Using the dtSearch Engine on Azure and AWS

General information on using the dtSearch Engine with Azure and AWS, including platform requirements, index storage and and data storage.

CodeProject article: Using the dtSearch Engine on Amazon Web Services (AWS) with Elastic Cloud Compute (EC2) and Elastic Block Store (EBS)

Article Excerpt:

   
CodeProject article: Using the dtSearch Engine on Amazon Web Services (AWS) with Elastic Cloud Compute (EC2) and Elastic Block Store (EBS)

Imagine harnessing the power of the dtSearch Engine to index and search Microsoft Office documents, PDFs, email, and other data with the worldwide accessibility and storage capacity of Amazon Web Services (AWS). This article will demonstrate the use of Elastic Cloud Compute (EC2) to create virtual machines and deploy applications on them, and Elastic Block Store (EBS) to create virtual disk volumes and attach them to the EC2 instances.

We’ll use the dtSearch Engine to create a console app and deploy it to EC2. We’ll use the console app to create an index of a data collection, then use the index to leverage the advanced search capabilities of the dtSearch Engine.

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CodeProject article: Using the dtSearch Engine on AWS Aurora

Article Excerpt:

   
CodeProject article: Using the dtSearch Engine on AWS Aurora

In a previous article, we demonstrated how to harness the power of the dtSearch Engine to index and search Microsoft Office documents with the worldwide accessibility and storage capacity of Amazon Web Services (AWS). In that example, we used EBS volumes to store our source documents and search index. It’s easy, however, to extend the same indexing and search features to other cloud storage services.

In this article, we’ll extend the dtSearch Engine-based example to use Amazon’s Aurora storage service, which is a hosted MySQL solution available through AWS. We build on the index and search example using EC2 and attached EBS volumes that we created in the article “Using dtSearch on Amazon Web Services with EC2 & EBS,” so we recommend working through that example first.

MySQL is great at many things, but it’s not great at full-text search. This makes the dtSearch Engine the perfect complement to Aurora. We’ll briefly discuss setting up the Aurora database and other services from AWS, then we’ll look at the implementation of two applications. One reads documents, inserts them into the Aurora database, then creates the index. The other allows end users to search the index.

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