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  1. Stackups
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  4. Search Engines
  5. Apache Solr vs Lucene

Apache Solr vs Lucene

OverviewComparisonAlternatives

Overview

Lucene
Lucene
Stacks175
Followers230
Votes2
Apache Solr
Apache Solr
Stacks224
Followers91
Votes0

Apache Solr vs Lucene: What are the differences?

# Introduction
This markdown provides a comparison between Apache Solr and Lucene, two popular search technologies used for information retrieval.

1. **Architecture**: Lucene is a Java-based search library that provides indexing and searching capabilities at a lower level, while Apache Solr is built on top of Lucene and provides a more user-friendly, feature-rich search platform with additional functionalities such as full-text search, faceted search, and result highlighting. 
2. **Scalability**: Apache Solr is designed for high scalability and can handle large volumes of data efficiently through distributed indexing and querying, while Lucene is best suited for smaller-scale applications that do not require the same level of scalability.
3. **Ease of Use**: Apache Solr offers a more user-friendly interface and configuration options compared to Lucene, making it easier for developers to set up and customize search functionalities without needing to delve deep into the intricacies of the underlying search engine library.
4. **Management and Monitoring**: Apache Solr provides built-in tools for managing and monitoring search indexes, as well as monitoring performance metrics, whereas Lucene requires developers to implement their monitoring and management tools or rely on third-party solutions.
5. **Community Support**: Apache Solr has a larger and more active community compared to Lucene, resulting in frequent updates, bug fixes, and new features being added to the platform, ensuring better long-term support and development.
6. **Deployment Options**: Apache Solr can be deployed as a standalone server or as a part of a larger application stack, making it flexible for various use cases, while Lucene is typically embedded within an application, limiting deployment options.

In Summary, Apache Solr provides a more scalable, user-friendly, and feature-rich search platform compared to Lucene, making it the preferred choice for applications requiring advanced search functionalities and high scalability. 

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Detailed Comparison

Lucene
Lucene
Apache Solr
Apache Solr

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 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.

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)
Advanced full-text search capabilities; Optimized for high volume traffic; Standards based open interfaces - XML, JSON and HTTP; Comprehensive administration interfaces; Easy monitoring; Highly scalable and fault tolerant; Flexible and adaptable with easy configuration
Statistics
Stacks
175
Stacks
224
Followers
230
Followers
91
Votes
2
Votes
0
Pros & Cons
Pros
  • 1
    Fast
  • 1
    Small
No community feedback yet
Integrations
Solr
Solr
Java
Java
No integrations available

What are some alternatives to Lucene, Apache Solr?

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.

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.

Searchkick

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.

Qdrant

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

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

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

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

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.

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