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Use Amazon CloudSearch to quickly search through data

Most applications today require a search functionality in them to let users search for content easily and quickly. But building that search feature is not a small task. It often requires specialized knowledge and massive compute resources to be able to search through massive amounts of data quickly. Any delay in delivering search results will deteriorate the user experience. If you’re planning to add such a search functionality to your application, I’d recommend giving Amazon CloudSearch a try. This will not only give you all the resources and technology you need to get started, but it’ll make the work very easy for your development teams. This is because they just need to call a few APIs to get search results.

CloudSearch works on logical components called search domains. Before you can setup CloudSearch for search, you need to create a search domain. All your data and searches will be tagged against this search domain. Using APIs, we can upload the data in which we need to make the search. If you’re already using other AWS services to store your data, such as S3, you can setup CloudSearch to use the same data.

Once the search domain is created and the data is uploaded, CloudSearch will start indexing the data to ready it for search. Once this operation is complete, CloudSearch will be able to deliver search results in real time using simple APIs. This is the best part about CloudSearch, it completely takes away the complexity of setting up and maintaining a search infrastructure. We don’t have to worry about indexing the data, optimizing the search algorithm, or any of those administrative tasks. 

Amazon CloudSearch provides several powerful search capabilities, including full-text search, faceted search, and customizable relevance ranking. Full text search lets us search for whole words or phrases within documents. So even if we don’t have the complete context of the search we’re making, we’ll be able to get search results that are relevant. Faceted search, on the other hand, allows us to refine our search results with filters. This comes in handy when the search radius, or the amount of data is huge, or when we’re performing the search on multiple datasets. 

We can even customize how the data and the search results are ranked based on relevance so that the search results are more relevant than the built-in search results. The customization we do for relevance ranking will only be applied on our dataset and for our project.

Coming to the technical advantages, CloudSearch supports multiple languages and data sources. It supports reading data from almost any data source within the AWS ecosystem, such as S3, RDS, etc. Also, the data format or the file format of the data is not limited to just one type, it supports multiple types. CloudSearch also supports all integration features offered by AWS, including the web console, the CLI, and the APIs. All operations, from creating search domains to uploading data to searching, all of these can be performed using either the console or the CLI or the APIs. And like any other AWS service, the documentation for CloudSearch is brilliant, allowing anybody to start integrating the service into their products. The APIs allow us to pass complex search queries with various filters, various data formats, and various indexing options to fine tune our search results. We can even monitor the performance of the service and optimize it using various tools that are provided by CloudSearch. I’d highly recommend trying out the service to understand it’s capabilities yourself.


And if you like what you see here, or on my Medium blog and Dev.To blog, and would like to see more of such helpful technical posts in the future, consider supporting me on Patreon and Github.

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