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  5. Manticore Search vs Typesense

Manticore Search vs Typesense

OverviewComparisonAlternatives

Overview

Manticore Search
Manticore Search
Stacks10
Followers21
Votes22
GitHub Stars11.4K
Forks619
Typesense
Typesense
Stacks70
Followers119
Votes39
GitHub Stars24.6K
Forks826

Manticore Search vs Typesense: What are the differences?

  1. Indexing Performance: Manticore Search is known for its high indexing performance, especially when dealing with large-scale datasets. On the other hand, Typesense caters well to smaller datasets and may not be as efficient for indexing large amounts of data quickly.

  2. Search Speed: Manticore Search offers faster search speeds, making it a suitable choice for applications requiring real-time search functionality. Typesense also provides fast search capabilities but may lag behind Manticore Search in terms of speed, especially for complex queries.

  3. Data Schema Flexibility: Manticore Search allows for more flexibility in defining data schemas compared to Typesense, making it more adaptable to diverse data structures and document types.

  4. Query Language: Manticore Search supports the SphinxQL query language, which offers a wide range of advanced search capabilities. Typesense, on the other hand, utilizes a simplified query language that may not be as feature-rich as SphinxQL.

  5. Community Support: Manticore Search has a larger and more established community presence, providing extensive resources, plugins, and support for users. Typesense, being relatively newer, may have a smaller community, resulting in fewer resources and community-based solutions.

  6. Scalability: Manticore Search is highly scalable, capable of handling large amounts of data and multiple concurrent users effectively. Typesense, while scalable to an extent, may face limitations when dealing with extremely high volumes of data or user traffic.

In Summary, Manticore Search excels in indexing performance, search speed, data schema flexibility, query language support, community size, and scalability compared to Typesense.

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Detailed Comparison

Manticore Search
Manticore Search
Typesense
Typesense

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.

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.

Full-text searching; RealTIme indexing; Facets; Percolate Queries; Distributed indexes; JSON attributes
Handles typographical errors elegantly; Simple to set-up and manage; Easy to tailor your search results to perfection; Meticulously designed and optimized for speed
Statistics
GitHub Stars
11.4K
GitHub Stars
24.6K
GitHub Forks
619
GitHub Forks
826
Stacks
10
Stacks
70
Followers
21
Followers
119
Votes
22
Votes
39
Pros & Cons
Pros
  • 2
    MySQL/PostgreSQL/ODBC/xml/csv sync out of the box
  • 2
    Real-time inserts
  • 2
    Free
  • 2
    Distributed
  • 2
    Easy to get started
Pros
  • 5
    Free
  • 4
    Easy to deploy
  • 4
    Facet search
  • 3
    Typo handling
  • 3
    Open source
Integrations
No integrations available
Mac OS X
Mac OS X
Ruby
Ruby
Linux
Linux
Python
Python
JavaScript
JavaScript

What are some alternatives to Manticore Search, Typesense?

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.

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.

Amazon Elasticsearch Service

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.

Azure Search

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.

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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