![]() Referred to as SolrCloud, and backed by Apache Zookeeper, these capabilities provide distributed, sharded, and replicated indexing and search capabilities.Īccording to Wikipedia, Solr was created at CNET Networks in 2004, donated to the Apache Software Foundation in 2006, and graduated from their incubator in 2007. Solr powers the search and navigation features of many of the world’s largest internet sites.Īpache Solr includes the ability to set up a cluster of Solr servers that combines fault tolerance and high availability. Apache SolrĪccording to Apache, Apache Solr is the popular, blazing fast, open source, enterprise search platform built on Apache Lucene. The Lucene Core sub-project provides Java-based indexing and search technology, as well as spellchecking, hit highlighting, and advanced analysis/tokenization capabilities.Īpache Lucene 7.7.0 and Apache Solr 7.7.0 were just released in February 2019 and used for all the post’s examples. Apache LuceneĪccording to Apache, the Apache Lucene project develops open-source search software, including the following sub-projects: Lucene Core, Solr, and PyLucene. ![]() We will consider the differences between querying for data and searching for information. We will compare and contrast Solr’s search capabilities to those of MongoDB, the leading NoSQL database. In this post, we will examine the search capabilities of Apache Solr. Real-Time and Streaming Data (such as IoT).ML (Machine Learning) and AI (Artificial Intelligence).Reporting, Data Analytics, and Big Data.CQRS (Command Query Responsibility Segregation) and Event Sourcing.Architectures in which this is common include the following. I’ve worked on many projects where the requirements suggested an architecture in which one type of data storage technology should be implemented to optimize for writes, while a different type or types of data storage technologies should be implemented to optimize for reads. Separating database reads from writes is not uncommon. In a scenario where data consumers are arbitrarily searching for relevant information within a distinct domain, implementing a search-optimized, Lucene-based platform, such as Elasticsearch or Apache Solr, for reads, is often an effective solution. End-users are not Database Administrators, they do not understand the nuances of SQL, they simply want relevant responses to their inquiries. They force end-users to search in unnatural or highly-structured ways or provide results that lack a sense of relevancy. Architects and Developers who limit themselves to traditional databases, often attempt to meet search requirements by creating unnecessarily and overly complex SQL query-based solutions. The ability to search for information is a basic requirement of many applications. We will then delve into some of Solr’s more advanced search capabilities. We will explore the similarities and differences between Solr and MongoDB by analyzing a series of comparative queries. In this post, we will examine what sets Apache Solr aside as a search engine, from conventional databases like MongoDB.
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