Cosmos DB, Graph and Azure Search, building a compelling cloud solution

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1 Azure Saturday 2018 Cosmos DB, Graph and Azure Search, building a compelling cloud solution Steef-Jan Wiggers| Codit

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2 Azure Saturday 2018 Thank you, sponsors!

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steefjan.wiggers@codit.eu +31 653 12 29 57 @SteefJan nl.linkedin.com/in/steefjan

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Not my first visit to Munich!

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No content

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The next race …

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Who we are Customers Entities 2000 Belgium 2004 France 2013 Portugal 2016 Switzerland 2016 UK 2016 The Netherlands 2017 Malta 180 worldwide

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Codit & Microsoft

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DATA & AI What the future will bring

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What can you expected in this session • Scenario • Cosmos DB • Graph model • Azure Search • Demo

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Scenario

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Scenario • Old knowledge base implementation with SOLR struggled with related content • Dependency on hosting - and service partner • High cohesion and tight coupling Knowledge Platform Tax Returns

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Business: • Increase quality of content • Better user experience • New business models & revenue streams • Independent Technology: • Completely PaaS • Azure (Pay as you go) • No IT management support only DevOps • Independent A new future proof knowledge base

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High Level Architecture Editing CMS Content Creation Knowledge Platform Integrate Cleanse Match & Merge Connectors Ingestion Standardize Validation Meta data Enrichment Value decay XRef Data quality, workflows & monitoring Content Constitution Content collection Relation Store Index Search Models Content API Knowledge base

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Solution Building Blocks Content collection Azure Search Document DB Graph Integrate Match & Merge Content API Importer/.NET Web App/.Net Search Index Relations Store

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Solution Architecture CMS Output FRONT-END Content Related Content

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• What are the costs (ROI, TCO) • Will it work for content and related content (Quality) • Meet business requirements • New revenue streams, business models Business challenges

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• Architectural Fit – POA • Microsoft Support • Training • Compare with other search solutions, and graph solutions • Performance • Scale • Complexity Technology challenges

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Cosmos DB

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Cosmos DB Column-family Document Graph Turnkey global distribution Elastic scale out of storage & throughput Guaranteed low latency at the 99th percentile Comprehensive SLAs Five well-defined consistency models Table API Key-value MongoDB API A globally distributed, massively scalable, multi-model database service Cassandra API

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• Turnkey global distribution • Multiple datacenters • Auto replication • 99,99% Availability • All resources are horizontally partitioned and vertically distributed • Replication topology is dynamic based on consistency level and network conditions Global Distribution

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Multi-model + multi-API • Different models: • Graph • Key-Value • Document DB • No schema or index management • Automatic indexing • API support: • SQL • JavaScript • Gremlin • MongoDB • Azure Table Storage • Cassandra

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Scale • Pay as go for storage and throughput • Elastic scale across regions • Partitions

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• Five levels of consistency • Programmatically change at anytime • Can be overridden on a per-request basis • Writing correct distribution applications is hard • Global distribution forces CAP theorem • Intuitive and practical with clear PACELC tradeoffs Consistency

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• Simultaneously read/write Latency

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SLA • Fully managed service • 99,99% SLA for latency • Guaranteed throughput, consistency and high availability

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Request Units • Request Units (RU) is a rate-based currency • Abstracts physical resources for performing requests • 1 RU = 1 read of 1 Kb document • Each request consumes fixed Rus • Provisioned in terms of RU/sec and RU/min • Rate limiting based on provisioned throughput • Can be in- and decreased instantly • Metered hourly

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Capacity Planner

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• Azure Search • Apache Spark Connector • Azure Functions Integrations

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Graph Model

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• TinkerPop is a developer group creating an open-source stack for graphs (http://tinkerpop.apache.org/) • Graph database and analytics systems Graph

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Data is your model, model is your data

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• LinkedIn (Business) • Facebook (Social Media) • Walmart (Recommendation) • Google (Search) • Airbnb (Search) • Cisco (Master Management) Graph implementation examples

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• Gremlin is a graph traversal language • Vertices, Edges, and Properties Graph API

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Add Vertices (V)

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Add Edge (E)

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Execute Gremlin

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Demo Graph Explorer

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Azure Search

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Azure Search

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“Search service” ▪ Scope for capacity ▪ Bound to a region ▪ Has keys, indexes, indexers, data sources Provisioning ▪ Azure Portal ▪ Azure resource management API Elastic scale ▪ Capacity can be changed dynamically ▪ Replicas ~ more QPS, HA ▪ Partitions ~ more documents, write throughput Provisioning

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“Index” ▪ Container for data, think “table” ▪ Has schema, CORS options, search options ▪ Create in portal or during app initialization Typical schema ▪ Fields definition: name, type, key Search specifics ▪ Field attributes – searchable, facetable, etc. ▪ Linguistics and analysis ▪ Suggesters for auto-complete ▪ Scoring profiles for ranking tuning Index

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Push - using indexing API ▪ POST to /indexes//docs/index ▪ Up to 1000 actions per batch ▪ Actions can be upload, merge, delete, etc. ▪ WebJobs are great for regular execution Pull - using indexers ▪ Azure SQL DB and Document DB ▪ Change detection, deletion markers ▪ Point it at the data source, define policy, done Index data

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Search + typical data operations ▪ Simple search options, + - * () “” ▪ Filter, sort, project, page over results ▪ Options work with search and suggest Search from client or server ▪ Use query keys when searching from clients ▪ CORS allows direct calls from browsers Render from search results ▪ Include necessary non-searchable data ▪ E.g. URLs for pictures, keys to main content Search

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Scoring profiles ▪ Field weights ▪ Scoring functions ▪ magnitude, freshness, distance, tags 3 main patterns ▪ Known data directly available in the index ▪ Personalization using tag boosting ▪ Analytics, compute externally and push to the index Customization

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Integrate with Azure Search

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Integrate with Azure Search

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Demo Search

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Scenario Revisted

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Development Content collection Azure Search Document DB Graph Integrate Match & Merge Content API Importer/.NET Web App/.Net Search Index Relations Store

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Solution Architecture CMS Output FRONT-END Content Related Content

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Search Content FRONT-END MENU BAR Content Dividend tax SEARCH Related Content Related Content Content B A A

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Demo API

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• Cosmos DB Graph fit for related content purpose • Cosmos DB Document fit for content • Cosmos DB + Search good combination • Meets business requirements • Complete Architecture on PaaS • Cool eh! Summary

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• CosmosDB • CosmosDB Graph Explorer • Azure Search • Azure Search Resources Resources

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Azure Saturday 2018 We appreciate your feedback! https://form.responster.com/mWJ1VI

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Azure Saturday 2018 Thank you!