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  1. Stackups
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  4. Search As A Service
  5. Amazon CloudSearch vs Solr

Amazon CloudSearch vs Solr

OverviewComparisonAlternatives

Overview

Amazon CloudSearch
Amazon CloudSearch
Stacks130
Followers152
Votes27
Solr
Solr
Stacks805
Followers644
Votes126

Amazon CloudSearch vs Solr: What are the differences?

Key Differences between Amazon CloudSearch and Solr

Introduction

In this article, we will compare Amazon CloudSearch and Solr, two popular search platforms, and discuss the key differences between them.

  1. Ease of Setup and Configuration: Amazon CloudSearch is a fully managed service, which means that Amazon takes care of the infrastructure and maintenance, making it easier to set up and configure. On the other hand, Solr requires manual setup, configuration, and maintenance, which may require more technical expertise.

  2. Scalability and Availability: Amazon CloudSearch is designed to scale automatically based on the data and query load, providing high availability and performance out of the box. Solr requires manual configuration and infrastructure adjustments to achieve the desired scalability and availability.

  3. Data Management: While both Amazon CloudSearch and Solr handle structured and unstructured data, CloudSearch has limitations in terms of supported data types and limited text processing capabilities. Solr, being highly customizable, offers more flexibility in terms of data types, indexing options, and text analysis.

  4. Search Features: Amazon CloudSearch offers a simplified search functionality that covers most common search use cases. However, it may lack some advanced features found in Solr, such as facet counting, spell checking, highlighting, and more. Solr provides extensive search features and capabilities, making it suitable for complex search scenarios.

  5. Integration with Other AWS Services: Amazon CloudSearch seamlessly integrates with other AWS services such as Amazon S3 for data storage, Amazon RDS for database integration, and AWS Identity and Access Management for user authentication and authorization. Solr, being an open-source search platform, may require additional integration efforts to connect with different AWS services.

  6. Customization and Extensibility: Solr offers extensive customization options, allowing users to modify the behavior of the search engine, create custom indexing pipelines, and use various plug-ins and extensions. CloudSearch, on the other hand, has limited customization capabilities, providing a more simplified and managed approach to search.

In Summary, Amazon CloudSearch is a fully managed, scalable, and easy-to-set-up search service that provides basic search functionality, while Solr offers more advanced features, customization options, and flexibility at the cost of manual configuration and maintenance efforts.

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Detailed Comparison

Amazon CloudSearch
Amazon CloudSearch
Solr
Solr

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.

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.

Simple to Configure – You can make your data searchable using the AWS Management Console, API calls, or command line tools. Simply point to a sample set of data, and Amazon CloudSearch automatically proposes a list of index fields and a suggested configuration.;Automatic Scaling For Data & Traffic – Amazon CloudSearch scales up and down seamlessly as the amount of data or query volume changes.;Low Latency, High Throughput – Amazon CloudSearch always stores your index in RAM to ensure low latency and high throughput performance even at large scale. Amazon CloudSearch was created from the same A9 technology that powers search on Amazon.com.;Rich Search Features – Amazon CloudSearch indexes and searches both structured data and plain text. It includes most search features that developers have come to expect from a search engine, such as faceted search, free text search, Boolean search, customizable relevance ranking, query time rank expressions, field weighting, and sorting of results using any field. Amazon CloudSearch also provides near real-time indexing of document updates.;Secure – Amazon CloudSearch uses strong cryptographic methods to authenticate users and prevent unauthorized control of your domains. Amazon CloudSearch supports HTTPS and includes web service interfaces to configure firewall settings that control network access to your domain.
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
Statistics
Stacks
130
Stacks
805
Followers
152
Followers
644
Votes
27
Votes
126
Pros & Cons
Pros
  • 12
    Managed
  • 7
    Auto-Scaling
  • 5
    Compound Queries
  • 3
    Easy Setup
Pros
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
Integrations
No integrations available
Lucene
Lucene

What are some alternatives to Amazon CloudSearch, Solr?

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 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.

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.

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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