DBMS > Google Cloud Datastore vs. H2 vs. Sadas Engine vs. TerminusDB

System Properties Comparison Google Cloud Datastore vs. H2 vs. Sadas Engine vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Document store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

Document store
RDF store

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

cloud.google.com/datastore

www.h2database.com

www.sadasengine.com

terminusdb.com

Technical documentation

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

terminusdb.github.io/terminusdb/#

Developer

Google

Thomas Mueller

SADAS s.r.l.

DataChemist Ltd.

Initial release

2008

2005

2006

2018

Current release

2.2.220, July 2023

8.0

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

free trial version available

Open Source

GPL V3

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++

Prolog, Rust

Server operating systems

hosted

All OS with a Java VM

AIX
Linux
Windows

Linux

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language (WOQL)

APIs and other access methods

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

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

Java

.Net
C
C#
C++
Groovy
Java
PHP
Python

JavaScript
Python

Server-side scripts

Stored procedures

using Google App Engine

Java Stored Procedures and User-Defined Functions

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

With clustering: 2 database servers on different computers operate on identical copies of a database

none

Journaling Streams

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory journaling

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Role-based access control

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