DBMS > EsgynDB vs. Fujitsu Enterprise Postgres vs. Spark SQL vs. ToroDB

System Properties Comparison EsgynDB vs. Fujitsu Enterprise Postgres vs. Spark SQL vs. ToroDB

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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

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.37
Rank	#278	Overall
	#128	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

www.postgresql.fastware.com

spark.apache.org/sql

github.com/torodb/server

Technical documentation

www.postgresql.fastware.com/product-manuals

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Esgyn

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Apache Software Foundation

8Kdata

Initial release

2015

2014

2016

Current release

Fujitsu Enterprise Postgres 14, January 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Scala

Java

Server operating systems

Linux

Linux
Windows

Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

partitioning by range, list and by hash

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

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 capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

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