Riak Search - Berlin Buzzwords 2010

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

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

Part One Why did we build Riak Search? 3

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

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

Writing to a Key/Value Store Object CLIENT RIAK 6

Querying a Key/Value Store Key Object CLIENT RIAK 7

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

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

Key/Value Stores like Key-Based Queries 10

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

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

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

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

An application built on Riak. Your Riak Application 15

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

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

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

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

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

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

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

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

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

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

Part Three How do we do it? 26

A Gentle Introduction to Document Indexing 27

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

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

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

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

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

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

Complex Behavior from Simple Structures 34

Storage Approaches... 35

Riak Search uses Consistent Hashing to store data on Partitions 36

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

Introduction to Consistent Hashing and Partitions Object 38

Document Partitioning vs. Term Partitioning 39

...and the Resulting Tradeoffs 40

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

Document Partitioning @ Query Time "dog OR cat" 42

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

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

Term Partitioning @ Query Time "dog OR cat" 45

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

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

Part Four Review 48

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

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

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

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

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

Riak Search - Berlin Buzzwords 2010

by Rusty Klophaus

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