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Amazon CloudSearch TCO Analysis

By Dwarakanath R – Principal Architect, 8KMiles Harish Ganesan – CTO, 8KMiles

TABLE OF CONTENTS Introduction ................................................................................................................................. 3

Approach and Methodology .......................................................................................................... 4

Use Cases ................................................................................................................................. 4

TCO Analysis ............................................................................................................................. 7

Website Search ..................................................................................................................... 7

Digital Asset Management ..................................................................................................... 8

E-Commerce ......................................................................................................................... 9

Online Classifieds ................................................................................................................ 10

Log Analysis ........................................................................................................................ 11

Conclusion ................................................................................................................................. 12

Amazon CloudSearch TCO Analysis

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Introduction

In the age of information, companies know that that success depends on their online presence, and

that means they need to focus on getting the right data to the client in the most effective and efficient

way. Searchability is key, and the choice of search platform is an important consideration.

The main contenders when it comes to web search platforms are Amazon Cloudsearch, Apache Solr,

and ElasticSearch. Different kinds of businesses will have different needs and will rely on different

feature sets, and it can be a challenge to identify the strengths and weaknesses—as well as the relative

costs—of each platform. In this paper we will compare critical features across a group of common use

cases, and provide an analysis of how each search platform stacks up, as well as the projected Total

Cost of Ownership (TCO).

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Approach and Methodology In this section, we handpick a few popular use cases and applications and identify the search

requirements of each. We also define a list of search features required for these applications to make

for a successful implementation. Further, we compare these requirements with search features are

available in Apache Solr, Elasticsearch, and Amazon CloudSearch. The outcome of this analysis will

help us to identify the best choice of search engine for a given set of search requirements.

Use Cases

Website Search: A sports media startup where developers perform all IT functions.

Digital Asset Management: A medium-sized company with regional operations, where

again developers handle all IT needs.

Ecommerce: Medium- to large-sized ecommerce company having global operations in

different countries around the world. They have separate engineering and IT operations

teams to develop applications and manage their infrastructure.

Online Classifieds: Medium- to large-sized online classifieds company having global

operations in countries around the world. They also have separate engineering and IT

operations teams to develop applications and manage their infrastructure.

Log Analysis: A large enterprise running thousands of servers, with gigabytes of logs

generated every day for analysis. Again, we see separate engineering and IT operations

teams to develop applications and manage their infrastructure.

The following matrix captures the broad requirements of each use case:

On closer inspection, you will see that every use case is unique; and their search feature requirements

are also different.

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An e-commerce storefront needs search features like auto suggest and facets. Recommendations rank at a higher priority.

Online classifieds need multi-lingual, geospatial, and fuzzy search capabilities.

For log analysis, customization, sorting, ranking, and cost optimization are top priorities.

The diagram below illustrates each use case, shown with corresponding search feature requirements

ranked in descending priority rank order.

In the above table, the priorities are ordered for each use case. For example, note that for an e-

commerce application, auto suggestion takes precedence over spell check; whereas log analysis users

place cost and customization as higher priorities than the rest.

The following matrix captures the above identified search features (for our use cases) which are

present in Apache Solr 5, Elasticsearch 1.4.4, and Amazon CloudSearch 2013 API. This matrix will allow

us to compare the corresponding required search features to those features available in each search

engine.

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In the next section, we introduce a Total Cost of Ownership (TCO) analysis for every use case

implemented using Apache Solr, Elasticsearch, and Amazon CloudSearch. The outcome of this analysis

shows the cost of infrastructure and human resources that would be needed to complete the

implementation.

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TCO Analysis

The following section provides a Total Cost of Ownership (TCO) analysis of each use case deployed

under all three search engines. The business use cases are also detailed with their technical

requirements. The chosen search engine and its conclusion are presented below for each use case.

Website Search

Search engine Choice: Amazon CloudSearch

The features that are required for the use case ‘Website search’ are available in Amazon CloudSearch, Apache Solr, and Elasticsearch. However, the most important requirement, ‘managed services’, is part of Amazon Cloud Search only. Apache Solr and Elasticsearch require ‘managed services’ to be set up to support the application.

The yearly TCO of the ‘Website search’ application using Amazon CloudSearch is 40%

lower than Apache Solr and Elasticsearch.

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Digital Asset Management

Search engine Choice: Amazon CloudSearch

The features that are required for use case ‘Digital Asset Management’ are available in Amazon CloudSearch, Apache Solr, and Elasticsearch.

The read scaling in Amazon CloudSearch is easier than in Apache Solr and Elasticsearch. Also, the CloudSearch is self-managed, which is an improvement over Apache Solr and Elasticsearch

The yearly TCO of Digital Asset Management using Apache Solr and Elasticsearch is 40% higher than Amazon CloudSearch.

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E-Commerce

Search engine Choice: Amazon CloudSearch

Amazon CloudSearch’s Elastic scaling and self-manage options make this a better product for the E-commerce use case over Apache Solr and Elasticsearch.

The yearly TCO of E-commerce applications using Amazon CloudSearch is almost 25%

lower than Apache Solr and Elasticsearch.

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Online Classifieds

Search engine Choice: Amazon CloudSearch

For the Classifieds use case, Amazon CloudSearch’s Elastic scaling and self-manage options make it a better choice than Apache Solr and Elasticsearch.

The yearly TCO of the Classifieds application using Apache Solr and Elasticsearch is

25% higher than Amazon CloudSearch.

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Log Analysis

Search engine Choice: Elasticsearch

For the Log Analysis use case, Customization ranks first among feature priorities. The Amazon CloudSearch service cannot be customized. This gives an advantage to

Elasticsearch and Apache Solr as they come from an open-source background.

The Log integrations with Elasticsearch are slightly better than Apache Solr and that

gives the advantage to Elasticsearch.

The yearly TCO of is similar for all three search engines.

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Conclusion Our analysis shows that among the top three search providers, Amazon CloudSearch is the best choice

for most of applications. In our use case “Website Search”, all three providers offered most of the required features, with the exception of the top priority—managed services. Only CloudSearch met this requirement, and at a 40% lower TCO than Apache Solr and Elasticsearch.

For “Digital Asset Management”, all main feature requirements were met by all platforms. However, read scaling is shown as being of medium importance to these users, and only CloudSearch offers that feature. Again, CloudSearch’s TCO came in 40% below its competitors.

For E-commerce, CloudSearch’s self-managed option and elastic scaling again make it the better choice over Apache Solr and Elasticsearch, this time at a 25% lower TCO. The same is true in our "Classifieds” use case—CloudSearch offers 25% savings while providing self-management options and elastic scaling.

Our "Log Analysis" use case is unique in that it ranks customization among its top priorities. Because Elasticsearch and Apache Solr come from an open source background, they offer this feature. Amazon CloudSearch, being a managed service, does not. A comparison of the TCO for this use case finds the three to be similar.

Amazon Cloudsearch’s advantages lie in its managed service and elastic scaling, which are features commonly required by users, yet not offered by Apache Solr and Elasticsearch. Additionally, Amazon CloudSearch is available at a significantly lower TCO for most applications.

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About the Authors

Dwarak is a Principal Architect at 8KMiles with more than decade hands-on experience in Cloud

Computing, Big Data, Web technologies and Product Management. He has varied and progressive

experience in architecting distributed Web and Enterprise systems and products. He is also

disciplined with deep domain knowledge in the banking, finance, retail, and e -commerce

industries. At present, he oversees technology consulting, architecture, delivery and customer

end to end transformational programs at 8KMiles.

Dwarakanath Ramachandran

Harish is the Chief Technology Officer (CTO) and Co-Founder of 8KMiles. Harish has more than

decade of experience in architecting and developing cloud computing, e -commerce and mobile

application systems. He has also built large Internet banking solutions that catered to the needs

of millions of users, where security and authentication were critical factors. He is responsible for

the overall technology direction of the 8KMiles products and services in Cloud, Big Data and

Mobility Space. Harish is a thought leader in Cloud related technologies, an Advisor and has

many followers for his blogs.

Harish Ganesan