This document provides an overview of how to retrieve information from Apache Solr. It discusses various query types and parameters including basic queries, matching multiple terms, fuzzy matching, range searches, sorting, faceting, and techniques for tuning relevance. The topics are covered through examples and explanations of Solr's query syntax and how it handles indexing and searching documents.

1. Retrieving
Information from Solr
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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. 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. Detailed
Architectural
Diagram

5. Basic Queries and Parameters

6. Exploring Solr’s
Query Form

7. Basic Queries and Parameters

8. Matching Multiple Terms

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. Negation
● Exclude documents containing specific terms

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. Grouped Expressions
● Represent arbitrarily complex queries

13. Exact Phrase Queries
● Search for exact phrase “new house”
● Can Combine with Boolean Queries

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. Fuzzy Matching

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. 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. Range Searches

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. 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. Sorting

22. Sorting
● Sort by score
● Values of Fields
● Ascending or Descending
● Multiple Fields

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. Geospatial Searches

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. Returning Calculated Distances
● You can use a pseudo field (a field that is calculated at query
time) to achieve this

27. Filter Queries

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. Faceting and Stats

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. Types of Facet
● Field Facets
● Range Facets
● Pivot Facets

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. Range Facet
● Divide a range into equal size buckets

34. Range Faceting

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. 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. 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. Facet by Query
● Sometimes, you need unequal ranges
● You can use the facet.query parameter
● Provides counts for subqueries

39. Tuning Relevance

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. 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. Lucene Scoring
Similarity scoring formula
• Used to rank results by measuring the similarity between a query
and the documents that match the query

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. Boosting and biasing
Lucene uses a standardized scoring approach
Lucene does not know:
● Your data
● Your users
● Their queries Their preferences

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. 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. Request parameters
We are going to make substantial use of request parameters, so
let's recap:

48. How can you improve search results?
Using a sledge hammer
● Ignore score, sort on X
● Filter by X, retry if 0 results

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. Retrieving
Information from Solr
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