This document summarizes a meetup about Vespa, an open source platform for low latency computations over large, evolving data sets. It provides an overview of the Vespa team, typical use cases at Oath like personalized articles and image search, and key features of Vespa like scalability, advanced ranking with tensors, and ease of use through simple configuration files. Installation and use of Vespa is demonstrated to be straightforward using Docker or RPM packages.

1. Vespa - Tokyo Meetup
Kristian Aune | March 2018

2. Kristian Aune
Tech Product Manager Vespa
Worked in Vespa Team since 2000

3. Who? アメリカ最大の携帯電話会社
総従業員数: 15万人
Verizonでデジタルメディアを取
り扱う子会社

4. Vespa Team
26 developers in Trondheim, Norway
History:
● Fast Search & Transfer: 1998 (alltheweb.com)
● Overture: 2004
● Yahoo: 2004
● Oath: 2017
● Vespa Open Source: September 2017 - we want comitters from Japan ! 😊

5. Topics
What is Vespa
● History
● The Vespa Team
Vespa usage in Oath
● Select use cases
Vespa features (highlights)
● Ease of use
● Scalability
● Advanced Ranking (tensors)

6. Vespa
A platform for low latency computations over large, evolving data sets:
● Search and selection over structured and unstructured data
● Relevance scoring
● Query time organization and aggregation of matching data
● Real-time writes
Typical use cases: text search, personalization/recommendation/targeting, real-time
data display

7. Gemini Native Ads
Ads blend into streams “natively”
● Relevance
● Budget updates
● Diversity
● Match-phase

8. Taiwan & Hong Kong
E-commerce
Auctions and search

9. Streams:
Personalized Articles
Popular and personalized articles
● Relevance
● Newsroom

10. News Direct Display
Search Results
● Freshness
News Search

11. Image Search
Flickr
Search and navigation
● Machine learned models
● Public and personal
● Massive updates after model
training

12. Fantasy Sports
Backend for all player data
● Team rosters
● Results
● Vespa is cornerstone in serving
architecture
● Grid batch updates
● Extremely low-cost serving

13. Recommendations
Recommendation engine
● Videos
● Contacts
● Questions / answers

14. Question-to-Answer
Search Direct Display
From question to answer
● In Search, return answer!

15. User-generated
Content
Example news.yahoo.com
● Top / recent comments

16. Installing
● Rpm packages or Docker images
● All nodes have the same packages/image
● CentOS (Runs on mac and win inside Docker or VirtualBox)
● 1 config variable:
http://docs.vespa.ai/documentation/vespa-quick-start.html
http://docs.vespa.ai/documentation/vespa-quick-start-centos.html
http://docs.vespa.ai/documentation/vespa-quick-start-multinode-aws.html
echo "override VESPA_CONFIGSERVERS [config-server-hostnames]" >>
$VESPA_HOME/conf/vespa/default-env.txt

17. ./hosts.xml
Ease of Use
./services.xml
<hosts>
<host name="host1.domain.name">
<alias>node1</alias>
</host>
<host name="host2.domain.name">
<alias>node2</alias>
</host>
<host name="host3.domain.name">
<alias>node3</alias>
</host>
</hosts>
<services version='1.0'>
<container id='default' version='1.0'>
<search/>
<document-api/>
<nodes>
<node hostalias=”node1”/>
</nodes>
</container>
<content id='music' version='1.0'>
<redundancy>2</redundancy>
<documents>
<document mode='index' type='music'/>
</documents>
<nodes>
<node hostalias=”node2” distribution-key=”1”/>
<node hostalias=”node3” distribution-key=”2”/>
</nodes>
</content>
</services>

18. Application
Packages
./searchdefinitions/music.sd
http://docs.vespa.ai/documentation/
search-definitions.html
search music {
document music {
field artist type string {
indexing: summary | index
}
field album type string {
indexing: summary | index
}
field track type string {
indexing: summary | index
}
field popularity type int {
indexing: summary | attribute
attribute: fast-search
}
}
rank-profile song inherits default {
first-phase {
expression {
0.7 * nativeRank(artist,album,track) +
0.3 * attribute(popularity)
}
}
}
}

19. Scalability
Vespa distributes data over
● A set of nodes
● With a certain replication factor
● In a set of groups
Nodes or distribution (config) change > Dynamic redistribution
No need to manually partition data - no shards!

20. Tensors
A data type in ranking expressions (in addition to double)
Makes it possible to deploy large and complex ML models to Vespa
Examples
● Deep neural nets
● FTRL (regression models with millions of parameters)
● Word2vec models
http://docs.vespa.ai/documentation/tensor-intro.html

21. What is a tensor?
Tensor: A multidimensional array which can be used for computation
Textual form: { {address}:double, .. } where address is {identifier:value},...
Examples
● 0-dimensional: A scalar {{}:0.1}
● 1-dimensional: A vector {{x:0}:0.1, {x:1}:0.2}
● 2-dimensional: A matrix {{x:0,y:0}:0.1, {x:0,y:1}:0.2}
Indexed tensor dimensions: Values addressed by numbers, continuous from 0
Mapped tensor dimensions: Values addressed by identifiers, sparse

22. TensorFlow import
Import machine learned ranking models trained with TensorFlow directly.
Add the files to the application package, and point to the model during ranking:
first-phase {
expression: sum(tensorflow("my_model/saved"))
}
http://docs.vespa.ai/documentation/tensorflow.html

23. TensorFlow import