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  5. Elasticsearch vs Lucene

Elasticsearch vs Lucene

OverviewDecisionsComparisonAlternatives

Overview

Elasticsearch
Elasticsearch
Stacks35.5K
Followers27.1K
Votes1.6K
Lucene
Lucene
Stacks175
Followers230
Votes2

Elasticsearch vs Lucene: What are the differences?

Introduction

Elasticsearch and Lucene are both open-source search engines widely used in information retrieval applications. While they share similarities, they also have key differences that set them apart.

  1. Data Model: Elasticsearch is a document-oriented search engine, while Lucene is a low-level library that provides access to inverted index structures. In Elasticsearch, data is stored as JSON documents, allowing for flexible and schema-less indexing. On the other hand, Lucene operates at a lower level, providing APIs to index and search individual fields within a document.

  2. Scalability and Distribution: Elasticsearch is designed to be highly scalable and distributed from the ground up. It allows for horizontal scaling by dividing the data across multiple nodes in a cluster, enabling efficient retrieval and processing even as the amount of data grows. Lucene, on the other hand, is a Java library that focuses on providing powerful indexing and search capabilities within a single machine.

  3. Query Language: Elasticsearch offers a RESTful API with its own query language called Query DSL. This language allows users to perform complex searches, aggregations, and statistical calculations on their data. In contrast, Lucene provides a programmatic API to perform searches, which requires writing code to construct queries and process search results.

  4. Full-Text Search vs. Indexing: Elasticsearch provides full-text search capabilities out-of-the-box, allowing users to perform efficient search operations on large volumes of text. Lucene primarily focuses on indexing and retrieval tasks and can be used as a building block for implementing search functionality. While Lucene can be utilized for full-text search, additional code and configurations are required.

  5. Real-Time Search: Elasticsearch offers real-time search capabilities, meaning that documents are indexed and made available for search almost immediately after they are added or modified. Lucene, being a lower-level library, does not provide this real-time functionality by default and requires additional effort to achieve similar capabilities.

  6. Community and Ecosystem: Elasticsearch has a large and active community, providing a wide range of plugins and integrations with other tools and frameworks. It has gained popularity as a versatile and scalable search and analytics platform. Lucene, being the underlying library for Elasticsearch, also has an active community but is more focused on providing low-level indexing and search capabilities.

In summary, Elasticsearch provides a distributed, scalable, and document-oriented search engine with its own query language, while Lucene is a powerful Java library that offers low-level indexing and search capabilities within a single machine.

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Advice on Elasticsearch, Lucene

Rana Usman
Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceonFirebaseFirebaseElasticsearchElasticsearchAlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k
Comments

Detailed Comparison

Elasticsearch
Elasticsearch
Lucene
Lucene

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).

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

Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
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
35.5K
Stacks
175
Followers
27.1K
Followers
230
Votes
1.6K
Votes
2
Pros & Cons
Pros
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
Cons
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
Pros
  • 1
    Small
  • 1
    Fast
Integrations
Kibana
Kibana
Beats
Beats
Logstash
Logstash
Solr
Solr
Java
Java

What are some alternatives to Elasticsearch, 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.

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

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.

Amazon CloudSearch

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

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.

Swiftype

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

MeiliSearch

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

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