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Lucene vs Sphinx: What are the differences?


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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Pros of Lucene
Pros of Sphinx
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    Simple deployment
  • 6
    Open source
  • 1
    Lots of extentions

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What is 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.

What is Sphinx?

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.

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What are some alternatives to Lucene and 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.
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).
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.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
See all alternatives