DBMS > GBase vs. Microsoft Azure SQL Database vs. RocksDB vs. Spark SQL vs. Yaacomo

System Properties Comparison GBase vs. Microsoft Azure SQL Database vs. RocksDB vs. Spark SQL vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Database as a Service offering with high compatibility to Microsoft SQL Server

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.gbase.cn

azure.microsoft.com/en-us/products/azure-sql/database

rocksdb.org

spark.apache.org/sql

yaacomo.com

Technical documentation

docs.microsoft.com/en-us/azure/azure-sql

github.com/facebook/rocksdb/wiki

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

Developer

General Data Technology Co., Ltd.

Microsoft

Facebook, Inc.

Apache Software Foundation

Q2WEB GmbH

Initial release

2004

2010

2013

2014

2009

Current release

GBase 8a, GBase 8s, GBase 8c

V12

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 2.0

commercial

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, Python

C++

Scala

Server operating systems

Linux

hosted

Linux

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

ADO.NET
JDBC
ODBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with always 3 replicas available

yes

none

Source-replica replication

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

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

yes

fine grained access rights according to SQL-standard

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