DBMS > Google Cloud Datastore vs. OpenEdge vs. OpenQM vs. Oracle Berkeley DB vs. Oracle Rdb

System Properties Comparison Google Cloud Datastore vs. OpenEdge vs. OpenQM vs. Oracle Berkeley DB vs. Oracle Rdb

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Application development environment with integrated database management system

QpenQM is a high-performance, self-tuning, multi-value DBMS

Widely used in-process key-value store

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

cloud.google.com/datastore

www.progress.com/openedge

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

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

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

Technical documentation

cloud.google.com/datastore/docs

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/cd/E17076_05/html/index.html

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

Developer

Google

Progress Software Corporation

Rocket Software, originally Martin Phillips

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2008

1984

1993

1994

1984

Current release

OpenEdge 12.2, March 2020

3.4-12

18.1.40, May 2020

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

commercial license 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

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

HP Open VMS

Data scheme

schema-free

yes

with some exceptions

schema-free

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

Typing

predefined data types such as float or date

yes, details here

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

close to SQL 92

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Progress proprietary ABL (Advanced Business Language)

.Net
Basic
C
Java
Objective C
PHP
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

since Version 11.4

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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)

Users and groups

Access rights can be defined down to the item level

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