Elasticsearch vs Solr: What are the differences?
Introduction
Elasticsearch and Solr are both widely used open-source search engines used for full-text search, analytics, and distributed search and analytics. While they have similar functionalities, there are key differences between the two. This article will highlight six key differences between Elasticsearch and Solr.
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Query Types: Elasticsearch supports a broader range of query types than Solr. Elasticsearch has a rich query language that allows for more complex queries, including nested queries, fuzzy queries, and wildcard queries, among others. Solr, on the other hand, has a more limited set of query types and is generally more focused on keyword-based search.
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Scalability and Distributed Search: Elasticsearch is built on a distributed architecture and is designed for scalability and distributed search out of the box. It uses automatic sharding and replication, making it easier to scale horizontally. Solr has added distributed capabilities over the years but requires manual configuration for scaling out and distributed search.
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Document Oriented: Elasticsearch is document-oriented, meaning it stores and indexes whole documents rather than individual fields. This makes it more suitable for scenarios where the entire document needs to be searched and retrieved. Solr, on the other hand, is traditionally field-oriented, meaning it focuses more on individual fields within a document.
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Data Replication and High Availability: Elasticsearch comes with built-in support for data replication and high availability. It automatically replicates data across nodes, ensuring that there are copies of the data available in case of node failures. Solr, while it has some support for replication and high availability, requires manual setup and configuration.
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Real-time Analytics: Elasticsearch has better support for real-time analytics compared to Solr. It offers near real-time search, meaning that documents are indexed and made searchable almost immediately. Solr, on the other hand, has a delay in indexing and can take some time before new documents are searchable.
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Ecosystem and Community: Elasticsearch has a larger and more active community compared to Solr. This means that there are more resources, plugins, and community support available for Elasticsearch. Additionally, Elasticsearch has a broader ecosystem of tools and integrations, making it easier to integrate with other systems.
In summary, Elasticsearch offers a more extensive query language, better scalability and distributed search capabilities, supports document-oriented indexing, provides built-in data replication and high availability mechanisms, offers near real-time search, and has a larger ecosystem and community compared to Solr.
