Date post: | 18-Aug-2015 |
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Evan BoyleSoftware EngineerAzure Search
Adding Azure Search to your mobile application
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Enter Azure SearchFully ManagedWe wake up at 3am so you don’t have to99.9% SLAAvailable in 11 regions and growing (latest additions are in Japan, Brazil, and Australia)
Powerful AbstractionsSearch engines are complicated and we’ve tried to make them easier to understandTune and rank your results in a way that makes senseFull power of Microsoft Office NLP stack so your users find what they want in over 50 languagesEasy integration with your favorite data sources
ScalableThink in terms of documents rather than VMs & clustersPay for the capacity need as your app growsPrototype on our free tier
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Azure Search ConceptsSearch ServiceUnit of deployment, isolation, and billingContains indexes, indexers, and data sourcesComes in two SKUs: Free (shared resources), and Standard (dedicated resources)Created through the Azure Preview Portal, or programmatically via the Management REST API
IndexCollection of documents that all share a common schema (like a table in a SQL database)Can include custom scoring profiles to affect ranking behavior of queriesCreated through the Azure Preview Portal, or programmatically via the Service REST API or .NET SDK
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GeoNames
Database of geological features across US• Latitude • Longitude• Name• Altitude• Feature class
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Loading your data
PushUse the REST API or .NET client to push JSON documentsWrite a script to do this once, or on a schedule using a web job
PullCreate an indexer for one of our supported data sourcesSet up a schedule to keep things in syncCreated through the Azure Preview Portal, or programmatically via the Service REST API or .NET SDK
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Try it out!
Azure Search free tierHost up to 10,000 documents in up to three indexeshttps://portal.azure.com
Azure TryAppServiceOne hour freeNo Azure Subscription, no credit card requiredhttps://tryappservice.azure.com/
What is an analyzer?
Input:
"Well!" thought Alice to herself "After such a fall as this, I shall think nothing of tumbling down-stairs!“
Standard analyzer output:
well thought alice to herself after sucha fall as this i shall think
nothing of tumbling down stairs
Lucene English analyzer
Input:
"Well!" thought Alice to herself "After such a fall as this, I shall think nothing of tumbling down-stairs!“
Output:well thought alic to herself after sucha fall as this i shall think
noth of tumbl down stair
Microsoft Natural Language Processing (NLP)technology
Differentiating features:Developed for:
- Support for 50 languages
- Morphological analysis
- Spelling variations
- Entity recognition
Microsoft English analyzer
Input:
"Well!" thought Alice to herself "After such a fall as this, I shall think nothing of tumbling down-stairs!“
Output:well think alice to herself after sucha fall as this i shall think
nothing of tumble down stair
Stemming vs. Lemmatization
Stemmed Lemmatized
think think think
thinks think think
thinking think think
thought though think
tumble tumbl tumble
tumbles tumbl tumble
tumbling tumbl tumble
tumbled tumbl tumble
nothing noth nothing
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Related ContentAdditional ResourcesDocumentation: https://azure.microsoft.com/documentation/services/search/GitHub Repository: https://github.com/AzureSearchREST API Reference: https://msdn.microsoft.com/library/azure/dn798935.aspx.NET SDK Reference: https://msdn.microsoft.com/library/azure/dn951165.aspxNYC Jobs Demo: http://azjobsdemo.azurewebsites.net/Try App Service: https://tryappservice.azure.com/Trails & Cortana App: https://github.com/MicrosoftLearning/AzureSearch-MVA