DBMS > ArcadeDB vs. Google Cloud Datastore vs. Infobright vs. Sadas Engine

System Properties Comparison ArcadeDB vs. Google Cloud Datastore vs. Infobright vs. Sadas Engine

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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

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store

Relational DBMS

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

arcadedb.com

cloud.google.com/datastore

ignitetech.com/softwarelibrary/infobrightdb

www.sadasengine.com

Technical documentation

docs.arcadedb.com

cloud.google.com/datastore/docs

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

Developer

Arcade Data

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

SADAS s.r.l.

Initial release

2021

2008

2005

2006

Current release

September 2021

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

free trial version available

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
Windows

AIX
Linux
Windows

Data scheme

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.

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like query language, no joins

SQL-like query language (GQL)

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
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

Java

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Source-replica replication

none

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

relationship in graphs

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

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

exploiting MySQL or PostgreSQL frontend capabilities

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

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