DBMS > JaguarDB vs. MySQL vs. SiriDB vs. Solr vs. Sphinx

System Properties Comparison JaguarDB vs. MySQL vs. SiriDB vs. Solr vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Widely used open source RDBMS

Open Source Time Series DBMS

A widely used distributed, scalable search engine based on Apache Lucene

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.11
Rank	#346	Overall
	#32	Time Series DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

Technical documentation

www.jaguardb.com/support.html

dev.mysql.com/doc

docs.siridb.com

solr.apache.org/resources.html

sphinxsearch.com/docs

Developer

DataJaguar, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Cesbit

Apache Software Foundation

Sphinx Technologies Inc.

Initial release

2015

1995

2017

2006

2001

Current release

3.3 July 2023

8.3.0, January 2024

9.5.0, February 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

MIT License

Open Source

Apache Version 2

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

C and C++

Java

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

Numeric data

yes

supports customizable data types and automatic typing

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

with proprietary extensions

Solr Parallel SQL Interface

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

simple rights management via user accounts

yes

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