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  5. Amazon Elasticsearch Service vs Azure Search

Amazon Elasticsearch Service vs Azure Search

OverviewDecisionsComparisonAlternatives

Overview

Azure Search
Azure Search
Stacks84
Followers224
Votes16
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Stacks371
Followers288
Votes24

Amazon Elasticsearch Service vs Azure Search: What are the differences?

# Introduction #
  1. Deployment: Amazon Elasticsearch Service is a fully managed service, which means Amazon takes care of cluster management, scaling, and maintenance, while Azure Search requires more manual configuration and management from the user's end.
  2. Pricing: Amazon Elasticsearch Service charges for the resources you use, while Azure Search has a flat pricing structure based on the service tier chosen, causing potential cost differences depending on usage patterns.
  3. Integration: Amazon Elasticsearch Service seamlessly integrates with other AWS services, providing a broader ecosystem for users, while Azure Search integrates well with other Microsoft Azure services, offering a similar ecosystem but within the Azure cloud environment.
  4. Indexing: Amazon Elasticsearch Service supports automatic indexing of JSON documents, eliminating the need for manual indexing, whereas Azure Search requires explicit indexing of documents, which can be a manual task for developers.
  5. Querying: Amazon Elasticsearch Service allows the use of Elasticsearch Query DSL (Domain Specific Language) for more complex queries, giving users more powerful search capabilities than Azure Search, which has its own query syntax that may be more limited in functionality.
  6. Scalability: Amazon Elasticsearch Service offers seamless scalability with the ability to add or remove nodes dynamically, while Azure Search may require manual scaling operations that could introduce downtime during high traffic periods.

In Summary, Amazon Elasticsearch Service and Azure Search differ in deployment, pricing, integration, indexing, querying, and scalability capabilities, which should be considered when choosing a search service for your project.

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Advice on Azure Search, Amazon Elasticsearch Service

Phillip
Phillip

Developer at Coach Align

Mar 18, 2021

Decided

The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.

40.7k views40.7k
Comments
André
André

Nov 20, 2020

Needs adviceonElasticsearchElasticsearchAmazon DynamoDBAmazon DynamoDB

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are:

  • Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON
  • Allow a strict match mode
  • Perform the search through all the JSON values (it can reach 6 nesting levels)
  • Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

60.3k views60.3k
Comments
Ted
Ted

Computer Science

Dec 19, 2020

Review

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

42.9k views42.9k
Comments

Detailed Comparison

Azure Search
Azure Search
Amazon Elasticsearch Service
Amazon Elasticsearch Service

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.

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.

Powerful, reliable performance;Easily tune search indices to meet business goals;Scale out simply;Enable sophisticated search functionality;Get up and running quickly;Simplify search index management
-
Statistics
Stacks
84
Stacks
371
Followers
224
Followers
288
Votes
16
Votes
24
Pros & Cons
Pros
  • 4
    Easy to set up
  • 3
    Managed
  • 3
    Auto-Scaling
  • 2
    Lucene based search criteria
  • 2
    More languages
Pros
  • 10
    Easy setup, monitoring and scaling
  • 7
    Document-oriented
  • 7
    Kibana
Integrations
Microsoft Azure
Microsoft Azure
Elasticsearch
Elasticsearch

What are some alternatives to Azure Search, Amazon Elasticsearch Service?

Elasticsearch

Elasticsearch

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).

Algolia

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.

Typesense

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

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.

Manticore Search

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.

Swiftype

Swiftype

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

MeiliSearch

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

Bonsai

Bonsai

Your customers expect fast, near-magical results from your search. Help them find what they’re looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

Azure Cognitive Search

Azure Cognitive Search

It is the only cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. Formerly known as Azure Search, it uses the same integrated Microsoft natural language stack that Bing and Office have used for more than a decade and AI services across vision, language and speech. Spend more time innovating and less time maintaining a complex cloud search solution.

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