DBMS > jBASE vs. MySQL vs. Splice Machine vs. Teradata Aster vs. TigerGraph

System Properties Comparison jBASE vs. MySQL vs. Splice Machine vs. Teradata Aster vs. TigerGraph

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

A robust multi-value DBMS comprising development tools and middleware

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Platform for big data analytics on multistructured data sources and types

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Multivalue DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.mysql.com

splicemachine.com

www.tigergraph.com

Technical documentation

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

dev.mysql.com/doc

splicemachine.com/how-it-works

docs.tigergraph.com

Developer

Rocket Software (formerly Zumasys)

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Teradata

Initial release

1991

1995

2014

2005

2017

Current release

5.7

8.4.0, April 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java

C++

Server operating systems

AIX
Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

yes

Typing

predefined data types such as float or date

optional

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

Embedded SQL for jBASE in BASIC

yes

with proprietary extensions

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
OLE DB

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
Basic
Jabbascript
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C#
C++
Java
Python
R

C++
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

Java

R packages

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

SQL Map-Reduce Framework

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Relationships in graphs

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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.

yes

User concepts

Access control

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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

fine grained access rights according to SQL-standard

Role-based access control

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