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DESCRIPTION
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, with specific focus on how Erlang is used to manage and execute an ever-changing population of ad hoc query processes.
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Erlang Factory· London· June 2010
Basho Technologies
Rusty Klophaus - @rklophaus
Riak SearchA Full-Text Search
and Indexing Engine
based on Riak
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
Key/Value
CLIENT RIAK
5
Writing to a Key/Value Store
Object
CLIENT RIAK
6
Writing to a Key/Value Store
Key
Object
CLIENT RIAK
Querying a Key/Value Store
7
Key + Instructions
Object(s)
CLIENT RIAK
Walk to Related
Keys
Querying Riak via LinkWalking
8
Key(s) + JS Functions
Computed Value(s)
CLIENT RIAK
Map
Reduce
Map
Querying Riak via Map/Reduce
9
Key/Value Stores
like
Key-Based Queries
10
where Category == "Shoes"
CLIENT RIAK
WTF!? I'm aKV store!
Query by Secondary Index
11
"Converse AND Shoes"
CLIENT RIAK
This is getting old.
Full-Text Query
12
These kinds of queries
need an Index.
*Market Opportunity!*
13
Part Two
What are the major
goals of Riak Search?
14
Your Application
Riak
An application built on Riak.
15
Your Application
RiakIndex
Object
Hrm... I need an index.
16
Your Application
Riak???
Hrm... I need an index with more features.
17
Your Application
RiakLucene
Lucene should do the trick...
18
Your Application
Lucene Lucene Lucene Riak
...shard to add more storage capacity...
19
Your Application
Lucene Lucene Lucene
Lucene Lucene Lucene
Lucene Lucene Lucene
Riak
...replicate to add more throughput.
20
Your Application
Lucene Lucene Lucene
Lucene Lucene Lucene
Lucene Lucene Lucene
Riak
...replicate to add more throughput.
21
Operations nightmare!
Your Application
Riak-ifiedLucene
Riak
What do we really want?
22
Your Application
RiakSearch
Riak
What do we really want?
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
Every dog has his day.#1
day, 1
dog, 1
every, 1
has, 1
his, 1
Inverted IndexDocument
The Inverted Index
28
The dog's bark is worse than his bite.
Every dog has his day.
Let the cat out of the bag.
It's raining cats and dogs.
#1
#2
#3
#4
Combined Inverted IndexDocuments
and, 4
bag, 3
bark, 2
bite, 2
cat, 3
cat, 4
day, 1
dog, 1
dog, 2
dog, 4
every, 1
has, 1
...
The Inverted Index
29
"dog AND cat"
AND
dog cat
At Query Time...
30
AND
dog cat
dog, 1
dog, 2
dog, 4
cat, 3
cat, 4
At Query Time...
31
AND(Merge Intersection)
1
2
4
3
4
Result: 4
At Query Time...
32
OR(Merge Union)
1
2
4
3
4
Result: 1, 2, 3, 4
At Query Time...
33
Complex Behavior from Simple Structures
34
Storage Approaches...
35
Riak Search uses
Consistent Hashing
to store data on
Partitions
36
Partitions = 10
Number of Nodes = 5
Partitions per Node = 2
Replicas (NVal) = 2
Introduction to Consistent Hashing and Partitions
37
Object
Introduction to Consistent Hashing and Partitions
38
Document Partitioning
vs.
Term Partitioning
39
...and the
Resulting Tradeoffs
40
Every dog has his day.#1
Document Partitioning @ Index Time
41
"dog OR cat"
Document Partitioning @ Query Time
42
Every dog has his day.#1
day, 1
dog, 1
every, 1
has, 1
his, 1
Term Partitioning @ Index Time
43
day, 1 has, 1
every, 1his, 1
dog, 1
Term Partitioning @ Index Time
44
"dog OR cat"
Term Partitioning @ Query Time
45
Document Partitioning Term Partitioning
+ Lower Latency Queries
- Lower Throughput
- Lots of Disk Seeks
- Higher Latency Queries
+ Higher Throughput
- Hotspots in Ring (the "Obama" problem)
Tradeoffs...
46
Riak Search: Term Partitioning
47
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.
Diving Deeper:
The Lifecycle of a Query
48
Parse the Query
49
meeting AND (face OR phone)
The Query
50
[{land, [
{term,"meeting",[]},
{lor,[
{term,"face",[]},
{term,"phone",[]}
]}
]}]
The Query as an Erlang Term (Parse w/ Leex and Yecc)
51
#land
#term
"meeting"
#lor
#term #term
"face""phone"
The Query as a Graph
52
Plan the Query
53
Catalog CatalogCatalog Catalog
Catalog CatalogCatalog Catalog
System Catalog
54
Term TermID
Term Weight
&
File Offset
System Catalog
55
#land
#term
"meeting"
#lor
#term #term
"face""phone"23 @ node B
17 @ node A 13 @ node C
Consult the System Catalog for Term/Node Weights
56
#land
#term
"meeting"
#lor
#term #term
"face""phone"23 @ node B
17 @ node A 13 @ node C
Use Term Weights to Plan the Query
57
#land
#term
"meeting"
#lor
#term #term
"face""phone"23 @ node B
17 @ node A 13 @ node C
Use Term Weights to Plan the Query
58
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
23 @ node B
17 @ node A13 @ node C
Use Term Weights to Plan the Query
59
[{node, {land, [ {node, {lor, [ {term,{"email","body","face"}, [ {node_weight,'node_c@127.0.0.1', 13} ]}, {term,{"email","body", "phone"}, [ {node_weight,'node_a@127.0.0.1', 17} ]} ]}, 'node_a@127.0.0.1' }, {term, {"email","body","meeting"}, [ {node_weight,'node_b@127.0.0.1', 23} ]} ]}, 'node_b@127.0.0.1'}]
The Node-Assigned Query as an Erlang Term
60
Execute the Query
61
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes
62
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes
63
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes
64
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes
65
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes
66
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes & Stream the Results
67
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes & Stream the Results
68
#land
#term
"meeting" #lor
#term #term
"face""phone"
#node@A
#node@B
Spawn the Query Processes & Stream the Results
69
#land
#term
'disconnect' #lor
#term #term
'disconnect''disconnect'
#node@A
#node@B
Terminate When Finished
70
Message Format
71
Message ::
{results, [Result]} |
{results, disconnect}
Result ::
{DocID, Properties}
DocID ::
term()
Properties ::
proplist()
The Message Format
72
{results, [
{375, []},
{961, [{color, "red"}]},
{155, [{pos, [1,2,5]}]}
]}
The Message Format
73
Yay for Erlang!
74
• Clean lines between load balancing and logic, single- and multi-node look the same
• Easy to create new operators, rapid development of experimental features
• Linked processes make cleanup a breeze
• Significant code reduction over early Java prototypes
Part Four
Review
75
"Converse AND Shoes"
CLIENT RIAK
WTF!? I'm a
KV store!
Riak Search turns this...
76
"Converse AND Shoes"
CLIENT RIAK
Gladly!
...into this...
77
"Converse AND Shoes"
CLIENT RIAK
Keys or Objects
...into this...
78
Your Application
RiakSearch
Riak
...while keeping operations easy.
79
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
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