
  24. 24. Geospatial Searches
  25. 25. Geospatial Searches ● Solr provides location-based search ● Define a “location” field that contains latitude and longitude ● You can use a Query parser called “geofilt” to search on this field, specifying the point and radius around it ● Another query parser bbox uses a square around the point to do faster but approximate calculations ● Other types of searches (grids, polygons, etc. are possible and covered in advanced course
  26. 26. Returning Calculated Distances ● You can use a pseudo field (a field that is calculated at query time) to achieve this
  27. 27. Filter Queries
  28. 28. The fq and q Parameters ● Indistinguishable at first glance: same query parameters passed to either parameters will return same documents. ● But, ○ fq serves a single purpose, to limit what is returned ○ q limits what is returned AND supplies the relevancy algorithm with a set of terms used for scoring ● fq results are cached and can be reused between searches ● Using fq we can avoid unnecessary relevancy calculations ● You can use multiple fq’s in a request (each individually cached), but only one q parameter
  29. 29. Faceting and Stats
  30. 30. Faceted Search ● High-level breakdown of search results based on one or more aspects (facets) of their documents ● Allows users to filter by (drill down into) specific components ● Can facet on values of fields, or facet by queries
  31. 31. Types of Facet ● Field Facets ● Range Facets ● Pivot Facets
  32. 32. Field Faceting ● Request back the unique values found in a particular field ● Most commonly used ● Works for single- and multi-valued fields ● Values are based on the indexed values of the field ● Common practice is to facet on a String field and search on a text field (to be discussed later). So, some schema preparation is required for faceting
  33. 33. Range Facet ● Divide a range into equal size buckets
  34. 34. Range Faceting
  35. 35. Date Range Facets ● Recall Solr Date Syntax covered earlier in class ● Uses UTC and Restricted DateTime format ● Allows rounding down by YEAR, MONTH, WEEK, DAY, MINUTE, SECOND ● NOW represents current time and using DateMath, we can specify yesterday, tomorrow, last year, etc.
  36. 36. Stats and Facets ● Can get aggregations on various fields ● From Solr 5.x onwards, stats on pivot facets is also available ● See https://lucidworks.com/blog/you-got-stats-in-my-facets/ for a great explanation of faceting
  37. 37. Pivot Facets ● Functions like pivot tables in spreadsheet apps ● Aggregate calculations that pivot on values from multiple fields ● Example: give me a count of 3,4 and 5 star hotels in the top three cities ● Solr 5.x also allows you to stats calculations on pivots
  38. 38. Facet by Query ● Sometimes, you need unequal ranges ● You can use the facet.query parameter ● Provides counts for subqueries
  39. 39. Tuning Relevance
  40. 40. Precision and recall Precision and recall Are the top results we show to users relevant? Recall Of the full set of documents found, have we found all of the relevant content in the index?
  41. 41. Relevancy Our goal is to give users relevant results Relevance is a soft or fuzzy thing ● Depends upon the judgment of users Scoring is our attempt to predict relevance Similarity classes hold the implementations ● DefaultSimilarity ( TF-IDF ) ● BM25Similarity ● DFRSimilarity ● IBSimilarity ● LMDirichletSimilarity ● LMJelinekMercerSimilarity
  42. 42. Lucene Scoring Similarity scoring formula • Used to rank results by measuring the similarity between a query and the documents that match the query
  43. 43. Domain knowledge Examples ● Cheaper ● Newer or more recent ● More popular or higher user clicks Higher average user ratings Interesting combinations ● Value = average user ratings ÷ price ● Staying power = recent popularity ÷ age
  44. 44. Boosting and biasing Lucene uses a standardized scoring approach Lucene does not know: ● Your data ● Your users ● Their queries Their preferences
  45. 45. Domain knowledge What do you know about your data? ● Any specific rules about your data that wouldn't be suitable in a generic IR scoring algorithm ● In many data domains, there are fundamental numeric properties that make some objects generally "better" than others
  46. 46. Domain knowledge More subtle examples ● Novelty factor ○ Quantity of user ratings × stdDev of ratings Profit margin ● Profit margin ○ Retail price ‒ factory cost Scarcity ● Scarcity ○ Quantity remaining ● Popularity by association or categorization ○ Sweaters sell better then swimsuits in November ● Manual ranking ○ New York Times bestseller list
  47. 47. Request parameters We are going to make substantial use of request parameters, so let's recap:
  48. 48. How can you improve search results? Using a sledge hammer ● Ignore score, sort on X ● Filter by X, retry if 0 results
  49. 49. How can you improve search results? ● Boost functions and queries ● Apply domain knowledge based on numeric properties by multiplying functions directly into the score
  50. 50. Retrieving Information from Solr JOSA Data Science Bootcamp

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