DBMS > GBase vs. MonetDB vs. RocksDB vs. Spark SQL vs. STSdb

System Properties Comparison GBase vs. MonetDB vs. RocksDB vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A relational database management system that stores data in columns

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

github.com/STSSoft/STSdb4

Technical documentation

www.monetdb.org/Documentation

github.com/facebook/rocksdb/wiki

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

Developer

General Data Technology Co., Ltd.

MonetDB BV

Facebook, Inc.

Apache Software Foundation

STS Soft SC

Initial release

2004

2013

2014

2011

Current release

GBase 8a, GBase 8s, GBase 8c

Dec2023 (11.49), December 2023

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

BSD

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

C++

Scala

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

Standard with numerous extensions

yes

SQL 2003 with some extensions

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

JDBC
ODBC

.NET Client API

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding via remote tables

horizontal partitioning

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

yes

fine grained access rights according to SQL-standard

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