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Solr vs Sonic Server: What are the differences?
## Introduction
When comparing Solr and Sonic Server, there are key differences that differentiate the two search platforms.
1. **Indexing Approach**: Solr uses an inverted index for indexing text data, enabling efficient search retrieval by mapping content to terms and documents. In contrast, Sonic Server utilizes the inverted index combined with the FST (Finite State Transducer) to achieve fast search queries, making it ideal for real-time applications.
2. **Real-Time Indexing**: Solr requires commits to make indexed data searchable, while Sonic Server supports real-time indexing without the need for commits. This difference speeds up data availability and searchability in Sonic Server compared to Solr.
3. **Performance**: Solr is designed for handling large-scale data and complex queries effectively, making it suitable for enterprise search applications. Sonic Server focuses on speed and low-latency search queries, making it more suitable for real-time search applications where performance is crucial.
4. **Query Language**: Solr uses the Lucene Query Syntax, allowing users to construct complex queries with Boolean logic and proximity search. Sonic Server, on the other hand, provides a JSON-based query language that is more intuitive for developers and simplifies query construction and customization.
5. **Scalability**: Solr offers scalability through distributed indexing and search capabilities, making it suitable for growing data volumes and high query loads. In comparison, Sonic Server provides horizontal scaling through sharding and replication, enabling seamless expansion based on performance demands.
6. **Data Storage**: Solr stores data in Apache Lucene indexes on disk, which can lead to higher disk usage for large datasets. In contrast, Sonic Server stores data in in-memory structured indexes, delivering faster search performance but requiring more memory resources.
In Summary, Solr and Sonic Server differ in indexing approach, real-time indexing, performance, query language, scalability, and data storage mechanisms.
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Learn MorePros of Solr
Pros of Sonic Server
Pros of Solr
- Powerful35
- Indexing and searching22
- Scalable20
- Customizable19
- Enterprise Ready13
- Restful5
- Apache Software Foundation5
- Great Search engine4
- Security built-in2
- Easy Operating1
Pros of Sonic Server
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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 Sonic Server?
A fast, lightweight and schema-less search backend. It ingests search texts and identifier tuples that can then be queried against in microseconds.
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What companies use Solr?
What companies use Sonic Server?
What companies use Sonic Server?
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What tools integrate with Solr?
What tools integrate with Sonic Server?
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What are some alternatives to Solr and Sonic Server?
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.