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  5. Algolia vs Lucene

Algolia vs Lucene

OverviewDecisionsComparisonAlternatives

Overview

Algolia
Algolia
Stacks1.4K
Followers1.1K
Votes699
Lucene
Lucene
Stacks175
Followers230
Votes2

Algolia vs Lucene: What are the differences?

Introduction

Algolia and Lucene are both search technologies used to provide efficient and accurate search capabilities in applications. While they share the common goal of enabling efficient searching, there are key differences between the two.

  1. Architecture: Algolia is a cloud-based search-as-a-service platform, while Lucene is a full-text search library. Algolia provides a hosted solution that abstracts away the complexities of infrastructure management, whereas Lucene is a Java-based library that developers need to integrate and manage within their own systems.

  2. Scalability: Algolia is designed to handle millions of queries per second and scales automatically to meet the demand, making it suitable for high-volume applications. On the other hand, Lucene requires manual indexing and management of search indexes, which may be more suitable for small to medium-scale applications with lower search query volumes.

  3. Real-time indexing and updates: Algolia offers real-time indexing and updates out of the box, allowing changes to data to be reflected in search results immediately. Lucene typically requires manual indexing processes to update search indexes, which may cause a delay between data changes and their availability in search results.

  4. Relevance ranking: Algolia includes customizable relevance ranking algorithms that can be fine-tuned to prioritize certain search results over others based on various factors. Lucene also provides relevance ranking capabilities but may require more hands-on configuration to achieve desired ranking behavior.

  5. Query language: Algolia provides a simple and intuitive query language that allows developers to easily construct complex search queries with minimal effort. Lucene, on the other hand, offers a more low-level API and requires developers to write code to construct search queries, making it more suitable for advanced search scenarios that require fine-grained control.

  6. Analytics and A/B testing: Algolia offers built-in analytics and A/B testing capabilities to enable developers to analyze search performance and optimize search results. Lucene does not include these features natively and would require additional custom implementation to achieve similar functionality.

In summary, Algolia is a cloud-based search-as-a-service platform with automatic scalability, real-time indexing, and customizable relevance ranking. Lucene, on the other hand, is a Java-based search library that requires manual indexing and configuration but provides fine-grained control over search queries.

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Advice on Algolia, 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

Algolia
Algolia
Lucene
Lucene

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.

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

Database search;Multi-attributes;Search as you type;Analytics dashboard; Ranking; Mobile; Search in any language;Understand users mistakes;Smart Highlighting;Realtime indexing;Protect your indexes from misuse;Discover realtime faceting;Search objects by location
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
1.4K
Stacks
175
Followers
1.1K
Followers
230
Votes
699
Votes
2
Pros & Cons
Pros
  • 126
    Ultra fast
  • 95
    Super easy to implement
  • 73
    Modern search engine
  • 71
    Excellent support
  • 70
    Easy setup, fast and relevant
Cons
  • 11
    Expensive
Pros
  • 1
    Fast
  • 1
    Small
Integrations
React
React
Ruby
Ruby
Jekyll
Jekyll
JavaScript
JavaScript
React Native
React Native
Vue.js
Vue.js
WordPress
WordPress
Shopify
Shopify
Docusaurus
Docusaurus
VuePress
VuePress
Solr
Solr
Java
Java

What are some alternatives to Algolia, Lucene?

Elasticsearch

Elasticsearch

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).

Solr

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.

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.

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