Lucene, Solr, and Elasticsearch are all closely related technologies in the field of search and indexing, but they have different characteristics and use cases. Here’s a comparison:
Apache Lucene
- Core Technology:
- Lucene is a high-performance, full-featured text search engine library written in Java. It’s the core foundation upon which Solr and Elasticsearch are built.
- Use Case:
- Ideal for applications that require custom search functionality. It’s a library, so it requires significant development effort to implement as part of a larger application.
- Features:
- Provides advanced search capabilities like full-text search, ranking, and support for various document formats.
- Very flexible but requires more programming to harness its full potential.
- Scalability:
- On its own, it doesn’t handle distributed search or scalability. This needs to be implemented by the application.
Apache Solr
- Based on Lucene:
- Solr is an open-source search platform built on top of Lucene. It extends Lucene and provides a search server with additional features.
- Use Case:
- Suitable for enterprise-level search applications. Offers out-of-the-box search capabilities that are easier to implement compared to raw Lucene.
- Features:
- Provides distributed search and indexing, replication, faceting, caching, a web admin interface, and more.
- Has a REST-like API which makes it language agnostic.
- Scalability:
- Designed for high scalability and fault tolerance.
Elasticsearch
- Based on Lucene:
- Like Solr, Elasticsearch is built on top of Lucene. It’s a distributed, RESTful search and analytics engine capable of solving a growing number of use cases.
- Use Case:
- Ideal for real-time, distributed search and analytics use cases. It is highly scalable and designed for cloud environments.
- Features:
- Known for its ease of use, robustness, and good scalability. It supports complex search queries and is highly responsive.
- Offers comprehensive REST APIs and a simple setup process.
- Scalability:
- Highly scalable, can easily manage petabytes of structured and unstructured data.
Summary
- Lucene is the core library for full-text indexing and search technology but requires significant programming effort.
- Solr is a scalable search server that offers a lot of out-of-the-box features and is more enterprise-oriented.
- Elasticsearch is renowned for its easy setup, scalability, and suitability for search and analytics use cases, especially in real-time applications.
The choice among these three depends on specific project requirements, existing technical infrastructure, and the level of customization needed.
- Elasticsearch has gained significant popularity for its ease of use and performance in various scenarios, including logging (ELK stack),
- while Solr is often favored in traditional enterprise search environments.
Some extra readings
Elasticsearch
- Elasticsearch + some Lucene concept https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up
- from the “top”, as observed from a user’s perspective https://www.elastic.co/blog/found-elasticsearch-top-down
- Elasticsearch index has shards, which are Lucene indexes. And those have inverted indexes
- Optimizing Elasticsearch Searches https://www.elastic.co/blog/found-optimizing-elasticsearch-searches/
- Sizing Elasticsearch https://www.elastic.co/blog/found-sizing-elasticsearch/
- For time oriented data, such as logs, a common strategy is to partition data into indexes that hold data for a certain time range
- Index per User
Solr
- https://solr.apache.org/guide/7_5/how-solrcloud-works.html
- https://www.tutorialspoint.com/apache_solr/apache_solr_architecture.htm
Lucene
- https://alibaba-cloud.medium.com/analysis-of-lucene-basic-concepts-5ff5d8b90a53
- An index is composed of one or more sub-indexes. A sub-index is called a segment