Apache Solr vs Sphinx

Overview

Sphinx

Stacks1.1K

Followers300

Votes32

Apache Solr

Stacks224

Followers91

Votes0

Apache Solr vs Sphinx: What are the differences?

Apache Solr: An open source search platform. 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; 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.

Apache Solr and Sphinx can be categorized as "Search Engines" tools.

Webedia, Grooveshark, and Ansible are some of the popular companies that use Sphinx, whereas Apache Solr is used by GameDuell, Capgemini, and Participant. Sphinx has a broader approval, being mentioned in 42 company stacks & 73 developers stacks; compared to Apache Solr, which is listed in 11 company stacks and 13 developer stacks.

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Detailed Comparison

Sphinx	Apache Solr
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.	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.
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	Advanced full-text search capabilities; Optimized for high volume traffic; Standards based open interfaces - XML, JSON and HTTP; Comprehensive administration interfaces; Easy monitoring; Highly scalable and fault tolerant; Flexible and adaptable with easy configuration
Statistics
Stacks 1.1K	Stacks 224
Followers 300	Followers 91
Votes 32	Votes 0
Pros & Cons
Pros 16 Fast 9 Simple deployment 6 Open source 1 Lots of extentions	No community feedback yet
Integrations
DevDocs Zapier Google Drive Google Chrome Dropbox	No integrations available

What are some alternatives to Sphinx, Apache Solr?

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

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

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.

Searchkick

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.

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

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

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.

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.

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.

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