Lucene vs Sphinx

Overview

Lucene

Stacks175

Followers230

Votes2

Sphinx

Stacks1.1K

Followers300

Votes32

Lucene vs Sphinx: What are the differences?

Introduction

Lucene and Sphinx are both popular open-source search engines used for information retrieval purposes. While both share some similarities, there are key differences between the two.

Indexing Approach: Lucene uses an inverted index approach to store data, which allows for efficient and fast full-text searching. Sphinx, on the other hand, focuses on real-time indexing and retrieval, making it more suitable for quickly updating data sources.
Scalability and Distributed Searching: Lucene is primarily designed for single-node deployments, and scaling it to support a distributed search infrastructure requires additional development effort. Sphinx, on the other hand, offers built-in support for distributed searching, making it easier to scale across multiple nodes.
Query Languages: Lucene uses a query language based on Boolean operators, where queries can be constructed using logical combinations. Sphinx, however, supports an extended SQL-like query language, making it more familiar and easier to use for developers familiar with SQL syntax.
Supported Document Formats: Lucene is capable of indexing and searching various document formats like text, HTML, PDF, etc., thanks to its analyzers and parsers. Sphinx, while it supports a wide range of document formats, primarily focuses on indexing and searching text-based documents.
Integrations and Language Support: Lucene has extensive integrations with programming languages like Java, Python, and Ruby, making it accessible for developers using these languages. Sphinx, while it also supports multiple programming languages, has stronger integration with PHP, as it was originally developed for PHP-based projects.
Community and Documentation: Lucene has a larger and more active community, resulting in a wider array of resources, forums, and documentation available. Sphinx, while having a smaller community, still has sufficient resources and documentation available for developers to utilize.

In Summary, Lucene and Sphinx differ in their indexing approach, scalability, query languages, supported document formats, integrations, and community size.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Lucene	Sphinx
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.	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.
over 150GB/hour on modern hardware;small RAM requirements -- only 1MB heap;incremental indexing as fast as batch indexing;index size roughly 20-30% the size of text indexed;ranked searching -- best results returned first;many powerful query types: phrase queries, wildcard queries, proximity queries, range queries;fielded searching (e.g. title, author, contents);sorting by any field;multiple-index searching with merged results;allows simultaneous update and searching;flexible faceting, highlighting, joins and result grouping;fast, memory-efficient and typo-tolerant suggesters;pluggable ranking models, including the Vector Space Model and Okapi BM25;configurable storage engine (codecs)	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
Statistics
Stacks 175	Stacks 1.1K
Followers 230	Followers 300
Votes 2	Votes 32
Pros & Cons
Pros 1 Fast 1 Small	Pros 16 Fast 9 Simple deployment 6 Open source 1 Lots of extentions
Integrations
Solr Java	DevDocs Zapier Google Drive Google Chrome Dropbox

What are some alternatives to Lucene, Sphinx?

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.

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.

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

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.

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

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

Related Comparisons

Postman vs Swagger UI

Google Maps vs Mapbox

Leaflet vs Mapbox vs OpenLayers

Mailgun vs Mandrill vs SendGrid

Paw vs Postman vs Runscope

Lucene vs Sphinx: What are the differences?

Introduction

Lucene and Sphinx are both popular open-source search engines used for information retrieval purposes. While both share some similarities, there are key differences between the two.

Indexing Approach: Lucene uses an inverted index approach to store data, which allows for efficient and fast full-text searching. Sphinx, on the other hand, focuses on real-time indexing and retrieval, making it more suitable for quickly updating data sources.
Scalability and Distributed Searching: Lucene is primarily designed for single-node deployments, and scaling it to support a distributed search infrastructure requires additional development effort. Sphinx, on the other hand, offers built-in support for distributed searching, making it easier to scale across multiple nodes.
Query Languages: Lucene uses a query language based on Boolean operators, where queries can be constructed using logical combinations. Sphinx, however, supports an extended SQL-like query language, making it more familiar and easier to use for developers familiar with SQL syntax.
Supported Document Formats: Lucene is capable of indexing and searching various document formats like text, HTML, PDF, etc., thanks to its analyzers and parsers. Sphinx, while it supports a wide range of document formats, primarily focuses on indexing and searching text-based documents.
Integrations and Language Support: Lucene has extensive integrations with programming languages like Java, Python, and Ruby, making it accessible for developers using these languages. Sphinx, while it also supports multiple programming languages, has stronger integration with PHP, as it was originally developed for PHP-based projects.
Community and Documentation: Lucene has a larger and more active community, resulting in a wider array of resources, forums, and documentation available. Sphinx, while having a smaller community, still has sufficient resources and documentation available for developers to utilize.

In Summary, Lucene and Sphinx differ in their indexing approach, scalability, query languages, supported document formats, integrations, and community size.