Vespa - Tokyo Meetup #yjmu

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March 20, 18

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https://yj-meetup.connpass.com/event/79031/

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Vespa - Tokyo Meetup Kristian Aune | March 2018

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

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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

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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 ! 😊

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

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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

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

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Taiwan & Hong Kong E-commerce Auctions and search

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

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News Search News Direct Display Search Results ● Freshness

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

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

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Recommendations Recommendation engine ● Videos ● Contacts ● Questions / answers

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

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

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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: echo "override VESPA_CONFIGSERVERS [config-server-hostnames]" >> $VESPA_HOME/conf/vespa/default-env.txt 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

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Ease of Use ./services.xml <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> ./hosts.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>

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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) } } } }

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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!

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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

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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

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[beta]
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

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TensorFlow import