Riak Search is a distributed data indexing and search platform built on top of Riak. The talk will introduce Riak Search, covering overall goals, architecture, and core functionality.

1. Riak Search
A Full-Text Search
and Indexing Engine
based on Riak
Berlin Buzzwords· June 2010
Basho Technologies
Rusty Klophaus - @rklophaus

2. Why did we build it?
What are the major goals?
How does it work?
2

3. Part One
Why did we build
Riak Search?
3

4. Riak is
a scalable, highly-available, networked,
open-source key/value store.
4

5. Writing to a Key/Value Store
Key/Value
CLIENT RIAK
5

6. Writing to a Key/Value Store
Object
CLIENT RIAK
6

7. Querying a Key/Value Store
Key
Object
CLIENT RIAK
7

8. Querying Riak via LinkWalking
Key + Instructions
Walk to
Related
Keys
Object(s)
CLIENT RIAK
8

9. Querying Riak via Map/Reduce
Key(s) + JS Functions
Map
Map
Reduce
Computed Value(s)
CLIENT RIAK
9

10. Key/Value Stores
like
Key-Based Queries
10

11. Query by Secondary Index
where Category == "Shoes"
WTF!? I'm a
KV store!
CLIENT RIAK
11

12. Full-Text Query
"Converse AND Shoes"
This is
getting old.
CLIENT RIAK
12

13. These kinds of queries
need an Index.
*Market Opportunity!*
13

14. Part Two
What are the major
goals of Riak Search?
14

15. An application built on Riak.
Your
Riak
Application
15

16. Hrm... I need an index.
Your
Riak
Application Index
Object
16

17. Hrm... I need an index with more features.
Your
??? Riak
Application
17

18. Lucene should do the trick...
Your
Lucene Riak
Application
18

19. ...shard to add more storage capacity...
Your
Application
Lucene Lucene Lucene Riak
19

20. ...replicate to add more throughput.
Lucene Lucene Lucene
Your
Application
Lucene Lucene Lucene Riak
Lucene Lucene Lucene
20

21. ...replicate to add more throughput.
Lucene Lucene Lucene
Your
Application
Lucene Lucene Lucene Riak
Lucene Lucene Lucene
Operations nightmare!
21

22. What do we really want?
Your Riak-ified
Riak
Application Lucene
22

23. What do we really want?
Your Riak
Riak
Application Search
23

24. Functionality? Be like Lucene (and more).
• Lucene Syntax
• Leverages Java Lucene Analyzers
• Solr Endpoints
• Integration via Riak Post-Commit Hook (Index)
• Integration via Riak Map/Reduce (Query)
• Near-Realtime
• Schema-less
24

25. Operations? Be like Riak.
• No special nodes
• Add nodes, get more compute and storage
• Automatically load balance
• Replicas for durability and performance
• Index and query in parallel
• Swappable storage backends
25

26. Part Three
How do we do it?
26

27. A Gentle Introduction to
Document Indexing
27

28. The Inverted Index
Document Inverted Index
day, 1
dog, 1
#1
Every dog has
his day.
every, 1
has, 1
his, 1
28

29. The Inverted Index
Documents Combined Inverted Index
and, 4
#1 Every dog has
his day.
bag, 3
bark, 2
bite, 2
The dog's bark
#2
cat, 3
is worse than
his bite. cat, 4
day, 1
dog, 1
#3
Let the cat out
of the bag. dog, 2
dog, 4
every, 1
#4 It's raining
cats and dogs. has, 1
...
29

30. At Query Time...
"dog AND cat"
AND
dog cat
30

31. At Query Time...
AND
dog cat
dog, 1
cat, 3
dog, 2
cat, 4
dog, 4
31

32. At Query Time...
Result: 4
AND
(Merge Intersection)
1
3
2
4
4
32

33. At Query Time...
Result: 1, 2, 3, 4
OR
(Merge Union)
1
3
2
4
4
33

34. Complex Behavior from Simple Structures
34

35. Storage Approaches...
35

36. Riak Search uses
Consistent Hashing
to store data on
Partitions
36

37. Introduction to Consistent Hashing and Partitions
Partitions = 10
Number of Nodes = 5
Partitions per Node = 2
Replicas (NVal) = 2
37

38. Introduction to Consistent Hashing and Partitions
Object
38

39. Document Partitioning
vs.
Term Partitioning
39

40. ...and the
Resulting Tradeoffs
40

41. Document Partitioning @ Index Time
#1 Every dog has
his day.
41

42. Document Partitioning @ Query Time
"dog OR cat"
42

43. Term Partitioning @ Index Time
day, 1
dog, 1
#1 Every dog has
his day.
every, 1
has, 1
his, 1
43

44. Term Partitioning @ Index Time
dog, 1
day, 1 has, 1
his, 1 every, 1
44

45. Term Partitioning @ Query Time
"dog OR cat"
45

46. Tradeoffs...
Document Partitioning Term Partitioning
+ Lower Latency Queries - Higher Latency Queries
- Lower Throughput + Higher Throughput
- Lots of Disk Seeks - Hotspots in Ring
(the "Obama" problem)
46

47. Riak Search: Term Partitioning
Term-partitioning is the most viable approach for our beta
clients’ needs: high throughput on Really Big Datasets.
Optimizations:
• Term splitting to reduce hot spots
• Bloom filters & caching to save query-time bandwidth
• Batching to save query-time & index-time bandwidth
Support for either approach eventually.
47

48. Part Four
Review
48

49. Riak Search turns this...
"Converse AND Shoes"
WTF!? I'm a
KV store!
CLIENT RIAK
49

50. ...into this...
"Converse AND Shoes"
Gladly!
CLIENT RIAK
50

51. ...into this...
"Converse AND Shoes"
Keys or Objects
CLIENT RIAK
51

52. ...while keeping operations easy.
Your Riak
Riak
Application Search
52

53. Thanks! Questions?
Search Team:
John Muellerleile - @jrecursive
Rusty Klophaus - @rklophaus
Kevin Smith - @kevsmith
Currently working with a small set of Beta users.
Open-source release planned for Q3.
www.basho.com