Skip to main content

Amazon CloudSearch - Technology Review


Amazon CloudSearch is a fully managed service in the cloud that makes it easy to

set up, manage, and scale a search solution. Amazon CloudSearch can search large

collections of data such as web pages, document files, forum posts, or product

information. CloudSearch makes it possible to search large collections of mostly

textual data items called documents to quickly find the best matching results.

Search requests are usually a few words of unstructured text. The returned results

are ranked with the best matching, or most relevant, items listed first.



As a managed search service, Amazon CloudSearch determines the size and

number of search instances required to deliver low latency, high throughput search

performance. Amazon CloudSearch also automatically scales to handle increases in

the amount of search traffic. When a search instance nears its maximum query load,

CloudSearch deploys a replica of the search instance. Conversely, when search

traffic drops, Amazon CloudSearch removes unneeded replicas to minimize costs.


Features


Amazon CloudSearch provides features to index and search both structured data

and plain text, including faceted search, free text search, Boolean search

expressions, customizable relevance ranking, query time rank expressions, field

weighting, searching, and sorting of results using any field, and text processing

options including tokenization, stopwords, stemming and synonyms. It also

provides near real-time indexing for document updates.



Processing Steps


To use Amazon CloudSearch, we need to follow these steps:


Create a search domain

        We can create Amazon CloudSearch search domain for each collection of

        data that we want to make searchable. A search domain encapsulates data

        and the hardware and software resources required to operate a search

        engine. Each search domain has one or more search instances. A search

        instance is a server instance that has a finite amount of RAM and CPU

        resources for indexing data and processing requests. The number of search

        instances in a domain depends on the documents in the collection and the

        volume and complexity of search requests.



Configure indexing options for the data


        Each document that are added to the search domain has a collection of fields

        that contain the data that can be searched or returned. Every document

        must have a unique document ID and at least one field. We need to define

        an index field for each of the fields that occur in the documents.


Upload data for indexing


        To make the data searchable, we need to format it in JSON or XML and

        upload it to search domain for indexing. In most cases, Amazon CloudSearch

        automatically indexes the data, and the changes are visible in search results

        in just a few minutes. However, certain changes to your domain

        configuration put the domain in the “needs Indexing” state. For those

        changes to take effect, we must explicitly run indexing to rebuild the index.


Submit search requests from website or application


        We can submit search requests to the domain's search endpoint as

        HTTP/HTTPS GET requests. Also, can specify a variety of options to constrain

        the search, request facet information, control ranking, and specify what you

        want to be returned in the results. Amazon CloudSearch looks up the search

        terms in the index and identifies all the documents that match the request.

        To generate a response, Amazon CloudSearch processes this list of search

        hits to filter and sort the matching documents and compute facets. Amazon

        CloudSearch then returns the response in JSON or XML.



Amazon CloudSearch Pricing


We need to pay only for what we use. There are no set-up fees or upfront

commitments to begin using Amazon CloudSearch. The major portion of a

typical domain’s costs come from search instance usage. All source documents

and updates to the domain are stored behind-the-scenes on Amazon S3 for data

durability and recovery, but customers get this for free, which is a significant

cost saving over self-managed search infrastructure. Customers are billed

according to their monthly usage across Search instances, Document batch

uploads, Index Documents requests and Data transfer.



Pros and Cons


Amazon CloudSearch provides several benefits including easy configuration,

auto scaling for data and traffic, self-healing clusters, and high availability with

Multi-AZ. Amazon CloudSearch supports many SDKs along with RESTful API calls.

The most popular SDKs are in Java, Ruby, Python, .Net, PHP, and Node.js.

Amazon CloudSearch indexes and searches both structured data and plain text.

It includes most search features that developers have come to expect from a

search engine, such as faceted search, free text search, Boolean search,

customizable relevance ranking, query time rank expressions, field weighting,

and sorting of results using any field.


One of the Cons with Amazon CloudSearch is the lack of control on spending.

It's very hard to pinpoint how much we will spend here. Since it goes by active

searches, the small price quote we get in the beginning will skyrocket if we have

more search data for a certain month or have bandwidth issues. There is no way

should set a maximum price. Also, the ability to customize the features are

minimal ad require thorough knowledge on AWS services.




Conclusion


Amazon CloudSearch is a complete search solution which will allow you to scale

and upload new data and make available to search. With Amazon CloudSearch,

one should be able to create their search domain, set search attributes, upload

the data, and start testing them out in no time.





References


• Amazon CloudSearch Service - https://aws.amazon.com/cloudsearch/

• Amazon CloudSearch Developer Guide:

https://docs.aws.amazon.com/cloudsearch/latest/developerguide/cloudsearch-dg.pdf

• A step-by-step guide to setting up Amazon Cloud Search:

https://www.cuelogic.com/blog/a-step-by-step-guide-to-setting-up-amazon-cloud-search-with-examples

• AWS Cloud Search Choices:

https://cloudacademy.com/blog/elasticsearch-vs-cloudsearch/

Comments

Post a Comment

Popular posts from this blog

DW Architecture - Traditional vs Bigdata Approach

DW Flow Architecture - Traditional             Using ETL tools like Informatica and Reporting tools like OBIEE.   Source OLTP to Stage data load using ETL process. Load Dimensions using ETL process. Cache dimension keys. Load Facts using ETL process. Load Aggregates using ETL process. OBIEE connect to DW for reporting.  

Cloudera QuickStart virtual machines (VMs) Installation

Cloudera Distribution including Apache Hadoop ( CDH ) is the most popular Hadoop distribution currently available. CDH is 100% open source. Cloudera quick start VMs include everything that is needed to tryout basic package based CDH installation. This is useful to create initial deployments for proof of concept (POC) or development.

CentralBins ChatGPT: Revolutionizing Data Dialogue

  Introduction to CentralBins ChatGPT CentralBins ChatGPT is an innovative AI-powered chatbot designed to revolutionize data dialogue. It leverages the cutting-edge technology of OpenAI's GPT-3 to transform the way data professionals, educators, and business leaders interact with big data and analytics. This advanced chatbot is developed by CentralBins, a leading provider of AI solutions in the field of big data analytics, data warehousing, and business intelligence. With CentralBins ChatGPT, users can engage in natural language conversations to gain insights, seek solutions, and explore the depths of data-driven decision-making. CentralBins ChatGPT is designed to streamline the process of querying, analyzing, and interpreting data, making it an indispensable tool for unlocking the potential of data assets across various domains and industries. Understanding the Role of ChatGPT in Data Dialogue ChatGPT plays a pivotal role in enhancing data dialogue by enabling seamless and intuiti...