Amazon DocumentDB vs Elasticsearch

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Amazon DocumentDB

Stacks72

Followers64

Votes0

Amazon DocumentDB vs Elasticsearch: What are the differences?

Data Model: Amazon DocumentDB is a fully managed document database service that supports MongoDB workloads, with JSON-like documents. Elasticsearch, on the other hand, is a search engine that indexes and searches unstructured content. While DocumentDB is specifically designed for document-oriented data, Elasticsearch is more oriented towards full-text search and analytics tasks.
Query Language: DocumentDB uses the MongoDB query language, which is intuitive for developers familiar with MongoDB. Elasticsearch uses a query language based on JSON, which allows for complex queries including fuzzy searches, wildcard searches, and more. The query language in Elasticsearch offers more advanced search capabilities compared to DocumentDB.
Scalability: Amazon DocumentDB is designed for horizontal scalability where users can add read replicas to distribute read traffic. Elasticsearch is designed for horizontal scalability for large-scale data, with support for sharding and replication to distribute data across nodes in a cluster. Elasticsearch is more suited for scenarios where massive data processing is required.
Analytics Capabilities: Elasticsearch is built for real-time analytics, log analysis, and full-text search use cases, providing powerful aggregations, filtering, and sorting capabilities. Amazon DocumentDB lacks some advanced analytical features provided by Elasticsearch, making it less suitable for advanced analytics workloads.
Indexing and Searching: Elasticsearch includes built-in indexing and searching features that are optimized for speed and efficiency. Amazon DocumentDB relies on indexes to improve query performance, but may not provide the same level of advanced search capabilities as Elasticsearch. Elasticsearch excels in indexing and searching documents across large datasets efficiently.
Community Support: Elasticsearch has a large and active open-source community, offering a wide range of plugins, integrations, and support resources. Amazon DocumentDB, being a managed service, may have limited community support compared to Elasticsearch, which can impact the availability of resources and expertise for troubleshooting.

In Summary, Amazon DocumentDB is tailored for document-oriented workloads with MongoDB compatibility, while Elasticsearch is focused on real-time analytics, search, and scalability with advanced indexing and searching capabilities.

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Advice on Elasticsearch, Amazon DocumentDB

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	Amazon DocumentDB
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).	Amazon DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.
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	MongoDB-compatible;Fully managed;Performance at scale
Statistics
Stacks 35.5K	Stacks 72
Followers 27.1K	Followers 64
Votes 1.6K	Votes 0
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 0 Scalable 0 Storage elasticity 0 Easy Setup
Integrations
Kibana Beats Logstash	No integrations available

What are some alternatives to Elasticsearch, Amazon DocumentDB?

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

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.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

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.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

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.

Related Comparisons

Amazon DocumentDB vs Elasticsearch: What are the differences?

Data Model: Amazon DocumentDB is a fully managed document database service that supports MongoDB workloads, with JSON-like documents. Elasticsearch, on the other hand, is a search engine that indexes and searches unstructured content. While DocumentDB is specifically designed for document-oriented data, Elasticsearch is more oriented towards full-text search and analytics tasks.
Query Language: DocumentDB uses the MongoDB query language, which is intuitive for developers familiar with MongoDB. Elasticsearch uses a query language based on JSON, which allows for complex queries including fuzzy searches, wildcard searches, and more. The query language in Elasticsearch offers more advanced search capabilities compared to DocumentDB.
Scalability: Amazon DocumentDB is designed for horizontal scalability where users can add read replicas to distribute read traffic. Elasticsearch is designed for horizontal scalability for large-scale data, with support for sharding and replication to distribute data across nodes in a cluster. Elasticsearch is more suited for scenarios where massive data processing is required.
Analytics Capabilities: Elasticsearch is built for real-time analytics, log analysis, and full-text search use cases, providing powerful aggregations, filtering, and sorting capabilities. Amazon DocumentDB lacks some advanced analytical features provided by Elasticsearch, making it less suitable for advanced analytics workloads.
Indexing and Searching: Elasticsearch includes built-in indexing and searching features that are optimized for speed and efficiency. Amazon DocumentDB relies on indexes to improve query performance, but may not provide the same level of advanced search capabilities as Elasticsearch. Elasticsearch excels in indexing and searching documents across large datasets efficiently.
Community Support: Elasticsearch has a large and active open-source community, offering a wide range of plugins, integrations, and support resources. Amazon DocumentDB, being a managed service, may have limited community support compared to Elasticsearch, which can impact the availability of resources and expertise for troubleshooting.

Amazon DocumentDB vs Elasticsearch

Overview