Elasticsearch vs MongoDB Atlas

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

MongoDB Atlas

Stacks856

Followers940

Votes34

Elasticsearch vs MongoDB Atlas: What are the differences?

Deployment Method: Elasticsearch is typically self-hosted on-premises or on cloud infrastructure like AWS, while MongoDB Atlas is a fully managed database service provided by MongoDB, running on the cloud with automatic scaling and backups.
Data Model: Elasticsearch is optimized for full-text search and complex queries, while MongoDB Atlas is a document-oriented database that stores data in a JSON-like format, making it easy to work with dynamic schemas.
Querying Capabilities: Elasticsearch utilizes its own query DSL (Domain Specific Language) for powerful and flexible search queries, including fuzzy searches and aggregations, while MongoDB Atlas supports query operations through its MongoDB Query Language.
Scalability: Elasticsearch is designed for horizontal scalability, allowing easy distribution of data across nodes for improved performance, while MongoDB Atlas offers automatic scaling for data storage as well as read and write operations.
Indexing Strategy: Elasticsearch indexes all fields by default, making it suitable for search applications like logging and monitoring, whereas MongoDB Atlas requires manual indexing to optimize query performance for specific fields in a collection.
Performance Optimization: Elasticsearch provides powerful indexing and search capabilities for large datasets, making it ideal for applications where search speed is crucial, while MongoDB Atlas excels in general-purpose applications with predictable workloads due to its schema flexibility and ease of data manipulation.

In Summary, Elasticsearch and MongoDB Atlas differ significantly in deployment methods, data models, querying capabilities, scalability options, indexing strategies, and performance optimizations.

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Advice on Elasticsearch, MongoDB Atlas

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!

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Detailed Comparison

Elasticsearch	MongoDB Atlas
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).	MongoDB Atlas is a global cloud database service built and run by the team behind MongoDB. Enjoy the flexibility and scalability of a document database, with the ease and automation of a fully managed service on your preferred cloud.
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	Global clusters for world-class applications. Support for 60+ cloud regions across AWS, Azure, & GCP.; Secure for sensitive data. Built-in security controls and features to meet your existing protocols and compliance standards.; Designed for developer productivity. Integrated tools to manipulate, visualize, and analyze your data. Execute code in real time in response to data changes.; Reliable for mission-critical workload. Highly available with distributed fault tolerance and backup options to meet your data recovery objectives.; Built for optimal performance. On-demand scaling, resource optimization tools, and real-time visibility into database performance.
Statistics
Stacks 35.5K	Stacks 856
Followers 27.1K	Followers 940
Votes 1.6K	Votes 34
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 10 MongoDB SaaS for and by Mongo, makes it so easy 6 Amazon VPC peering 4 Granular role-based access controls 4 MongoDB atlas is GUItool through you can manage all DB 3 Built-in data browser
Integrations
Kibana Beats Logstash	MongoDB

What are some alternatives to Elasticsearch, MongoDB Atlas?

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.

MongoLab

mLab is the largest cloud MongoDB service in the world, hosting over a half million deployments on AWS, Azure, and Google.

Compose

Compose makes it easy to spin up multiple open source databases with just one click. Deploy MongoDB for production, take Redis out for a performance test drive, or spin up RethinkDB in development before rolling it out to production.

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.

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.

ObjectRocket

Fast, scalable, and reliably-managed Mongo DB, Redis, Elasticsearch, PostgreSQL, CockroachDB and TimescaleDB. An easy to use DBaaS (database as a service) platform on private or public cloud. Complete DB Management & Administration.

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

Related Comparisons

Elasticsearch vs MongoDB Atlas: What are the differences?

Deployment Method: Elasticsearch is typically self-hosted on-premises or on cloud infrastructure like AWS, while MongoDB Atlas is a fully managed database service provided by MongoDB, running on the cloud with automatic scaling and backups.
Data Model: Elasticsearch is optimized for full-text search and complex queries, while MongoDB Atlas is a document-oriented database that stores data in a JSON-like format, making it easy to work with dynamic schemas.
Querying Capabilities: Elasticsearch utilizes its own query DSL (Domain Specific Language) for powerful and flexible search queries, including fuzzy searches and aggregations, while MongoDB Atlas supports query operations through its MongoDB Query Language.
Scalability: Elasticsearch is designed for horizontal scalability, allowing easy distribution of data across nodes for improved performance, while MongoDB Atlas offers automatic scaling for data storage as well as read and write operations.
Indexing Strategy: Elasticsearch indexes all fields by default, making it suitable for search applications like logging and monitoring, whereas MongoDB Atlas requires manual indexing to optimize query performance for specific fields in a collection.
Performance Optimization: Elasticsearch provides powerful indexing and search capabilities for large datasets, making it ideal for applications where search speed is crucial, while MongoDB Atlas excels in general-purpose applications with predictable workloads due to its schema flexibility and ease of data manipulation.

In Summary, Elasticsearch and MongoDB Atlas differ significantly in deployment methods, data models, querying capabilities, scalability options, indexing strategies, and performance optimizations.

Elasticsearch vs MongoDB Atlas

Overview