DBMS > EsgynDB vs. Google Cloud Datastore vs. Oracle Rdb vs. SAP SQL Anywhere vs. ToroDB

System Properties Comparison EsgynDB vs. Google Cloud Datastore vs. Oracle Rdb vs. SAP SQL Anywhere vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

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

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	3.50
Rank	#81	Overall
	#44	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/datastore

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

www.sap.com/products/technology-platform/sql-anywhere.html

github.com/torodb/server

Technical documentation

cloud.google.com/datastore/docs

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Esgyn

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

SAP

formerly Sybase

8Kdata

Initial release

2015

2008

1984

1992

2016

Current release

7.4.1.1, 2021

17, July 2015

License

Commercial or Open Source

commercial

Open Source

AGPL-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

C++, Java

Java

Server operating systems

Linux

hosted

HP Open VMS

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

string, integer, double, boolean, date, object_id

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

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

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

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

yes, in C/C++, Java, .Net or Perl

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication using Paxos

Source-replica replication

Database mirroring

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

Eventual Consistency
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

yes, on a single node

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 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 and roles

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