DBMS > Cubrid vs. Datomic vs. Google Cloud Datastore vs. InterSystems Caché

System Properties Comparison Cubrid vs. Datomic vs. Google Cloud Datastore vs. InterSystems Caché

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

A multi-model DBMS and application server

Primary database model

Relational DBMS

Document store

Key-value store
Object oriented DBMS
Relational DBMS

Secondary database models

Document store

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

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Website

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

www.datomic.com

cloud.google.com/datastore

www.intersystems.com/products/cache

Technical documentation

cubrid.org/manuals

docs.datomic.com

cloud.google.com/datastore/docs

docs.intersystems.com

Developer

CUBRID Corporation, CUBRID Foundation

Cognitect

Google

InterSystems

Initial release

2008

2012

2008

1997

Current release

11.0, January 2021

1.0.7075, December 2023

2018.1.4, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

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, C++, Java

Java, Clojure

Server operating systems

Linux
Windows

All OS with a Java VM

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

depending on used data model

Typing

predefined data types such as float or date

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

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

.NET Client API
JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

Clojure
Java

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

C#
C++
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Transaction Functions

using Google App Engine

yes

Triggers

yes

By using transaction functions

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

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

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Access rights for users, groups and roles

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