DBMS > Dgraph vs. HEAVY.AI vs. Interbase vs. Oracle Rdb

System Properties Comparison Dgraph vs. HEAVY.AI vs. Interbase vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Light-weight proven RDBMS

originally from Borland

Primary database model

Graph DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

dgraph.io

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

www.embarcadero.com/products/interbase

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

dgraph.io/docs

docs.heavy.ai

docs.embarcadero.com/products/interbase

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Dgraph Labs, Inc.

HEAVY.AI, Inc.

Embarcadero

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2016

1984

Current release

5.10, January 2022

InterBase 2020, December 2019

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

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++ and CUDA

Server operating systems

Linux
OS X
Windows

Linux

Android
iOS
Linux
OS X
Windows

HP Open VMS

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

Server-side scripts

Stored procedures

yes

Interbase procedure and trigger language

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication

Interbase Change Views

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

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

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More resources

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