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  4. Search Engines
  5. Searchkick vs Sphinx

Searchkick vs Sphinx

OverviewComparisonAlternatives

Overview

Sphinx
Sphinx
Stacks1.1K
Followers300
Votes32
Searchkick
Searchkick
Stacks18
Followers34
Votes1
GitHub Stars6.7K
Forks766

Searchkick vs Sphinx: What are the differences?

Searchkick: Intelligent search made easy. Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users; Sphinx: Open source full text search server, designed from the ground up with performance, relevance (aka search quality), and integration simplicity in mind. Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server. A variety of text processing features enable fine-tuning Sphinx for your particular application requirements, and a number of relevance functions ensures you can tweak search quality as well.

Searchkick and Sphinx can be primarily classified as "Search Engines" tools.

Some of the features offered by Searchkick are:

  • stemming - tomatoes matches tomato
  • special characters - jalapeno matches jalapeño
  • extra whitespace - dishwasher matches dish washer

On the other hand, Sphinx provides the following key features:

  • Output formats: HTML (including Windows HTML Help), LaTeX (for printable PDF versions), ePub, Texinfo, manual pages, plain text
  • Extensive cross-references: semantic markup and automatic links for functions, classes, citations, glossary terms and similar pieces of information
  • Hierarchical structure: easy definition of a document tree, with automatic links to siblings, parents and children

Searchkick is an open source tool with 4.96K GitHub stars and 582 GitHub forks. Here's a link to Searchkick's open source repository on GitHub.

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CLI (Node.js)
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Detailed Comparison

Sphinx
Sphinx
Searchkick
Searchkick

It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users.

Output formats: HTML (including Windows HTML Help), LaTeX (for printable PDF versions), ePub, Texinfo, manual pages, plain text;Extensive cross-references: semantic markup and automatic links for functions, classes, citations, glossary terms and similar pieces of information;Hierarchical structure: easy definition of a document tree, with automatic links to siblings, parents and children;Automatic indices: general index as well as a language-specific module indices;Code handling: automatic highlighting using the Pygments highlighter;Extensions: automatic testing of code snippets, inclusion of docstrings from Python modules (API docs), and more
stemming - tomatoes matches tomato;special characters - jalapeno matches jalapeño;extra whitespace - dishwasher matches dish washer;misspellings - zuchini matches zucchini;custom synonyms - qtip matches cotton swab;query like SQL - no need to learn a new query language;reindex without downtime;easily personalize results for each user;autocomplete;“Did you mean” suggestions;works with ActiveRecord and Mongoid
Statistics
GitHub Stars
-
GitHub Stars
6.7K
GitHub Forks
-
GitHub Forks
766
Stacks
1.1K
Stacks
18
Followers
300
Followers
34
Votes
32
Votes
1
Pros & Cons
Pros
  • 16
    Fast
  • 9
    Simple deployment
  • 6
    Open source
  • 1
    Lots of extentions
Pros
  • 1
    Open Source
Integrations
DevDocs
DevDocs
Zapier
Zapier
Google Drive
Google Drive
Google Chrome
Google Chrome
Dropbox
Dropbox
No integrations available

What are some alternatives to Sphinx, Searchkick?

MkDocs

MkDocs

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

Lucene

Lucene

Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.

Google

Google

Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

YugabyteDB

YugabyteDB

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

Apache Solr

Apache Solr

It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.

Qdrant

Qdrant

It is an open-source Vector Search Engine and Vector Database written in Rust. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

Weaviate

Weaviate

It is an open-source vector search engine. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.

AddSearch

AddSearch

We help your website visitors find what they are looking for. AddSearch is a lightning fast, accurate and customizable site search engine with a Search API. AddSearch works on all devices and is easy to install, customize and tweak.

ArangoSearch

ArangoSearch

It is a C++ based full-text search engine including similarity ranking capabilities natively integrated into ArangoDB. It allows users to combine two information retrieval techniques: boolean and generalized ranking retrieval. Search results “approved” by the boolean model can be ranked by relevance to the respective query using the Vector Space Model in conjunction with BM25 or TFIDF weighting schemes.

Carrot2

Carrot2

It organizes your search results into topics. With an instant overview of what's available, you will quickly find what you're looking for.

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