Solr vs Sphinx

Overview

Solr

Stacks805

Followers644

Votes126

Sphinx

Stacks1.1K

Followers300

Votes32

Solr vs Sphinx: What are the differences?

Introduction

Solr and Sphinx are both search platforms that offer powerful indexing and retrieval capabilities. Although they share similarities, there are key differences between the two.

Architecture: Solr is built on Apache Lucene, which is a full-text search library, whereas Sphinx is a standalone search engine. Solr is a Java-based application that requires Java Virtual Machine (JVM) to run, while Sphinx is written in C++ and does not require any external dependencies.
Scalability: Solr is known for its scalability and can handle large amounts of data with ease. It supports distributed searching and indexing, making it suitable for handling big data applications. On the other hand, Sphinx is also scalable but is better suited for smaller deployments and does not provide the same level of distributed capabilities as Solr.
Query Language: Solr uses a custom query language called Solr Query Syntax (SOS), which is similar to SQL and allows for complex queries and filtering. Sphinx, on the other hand, uses SphinxQL, which is also SQL-like but has certain limitations compared to SOS. Solr's query language provides more flexibility and advanced features for querying and filtering search results.
Integration with Databases: Solr has extensive integration capabilities with various databases, such as MySQL, Oracle, and PostgreSQL. This allows Solr to seamlessly index and search data from multiple sources. Sphinx also supports integration with databases but is more commonly used with its own storage engine.
Faceted Search: Solr provides robust support for faceted search, which allows users to filter and narrow down search results based on different attributes or facets. Sphinx, on the other hand, has limited support for faceted search and requires additional customization to achieve similar functionality.
Community and Ecosystem: Solr has a larger and more active community compared to Sphinx, which results in a wider range of available resources, plugins, and extensions. Solr also has better documentation and is more widely adopted in the industry, making it easier to find skilled developers and support.

In summary, Solr and Sphinx differ in their architecture, scalability, query language, integration capabilities, support for faceted search, and community ecosystem.

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

Solr	Sphinx
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.	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.
Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture	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 805	Stacks 1.1K
Followers 644	Followers 300
Votes 126	Votes 32
Pros & Cons
Pros 35 Powerful 22 Indexing and searching 20 Scalable 19 Customizable 13 Enterprise Ready	Pros 16 Fast 9 Simple deployment 6 Open source 1 Lots of extentions
Integrations
Lucene	DevDocs Zapier Google Drive Google Chrome Dropbox

What are some alternatives to Solr, Sphinx?

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.

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.

Dejavu

dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).

Elassandra

Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.

Tantivy

It is a full-text search engine library inspired by Apache Lucene and written in Rust. It is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.

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.

Jina

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

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.

Mirage

The Elasticsearch query DSL supports 100+ query APIs ranging from full-text search, numeric range filters, geolocation queries to nested and span queries. Mirage is a modern, open-source web based query explorer for Elasticsearch.

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

Solr vs Sphinx: What are the differences?

Introduction

Solr and Sphinx are both search platforms that offer powerful indexing and retrieval capabilities. Although they share similarities, there are key differences between the two.

Architecture: Solr is built on Apache Lucene, which is a full-text search library, whereas Sphinx is a standalone search engine. Solr is a Java-based application that requires Java Virtual Machine (JVM) to run, while Sphinx is written in C++ and does not require any external dependencies.
Scalability: Solr is known for its scalability and can handle large amounts of data with ease. It supports distributed searching and indexing, making it suitable for handling big data applications. On the other hand, Sphinx is also scalable but is better suited for smaller deployments and does not provide the same level of distributed capabilities as Solr.
Query Language: Solr uses a custom query language called Solr Query Syntax (SOS), which is similar to SQL and allows for complex queries and filtering. Sphinx, on the other hand, uses SphinxQL, which is also SQL-like but has certain limitations compared to SOS. Solr's query language provides more flexibility and advanced features for querying and filtering search results.
Integration with Databases: Solr has extensive integration capabilities with various databases, such as MySQL, Oracle, and PostgreSQL. This allows Solr to seamlessly index and search data from multiple sources. Sphinx also supports integration with databases but is more commonly used with its own storage engine.
Faceted Search: Solr provides robust support for faceted search, which allows users to filter and narrow down search results based on different attributes or facets. Sphinx, on the other hand, has limited support for faceted search and requires additional customization to achieve similar functionality.
Community and Ecosystem: Solr has a larger and more active community compared to Sphinx, which results in a wider range of available resources, plugins, and extensions. Solr also has better documentation and is more widely adopted in the industry, making it easier to find skilled developers and support.

In summary, Solr and Sphinx differ in their architecture, scalability, query language, integration capabilities, support for faceted search, and community ecosystem.