Azure Search Deep Dive

Gunnar PeipmanASP.NET/IIS MVP

http://gunnarpeipman.com

Agenda• Introduction to search• Search in Microsoft world• Planning for search• Azure Search demos

Introduction to search

Two types of people1. Navigation people2. Search people

Q: What do navigation people with search? A: They type in their favorite link catalog URL, search for it and then start navigating again

Search over time• Simple search scripts• Database search• Search engines• NoSQL• Search services

Components of search systems• Crawler – in web systems finds resources to index• Indexer – adds found resources to search index• Analyzis – performs analyzis of on indexed content• Query – performs search and returns results

Search in Microsoft world

Search technologies this far• Desktop search – indexes desktop content (files, mailboxes, media)• Bing – web search• SharePoint search – search from enterprise data, web, and LOB

systems

Azure Search• Desktop search – doesn’t scale• Bing – oriented to web• SharePoint Search – extremely powerful but expensive • Azure Search – lightweight custom search offered as a service

Elastic in the cloud?• Built on top of Elastic Search• Still it is not cloud-hosted Elastic Search• Azure Search has its own JSON-based API-s

Planning for search

Tips’n’tricks• Search index must contain only two types of data:• Data to search• Data to display results

• Analyze what data is minimally needed for search• Often search index is flat presentation of some domain classes• If needed use more than one index• Don’t allow users to come out with requirement „search for

everything“ – visualize them what everything means

Creating search index

Creating search index• Join Microsoft Azure• Create free or paid Azure Search service• Use one of these to create search indices:• Use Azure Preview Portal• Use Azure Search library (full .NET only)• Use plain JSON requests (full .NET and CoreCLR)

• Configure indices

Managing search index

Managing index• Update index after insert, update and delete• Update index after related data is changed• Support indexing from data sources• Make it easy to refresh all items in index• Keep users away from Azure portal and JSON

I want indexing to be easy and automated process

Managing search indexSingle product scenario:1. Product changed2. Detect change type3. Update index

This flow can be implemented behind inser, update and delete operations.

Managing search indexMultiple product scenario:1. Query for products to index2. Save changes to index with up to 16K batches

Do this when:3. Product category name changes4. Products in category have some new attribute5. Products for query are changed

Managing search indexAll products scenario:1. Create query to retrieve all products 2. Use paging to keep load normal3. Send changes for page to index4. You may want to write console application or service for this

Do this when:5. You need to re-create search index 6. Big number of products have changed and there’s no query for detecting

changed products

DemoUpdating products in Azure Search

DemoSearching from index

DemoPaging

Paging scenario• Support different search providers:• Azure Search• Search from SQL Server• SharePoint Search• Custom NoSQL database

• Search clients should know nothing about current search provider• We need one query and one

response class to keep clients away from details

Paging• Azure Search supports paging• PagedResult<T> - my construct for all pagings• Base class – carries data also to pagers in UI• Generic class – carries typed results to callers• Query class and PagedResult classes are the only data

transfer mechanism between layers

DemoScoring profiles

Scoring profiles• Scoring profiles help search to rank results• Scoring types:

• Weights – how importamt is match for fields• Freshness – how new or old item is• Magnitude – how high or low numeric value is• Distance – proximity and geographic location

• Function aggregation:• Sum• Average• Minimum• Maximum

• One scoring profile per search request!

I want to get acurate results when searching

products

DemoFaceted navigation

Faceted navigation• Facets are those blocks on listings where

visitors can set additional filters• Example: price range, manufacturers, flight

companies etc

I want simple filters to narrow down search

results

DemoSuggesters

Suggesters• Suggester is „lightweight search“• Used to look up results from search index• Works well for auto-complete search boxes

Sometimes I want suggestions when typing

phrase to search box

DemoTag boosting

Tag boosting• Add groups of tags to products• Each group can boost up search results rank for

given products• Example: based on customer preferences

boost up the rank of specific brands

I always want to see my favorite products first in

listings

Indexers

Indexers• Automated index updates• Build queries• Define data sources• Define indexers• If needed run on schedulers

Can I skip coding and import fresh data to index

from data source?

Thanks