Faceted Navigation presented by me at the Wildcardconf 2015 (http://wildcardconf.com). Examples when Faceted Navigation done right and wrong, observations what should be avoided and what should be implemented to achieve goal of Faceted Navigation.

1. Faceted Navigation
Elastic filtering with
ElasticSearch
Ruslan Zavacky
Sr. Software Architect
@ruslanzavacky
ruslan.zavacky@gmail.com

2. What is Faceted
Navigation?

8. Meanwhile in Latvia

11. WUT?
That’s simple Filtering

12. Requirements

13. Faceted Navigation helps users to browse
large sets of products quickly and intuitively.

14. What
we
should
know
about
Faceted Navigation?

15. Facets shown to customer should be relevant
to their Search or Navigation needs

16. Search and Refine Samsung

17. Too much

18. and what is this s**t?

19. Do not show facets, that are not
making any changes to the result set

20. Search and Refine Samsung

21. Avoid
dead end
and
zero results

22. Zero Results, how I even got it?

23. How ebay handles it

24. How Amazon handles it

25. Allow users to review and adjust
their filtering options at any stage

26. The Flamenco
Search Interface
Project
http://flamenco.berkeley.edu

27. Our experience

28. 400,000+ Product
4,000+ Specifications
20,000+ Specification Values
Brands, Categories, Prices, Deals, Ratings

29. Dominate
your
data

30. Frontend > Backend Backend > Frontend
More logic on Frontend More logic on Backend
Faster Queries Complicated Queries
Complicated Frontend Complicated Frontend
Hard to Scale Easy to Scale
Smaller Response Size Bigger Response Size
We’ve chosen the right solution
Left solution Right solution

31. Backend is doing all the heavy work.
Building all queries, calculating differences
between initial set of data and filtered.

32. Frontend is responsible only for drawing
and re-drawing facets after change.

33. Road to Hell non-
empty results

34. Javascript Performance

35. Internet Explorer

37. ElasticSearch

39. We’ve chose it long before
Faceted Navigation
And Battle Tested it

40. Then we’ve realised that we can
squeeze a lot from it to
achieve our business goals

41. We use ElasticSearch for
Storing Data
Analytics
Full-Text Searching
and Faceted Navigation

42. REST
Aggregations
=
GROUP BY on steroids

43. Stats Agg - count, min, max, avg, sum
Terms, Missing, IPv4, Geo Distance,
Historgram, Stats, Min, Max, Sum,
Percentiles, Cardinality, Geo Bounds,
Top Hits, Filter, Filters, Nested, Children,
Significant Terms, Range, Date Range,
Date Histogram, GeoHash Grid
Terms Agg - count distinct values

44. Title Bushnell AR Optics 3-9x40 Riflescope
Specifications
Condition: New, Used
Magnification: 3x, 2x, 1.5x
Length: 12.2in, 11.1in, 12.1in
Reticle: BDC
Brand Bushnell
Categories Riflescopes, Optics
Data Example

45. Brands, Categories - are simple Terms aggregations

46. Specifications are the hard part
Condition, Magnification, Length, Reticle
We should know product counts with such specifications

47. To achieve that we’ve separated specifications and
specification values into different fields in Product Object

48. To achieve that we’ve separated specifications and
specification values into different fields in Product Object

49. #1 First query to ElasticSearch
Return products, prepare initial aggregations
do not apply any specific filters, except global ones
Result
We will have all products that match initial criteria
(page, limit, orderable) and all facets that selected
products have.

50. #2 Second query to ElasticSearch
Run the same query as previous, but add filtered results
and make this query to return only aggregations (facets)
Result
You’ll get filtered aggregations that match your criteria

51. Merge Query Results Together
and
voilà

52. DEMO TIME

53. Analytics

54. More than 1600 hours of
Research, Design and Development

55. More than 80 darts games

56. X% Conversion

57. XX:XX average session duration

58. Thank You!
Q&A time