Online Store Topology

Online Store Topology : The online store is a search-driven site that uses SharePoint 2013 search technology to dynamically show content.  This lesson begins with the overview of search components before covering the server roles that are included in the online store topology. Then, this lesson discusses the sample online store topologies and hardware sizing, and provides the high-availability and performance considerations for planning your online store deployment.

Lesson Objectives

The objective is to provide reference and examples to help you plan the topology for your online store implementation.

Search Components

The search architecture in SharePoint 2013 contains a number of search components and databases.  SharePoint 2013 search components are as follows:

• Crawl component
• Content-processing component
• Analytics-processing component
• Index component
• Query-processing component
• Search administration component

The crawl component crawls the content sources. You can crawl lots of content sources, for example file shares, product catalog, and SharePoint content. To retrieve information, the crawl component connects to the content sources by invoking the appropriate indexing connector or protocol handler. After retrieving the content, the crawl component passes crawled items to the content-processing component.

The content-processing component processes crawled items and sends these items to the index component. The content-processing component performs operations such as document parsing and property mapping. It also performs linguistics processing such as language detection and entity extraction. The component transforms crawled items into artifacts that are included in the search index. The content-processing component also writes information about links and URLs to the link database.

The analytics-processing component performs two types of analyses: search analytics and usage analytics. This component uses information from these analyses to improve search relevance, create search reports, and generate recommendations. Search analytics is about extracting information, such as links, the number of times an item is clicked, anchor text, data related to people, and metadata, from the link database. This information is important to relevance.

Usage analytics is about analyzing usage log information received from the front-end via the event store. Usage analytics generates usage and statistics reports. The results from the analyses are added to the items in the search index. In addition, results from usage analytics are stored in the analytics-reporting database.