Solr vs Sonic Server

Overview

Solr

Stacks805

Followers644

Votes126

Sonic Server

Stacks4

Followers24

Votes0

GitHub Stars21.0K

Forks604

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.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Solr	Sonic Server
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.	A fast, lightweight and schema-less search backend. It ingests search texts and identifier tuples that can then be queried against in microseconds.
Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture	Search terms are stored in collections, organized in buckets; Search results return object identifiers;; Search query typos are corrected; Insert and remove items in the index; Auto-complete any word in real-time;; Full Unicode compatibility; Networked channel interface; Easy-to-use libraries
Statistics
GitHub Stars -	GitHub Stars 21.0K
GitHub Forks -	GitHub Forks 604
Stacks 805	Stacks 4
Followers 644	Followers 24
Votes 126	Votes 0
Pros & Cons
Pros 35 Powerful 22 Indexing and searching 20 Scalable 19 Customizable 13 Enterprise Ready	No community feedback yet
Integrations
Lucene	PHP Python Rust Golang Node.js

What are some alternatives to Solr, Sonic Server?

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.

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.

MkDocs

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

Dejavu

dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).

Elassandra

Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.

Tantivy

It is a full-text search engine library inspired by Apache Lucene and written in Rust. It is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.

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.

Jina

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

Google

Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

YugabyteDB

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

Related Comparisons

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.

Solr vs Sonic Server

Overview