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Lucene vs Solr: What are the differences?

Key Differences between Lucene and Solr

Lucene and Solr are both open-source search libraries that are used for information retrieval and text search. While they have some similarities, there are also key differences between the two:

  1. Indexing and Querying: Lucene is primarily a Java library that provides indexing and querying functionalities to build search applications. It focuses on providing a low-level API for full-text indexing and searching. In contrast, Solr is built on top of Lucene and provides a ready-to-use search platform with additional features such as distributed searching, caching, faceted search, and more.

  2. Architecture: Lucene is a library that can be directly integrated into a Java application to add indexing and searching capabilities. It works at a lower level, allowing developers to have fine-grained control over the search process. On the other hand, Solr provides a standalone server with a REST-like API that can be used to interact with the search engine. It simplifies the process of building search applications and provides a ready-to-use platform.

  3. Scalability and Reliability: Solr is designed to handle large-scale deployments and distributed searching out of the box. It can distribute indexing and querying across multiple machines for improved performance and fault tolerance. Lucene, being a library, can also be used in a distributed environment, but it requires more manual configuration and setup.

  4. Features and Functionality: Solr extends the capabilities of Lucene by providing additional features such as faceted search, highlighting, spell checking, document processing, support for different data sources, and more. These features are built on top of Lucene's core functionality and provide a high-level abstraction for developers.

  5. Ease of Use: Solr provides a ready-to-use search platform with an out-of-the-box configuration, making it easier for developers to get started. It has rich documentation and a user-friendly interface for configuration and monitoring. Lucene, being a library, requires more coding and configuration to build a search application from scratch.

  6. Community and Support: Both Lucene and Solr have active open-source communities, but Solr has a larger user base and a more robust support ecosystem. It has a wider range of plugins, extensions, and third-party integrations available, making it easier to find solutions to common problems.

In summary, Lucene is a low-level Java library for full-text indexing and searching, while Solr is a ready-to-use search platform built on top of Lucene, providing additional features and higher-level abstractions. Solr simplifies the development process and offers scalability, reliability, and a more extensive support ecosystem.

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Pros of Lucene
Pros of Solr
  • 1
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  • 35
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    Indexing and searching
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  • 13
    Enterprise Ready
  • 5
  • 5
    Apache Software Foundation
  • 4
    Great Search engine
  • 2
    Security built-in
  • 1
    Easy Operating

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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 Solr?

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.

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What companies use Lucene?
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What are some alternatives to Lucene and Solr?
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).
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.
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