DBMS > Cubrid vs. Graph Engine vs. HEAVY.AI vs. XTDB

System Properties Comparison Cubrid vs. Graph Engine vs. HEAVY.AI vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	0.97
Rank	#189	Overall
	#86	Relational DBMS

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

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

www.graphengine.io

github.com/heavyai/heavydb
www.heavy.ai

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cubrid.org/manuals

www.graphengine.io/docs/manual

docs.heavy.ai

www.xtdb.com/docs

Developer

CUBRID Corporation, CUBRID Foundation

Microsoft

HEAVY.AI, Inc.

Juxt Ltd.

Initial release

2008

2010

2016

2019

Current release

11.0, January 2021

5.10, January 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache Version 2; enterprise edition available

Open Source

MIT License

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

.NET and C

C++ and CUDA

Clojure

Server operating systems

Linux
Windows

.NET

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP REST
JDBC

Supported programming languages

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

C#
C++
F#
Visual Basic

All languages supporting JDBC/ODBC/Thrift
Python

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

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