Elasticsearch vs Flood IO

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Flood IO

Stacks20

Followers70

Votes5

Elasticsearch vs Flood IO: What are the differences?

Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). On the other hand, Flood IO is detailed as "Load Testing for everyone". Flood IO is a self-service load testing platform, designed to allow anyone to easily test the performance of their website from a few hundred users, right to up to 1 million or more.

Elasticsearch can be classified as a tool in the "Search as a Service" category, while Flood IO is grouped under "Load and Performance Testing".

Some of the features offered by Elasticsearch are:

Distributed and Highly Available Search Engine.
Multi Tenant with Multi Types.
Various set of APIs including RESTful

On the other hand, Flood IO provides the following key features:

Beautiful reports, detailed analysis
Monitor your tests in real time
Supports JMeter and Gatling

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

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Advice on Elasticsearch, Flood IO

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k

Comments

Vrashab

QA at Altair

Jun 23, 2020

Needs adviceon

Gatling

Locust

Flood IO

I have to run a multi-user load test and have test scripts developed in Gatling and Locust.

I am planning to run the tests with Flood IO, as it allows us to create a custom grid. They support Gatling. Did anyone try Locust tests? I would prefer not to use multiple infra providers for running these tests!

142k views142k

Comments

Detailed Comparison

Elasticsearch	Flood IO
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).	Performance testing with Flood increases customer satisfaction and confidence in your production apps and reduces business risk.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License	Browser-based load testing; JMeter load testing; Gatling load testing; Detailed reporting; on-premise; multi-cloud; AWS load testing
Statistics
Stacks 35.5K	Stacks 20
Followers 27.1K	Followers 70
Votes 1.6K	Votes 5
Pros & Cons
Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale	Pros 2 Affordable 2 Great feature set 1 Easy to use
Integrations
Kibana Beats Logstash	Travis CI GitHub New Relic Jenkins Slack CircleCI Amazon API Gateway AWS IAM Flowdock

What are some alternatives to Elasticsearch, Flood IO?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

k6

It is a developer centric open source load testing tool for testing the performance of your backend infrastructure. It’s built with Go and JavaScript to integrate well into your development workflow.

Locust

Locust is an easy-to-use, distributed, user load testing tool. Intended for load testing web sites (or other systems) and figuring out how many concurrent users a system can handle.

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Gatling

Gatling is a highly capable load testing tool. It is designed for ease of use, maintainability and high performance. Out of the box, Gatling comes with excellent support of the HTTP protocol that makes it a tool of choice for load testing any HTTP server. As the core engine is actually protocol agnostic, it is perfectly possible to implement support for other protocols. For example, Gatling currently also ships JMS support.

Loader.io

Loader.io is a free load testing service that allows you to stress test your web-apps/apis with thousands of concurrent connections.

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Related Comparisons

Some of the features offered by Elasticsearch are:

Distributed and Highly Available Search Engine.
Multi Tenant with Multi Types.
Various set of APIs including RESTful

On the other hand, Flood IO provides the following key features:

Beautiful reports, detailed analysis
Monitor your tests in real time
Supports JMeter and Gatling

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

Elasticsearch vs Flood IO