DBMS > mSQL vs. MySQL vs. Prometheus vs. SQLite vs. Titan

System Properties Comparison mSQL vs. MySQL vs. Prometheus vs. SQLite vs. Titan

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used open source RDBMS

Open-source Time Series DBMS and monitoring system

Widely used embeddable, in-process RDBMS

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

hughestech.com.au/products/msql

www.mysql.com

prometheus.io

www.sqlite.org

github.com/thinkaurelius/titan

Technical documentation

dev.mysql.com/doc

prometheus.io/docs

www.sqlite.org/docs.html

github.com/thinkaurelius/titan/wiki

Developer

Hughes Technologies

Oracle

since 2010, originally MySQL AB, then Sun

Dwayne Richard Hipp

Aurelius, owned by DataStax

Initial release

1994

1995

2015

2000

2012

Current release

4.4, October 2021

8.4.0, April 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C and C++

Java

Server operating systems

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

server-less

Linux
OS X
Unix
Windows

Data scheme

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

not rigid because of 'dynamic typing' concept.

yes

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

with proprietary extensions

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C
C++
Delphi
Java
Perl
PHP
Tcl

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

by Federation

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

none

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Relationships in graph

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

User authentification and security via Rexster Graph Server

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