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  5. Lucene vs Solr

Lucene vs Solr

OverviewComparisonAlternatives

Overview

Solr
Solr
Stacks805
Followers644
Votes126
Lucene
Lucene
Stacks175
Followers230
Votes2

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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Detailed Comparison

Solr
Solr
Lucene
Lucene

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.

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

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
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)
Statistics
Stacks
805
Stacks
175
Followers
644
Followers
230
Votes
126
Votes
2
Pros & Cons
Pros
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
Pros
  • 1
    Small
  • 1
    Fast
Integrations
No integrations available
Java
Java

What are some alternatives to Solr, Lucene?

Algolia

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.

Sphinx

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.

MkDocs

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

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

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

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.

Jina

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

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

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

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.

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