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Apache Solr Tutorial

Apache Solr Tutorial

Solr is a specific NoSQL technology that is optimized for a unique class of problems. Solr is a scalable, ready-to-deploy enterprise search engine that was developed to search a large volume of text-centric data and returns results sorted by relevance. The following are the benefits of Apache Solr:

  • Scalable: It scales by distributing indexing and query processing to multiple servers in a cluster.
  • Ready to deploy: It is open-source, easy to install and configure, and provides a preconfigured example to help you get started.
  • Optimized for search: It is fast and can execute complex queries in a fraction of second speed, often only tens of milliseconds.
  • Large volumes of documents: It is designed to deal with indexes containing many millions of documents.
  • Text-centric: It is optimized for searching natural-language text, like emails, web pages, resumes, PDF documents, and social messages such as tweets or blogs.
  • Results sorted by relevance: It returns documents in ranked order based on how relevant each document is to the user's query.

Note: Solar isn't a web search engine like Google or Bing and had nothing to do with search engine optimization for a website.

Why do we need a Search Engine?

Search Engines mainly work as filters for the need of information available on the World Wide Web. It allows us to quickly and easily find any information about their interest or value, without the need to go through numerous irrelevant web pages. The goal of the Search Engines is to give users filtered search results that lead to relevant information on high-quality websites where a huge number of data is available, such as- JavaTpoint, Wikipedia, etc.

Features Overview

Let us briefly explain some random key features of Solr that are organized along with the following categories:

  • User experience
  • Data modeling
  • A new feature in Solr

User-experience features

Apache Solr provides several important features that can be used to deliver a search solution that's easy to use, initiative, and powerful. It only exposes a REST-like HTTP API and cannot provide a search-related UI component in any language or framework. Following are the UI components that take advantage of some of the following user experience features:

  • Pagination and sorting: Solr is optimized to serve pagination and requests rather than returning all matching documents, in which only the top N documents are returned on the first page. If users don't find what they're looking for on the first page.
  • Faceting: It provides users with tools to refine their search criteria and discover more information by categorizing search results into subgroups using facets.
  • Autosuggest: It can be used by the users who expect that their search application to "do the right thing" even if they fill the incomplete information. It allows users to see the lists of suggested terms and phrases based on documents in your index.
  • Spell-checker: It can be used to automatically correct the word we want to write. Users expect the search engine to handle misspelling gracefully.
  • Hit highlighting: It can be used to show the specific sections of each document.
  • Geospatial search: It can be used by solr to sort the documents by distance from geolocation.


This online tutorial will be helpful for all those students and developers who would like to understand the basic functionalities of Apache Solr for the development of sophisticated and high-performing applications.


In this Solr tutorial, we will learn how to use Solr to design and implement scalable search solutions. We will begin with the types of data and use cases supported by Solr. It will help us understand where Solr fits into the big picture of modern application architectures.


Before you start learning with this tutorial, we expect that you have good knowledge of Java programming. It is not mandatory. Still, it will help to understand better Solr and some prior exposure to Lucene and Hadoop environment.


We assure you that you will not find any difficulty while learning through our Solr tutorial. But if you find any mistake in this tutorial, we request you to kindly post the problem in the contact form so that we can improve it.

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