DBMS > ArcadeDB vs. Google Cloud Bigtable vs. GridDB vs. OpenEdge vs. Oracle Rdb

System Properties Comparison ArcadeDB vs. Google Cloud Bigtable vs. GridDB vs. OpenEdge vs. Oracle Rdb

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Scalable in-memory time series database optimized for IoT and Big Data

Application development environment with integrated database management system

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	0.02
Rank	#374	Overall
	#52	Document stores
	#38	Graph DBMS
	#56	Key-value stores
	#38	Time Series DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	3.69
Rank	#78	Overall
	#42	Relational DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Website

arcadedb.com

cloud.google.com/bigtable

griddb.net

www.progress.com/openedge

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

Technical documentation

docs.arcadedb.com

cloud.google.com/bigtable/docs

docs.griddb.net

documentation.progress.com/output/ua/OpenEdge_latest

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

Developer

Arcade Data

Google

Toshiba Corporation

Progress Software Corporation

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2021

2015

2013

1984

Current release

September 2021

5.1, August 2022

OpenEdge 12.2, March 2020

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

hosted

Linux

AIX
HP-UX
Linux
Solaris
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

numerical, string, blob, geometry, boolean, timestamp

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language, no joins

SQL92, SQL-like TQL (Toshiba Query Language)

yes

close to SQL 92

yes

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Java

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

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

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID at container level

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users can be defined per database

Users and groups

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