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  5. MeiliSearch vs Solr

MeiliSearch vs Solr

OverviewComparisonAlternatives

Overview

Solr
Solr
Stacks805
Followers644
Votes126
MeiliSearch
MeiliSearch
Stacks125
Followers123
Votes10
GitHub Stars54.3K
Forks2.2K

MeiliSearch vs Solr: What are the differences?

MeiliSearch vs Solr: Key Differences

Introduction

MeiliSearch and Solr are both powerful search engines commonly used in websites and applications. However, they have key differences that set them apart in terms of features and functionality. In this comparison, we will highlight six main differences between MeiliSearch and Solr.

  1. Search Algorithm and Ranking: MeiliSearch utilizes BM25 as its default similarity algorithm, providing accurate search results based on term frequency and document relevance. On the other hand, Solr uses a combination of TF-IDF and BM25, offering a wide range of scoring and ranking options to meet specific search requirements.

  2. Real-time Updates: MeiliSearch excels at handling real-time updates, instantly reflecting changes made to indexed data without any delay. In contrast, Solr requires manual indexing or periodic updates, which can result in delays in reflecting changes in search results.

  3. Query Features: MeiliSearch offers a powerful and user-friendly search experience, supporting typo-tolerance, filtering, faceting, and customizable ranking rules out of the box. Solr, on the other hand, provides advanced query features, including highlighting, spatial search, and grouping, making it suitable for complex search requirements.

  4. Ease of Use: MeiliSearch is known for its simplicity and ease of use, with a minimalistic API and straightforward configuration. It requires no external dependencies and provides instant setup and deployment. In contrast, Solr has a steeper learning curve and requires additional setup and configuration for optimal performance.

  5. Language Support: MeiliSearch has native support for over 50 languages, ensuring accurate search results across a wide range of linguistic variations. Solr also supports multiple languages but may require additional configuration and customization to achieve the desired accuracy in search outcomes.

  6. Scalability: While both MeiliSearch and Solr are scalable, their approaches differ. MeiliSearch provides a horizontally scalable architecture, allowing the addition of multiple instances to handle increasing search traffic. Solr offers both horizontal and vertical scalability, enabling the distribution of searches across multiple servers and the allocation of more resources to handle large-scale deployments.

Summary

In summary, MeiliSearch stands out with its simple setup, real-time updates, and user-friendly query features, making it an excellent choice for developers seeking a hassle-free search engine solution. On the other hand, Solr offers more advanced features, customizable ranking options, and robust scalability options, making it suitable for complex search requirements and larger deployments.

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Detailed Comparison

Solr
Solr
MeiliSearch
MeiliSearch

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.

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.

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
Search as-you-type experience (answers < 50ms); Full-text search; Typo tolerant (understands typos and spelling mistakes); Supports Kanji; Supports Synonym; Easy to install, deploy, and maintain; Whole documents returned; Highly customizable; RESTfull API
Statistics
GitHub Stars
-
GitHub Stars
54.3K
GitHub Forks
-
GitHub Forks
2.2K
Stacks
805
Stacks
125
Followers
644
Followers
123
Votes
126
Votes
10
Pros & Cons
Pros
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
Pros
  • 1
    Open source
  • 1
    Restfull
  • 1
    Saas option
  • 1
    Great long tail search results
  • 1
    Fast responses to online chat
Integrations
Lucene
Lucene
No integrations available

What are some alternatives to Solr, MeiliSearch?

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).

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.

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.

Swiftype

Swiftype

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

Quickwit

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

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).

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