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Solr vs Sphinx: What are the differences?

Introduction

Solr and Sphinx are both search platforms that offer powerful indexing and retrieval capabilities. Although they share similarities, there are key differences between the two.

  1. Architecture: Solr is built on Apache Lucene, which is a full-text search library, whereas Sphinx is a standalone search engine. Solr is a Java-based application that requires Java Virtual Machine (JVM) to run, while Sphinx is written in C++ and does not require any external dependencies.

  2. Scalability: Solr is known for its scalability and can handle large amounts of data with ease. It supports distributed searching and indexing, making it suitable for handling big data applications. On the other hand, Sphinx is also scalable but is better suited for smaller deployments and does not provide the same level of distributed capabilities as Solr.

  3. Query Language: Solr uses a custom query language called Solr Query Syntax (SOS), which is similar to SQL and allows for complex queries and filtering. Sphinx, on the other hand, uses SphinxQL, which is also SQL-like but has certain limitations compared to SOS. Solr's query language provides more flexibility and advanced features for querying and filtering search results.

  4. Integration with Databases: Solr has extensive integration capabilities with various databases, such as MySQL, Oracle, and PostgreSQL. This allows Solr to seamlessly index and search data from multiple sources. Sphinx also supports integration with databases but is more commonly used with its own storage engine.

  5. Faceted Search: Solr provides robust support for faceted search, which allows users to filter and narrow down search results based on different attributes or facets. Sphinx, on the other hand, has limited support for faceted search and requires additional customization to achieve similar functionality.

  6. Community and Ecosystem: Solr has a larger and more active community compared to Sphinx, which results in a wider range of available resources, plugins, and extensions. Solr also has better documentation and is more widely adopted in the industry, making it easier to find skilled developers and support.

In summary, Solr and Sphinx differ in their architecture, scalability, query language, integration capabilities, support for faceted search, and community ecosystem.

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Pros of Solr
Pros of Sphinx
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Restful
  • 5
    Apache Software Foundation
  • 4
    Great Search engine
  • 2
    Security built-in
  • 1
    Easy Operating
  • 16
    Fast
  • 9
    Simple deployment
  • 6
    Open source
  • 1
    Lots of extentions

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What is Solr?

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.

What is Sphinx?

It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

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What companies use Solr?
What companies use Sphinx?
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What tools integrate with Solr?
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What are some alternatives to Solr and Sphinx?
Lucene
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
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.
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).
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
See all alternatives