DBMS > Cubrid vs. Geode vs. Google Cloud Datastore vs. Microsoft SQL Server

System Properties Comparison Cubrid vs. Geode vs. Google Cloud Datastore vs. Microsoft SQL Server

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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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Microsofts flagship relational DBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

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

geode.apache.org

cloud.google.com/datastore

www.microsoft.com/en-us/sql-server

Technical documentation

cubrid.org/manuals

geode.apache.org/docs

cloud.google.com/datastore/docs

learn.microsoft.com/en-US/sql/sql-server

Developer

CUBRID Corporation, CUBRID Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Microsoft

Initial release

2008

2002

2008

1989

Current release

11.0, January 2021

1.1, February 2017

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java

C++

Server operating systems

Linux
Windows

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

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 (OQL)

SQL-like query language (GQL)

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

Java Client API
Memcached protocol
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

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

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

using Google App Engine

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Cache Event Listeners

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Multi-source replication using Paxos

yes, but depending on the SQL-Server Edition

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

Eventual 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

yes, on a single node

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

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

Access rights per client and object definable

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

fine grained access rights according to SQL-standard

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