DBMS > Cubrid vs. Dgraph vs. Ehcache vs. mSQL vs. Tarantool

System Properties Comparison Cubrid vs. Dgraph vs. Ehcache vs. mSQL vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Distributed and scalable native Graph DBMS

A widely adopted Java cache with tiered storage options

mSQL (Mini SQL) is a simple and lightweight RDBMS

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

dgraph.io

www.ehcache.org

hughestech.com.au/products/msql

www.tarantool.io

Technical documentation

cubrid.org/manuals

dgraph.io/docs

www.ehcache.org/documentation

www.tarantool.io/en/doc

Developer

CUBRID Corporation, CUBRID Foundation

Dgraph Labs, Inc.

Terracotta Inc, owned by Software AG

Hughes Technologies

Initial release

2008

2016

2009

1994

2008

Current release

11.0, January 2021

3.10.0, March 2022

4.4, October 2021

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

free licenses can be provided

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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.

Implementation language

C, C++, Java

Java

C and C++

Server operating systems

Linux
Windows

Linux
OS X
Windows

All OS with a Java VM

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

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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.

Secondary indexes

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

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

Full-featured ANSI SQL support

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JCache

JDBC
ODBC

Open binary protocol

Supported programming languages

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

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

Java

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

Lua, C and SQL stored procedures

Triggers

yes

Cache Event Listeners

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

by using Terracotta Server

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication via Raft

yes

by using Terracotta Server

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

none

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

fine grained access rights according to SQL-standard

Planned for future releases

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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