Introduction

When beginning a search consulting project, one of the initial questions that arise is which search engine to utilize. In this article, we will compare Elasticsearch with its fork, OpenSearch, and explore its features, functionalities, and considerations for users.

Overview

Both Elasticsearch and OpenSearch offer a wide range of features, including common, competing, and diverging functionalities. Common functionality is derived from Lucene, providing tasks such as document indexing, merging, similarities, and filter caches. Upgrades to newer versions of Lucene are inherited by both engines.

Competing functionality drove the initial fork, addressing proprietary features in Elasticsearch. OpenSearch provides open-source alternatives for tasks like authentication, authorization, index management, and alerting. While the overall purpose is the same, the specific implementations may differ.

Distinct Features of OpenSearch:

OpenSearch introduces a range of unique features that set it apart from Elasticsearch. Some notable examples include advanced security analytics, OpenSearch Dashboards for data visualization and exploration, machine learning capabilities, and the OpenSearch Query Language (PPL) for more powerful and flexible querying. These features offer users additional functionalities and options when building their search and analytics solutions.

Licensing Distinction:

Another key difference lies in the licensing aspect. OpenSearch is fully open source, while Elasticsearch has certain functionalities that may not be available for free under the Basic license. This distinction reinforces the open-source nature of OpenSearch and its commitment to providing a comprehensive and freely accessible search solution.

Comparison table between OpenSearch and ElasticSearch

OpenSearch dashboards and Kibana Dashboards

OpenSearch Dashboards:

OpenSearch Dashboards is the internet-primarily based user interface for OpenSearch, an open-source search and analytics engine. It presents a visual interface for coping with and exploring records saved in OpenSearch indices. With OpenSearch Dashboards, customers can create and personalize interactive dashboards, visualize information thru numerous charts, graphs, and maps, and perform searches and aggregations to advantage of insights from their statistics. It gives a range of capabilities for information visualization, records exploration, and dashboard introduction.

Kibana Dashboards:

Kibana is the records visualization and exploration tool associated with Elasticsearch, every other famous search and analytics engine. Kibana Dashboards allow customers to create and proportion interactive visualizations and dashboards primarily based on records saved in Elasticsearch indices. With Kibana Dashboards, users can build visual representations in their statistics, practice filters, and queries to discover particular subsets of records and create real-time tracking dashboards to track key metrics and traits. It offers a wide variety of visualization alternatives, consisting of charts, graphs, maps, and log evaluation gear.

Comparison table between OpenSearch dashboards and Kibana Dashboards

When to use which service

OpenSearch

OpenSearch is suitable for organizations that require a distributed, scalable, and highly available search and analytics engine, and need advanced search and analytics features.

• If you are focused on using a fully open-source solution, OpenSearch with OpenSearch Dashboards may be the better choice as they are both open-source.

• If you are building your application on AWS and want a solution that is specifically designed for AWS, OpenSearch with OpenSearch Dashboards may be the better choice as they are both built and optimized for AWS.

Pricing:

In terms of pricing, OpenSearch is an open-source tool, but when we use it for AWS services, the charges are determined by the AWS services that we use.

For example:

Let’s say you are new to Amazon OpenSearch Service and are creating a domain in the US-East (N. Virginia) region. You are testing the service with three t3.small.search instances and 15 GB storage in each instance. You are using Amazon EBS General Purpose SSD (gp2) volumes as your preferred storage option. Amazon OpenSearch Service provides free usage of up to 750 hours per month of a t2.small.search or t3.small.search instance, with 10 GB of EBS storage. Considering a month’s usage, the three instances on your domain would run for 730 hours each, totaling 2,190 hours of usage. Minus 750 hours of free usage, you will be charged for 1,440 instance hours, which amounts to $51.84 for the month (see calculations in the table below). Similarly, for EBS, your three instances put together will have 45 GB of EBS storage. Minus 10 GB of free storage, you will be charged 35 GB, which amounts to $4.725 for the month. Your total cost for the month is $56.67.

Pricing Table

ELK Stack

ELK Stack is suitable for organizations that require a full-stack solution for logging, monitoring, and analytics. It provides a comprehensive set of tools for data ingestion, storage, search, analysis, visualization, and alerting.

• If you already use Elasticsearch and Kibana and have a working system, it may be easier to stick with the ELK stack rather than switch to OpenSearch with OpenSearch Dashboards.

• If you require a more mature ecosystem of plugins and integrations, the ELK stack may be the better choice as it has been around for longer and has a more mature ecosystem.

• Elastic (the company behind the ELK stack) offers a range of support plans if you require enterprise-level support, while OpenSearch currently relies on community-based support.

Pricing:

For pricing of the ELK stack, you can visit this link: https://aws.amazon.com/marketplace/pp/prodview-x7ugwci6rn4be#pdp-pricing

Official Pricing of ElasticSearch: https://www.elastic.co/pricing/

Conclusion

Choosing between Elasticsearch and OpenSearch should be based on a careful evaluation of your specific needs, available resources, and alternatives. Both search and analytics engines offer a wide range of functions and functionalities.

Elasticsearch, with its greater maturity and comprehensive features, is a solid choice for organizations seeking a well-established solution. It provides a comprehensive ecosystem and offers a mature set of functionalities for search, analytics, and visualization. However, certain features may not be available for free under the Basic license, so it’s important to consider the licensing aspects.

On the other hand, OpenSearch focuses on providing open-source alternatives for previously proprietary functions found in Elasticsearch. It offers advanced security analytics, OpenSearch Dashboards for data visualization and exploration, machine learning capabilities, and more. If you prioritize open-source solutions or require specific functionalities provided by OpenSearch, it may be the better option for your use case.

It’s important to remember that both Elasticsearch and OpenSearch are evolving initiatives, and as they continue to diverge, there may be changes in functionality and compatibility. Therefore, it is advisable to plan migrations sooner rather than later to avoid any potential issues or missing features that could arise in the future.

Ultimately, the decision between Elasticsearch and OpenSearch should be based on careful consideration of your unique use case, desired functionalities, and the evolving nature of the projects. It’s recommended to stay informed about the latest developments and consult with experts to ensure you make the most suitable choice for your search consulting project.

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