DBMS > Badger vs. dBASE vs. GBase vs. HEAVY.AI vs. Spark SQL

System Properties Comparison Badger vs. dBASE vs. GBase vs. HEAVY.AI vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

An analytical database for business intelligence with large customers in China.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Primary database model

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	11.18
Rank	#44	Overall
	#28	Relational DBMS

Score	1.04
Rank	#190	Overall
	#88	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dgraph-io/badger

www.dbase.com

www.gbase.cn

github.com/heavyai/heavydb
www.heavy.ai

spark.apache.org/sql

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.dbase.com/support/knowledgebase

docs.heavy.ai

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

Developer

DGraph Labs

Asthon Tate

HEAVY.AI, Inc.

Apache Software Foundation

Initial release

2017

1979

2016

2014

Current release

dBASE 2019, 2019

GBase 8a

5.10, January 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

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++ and CUDA

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

DOS

dBase Classic
Windows

dBase Pro

Linux

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

none

The IDE can access other DBMS or ODBC-sources.

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

Supported programming languages

dBase proprietary IDE

All languages supporting JDBC/ODBC/Thrift
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

DB-Engines Ranking coverage expanded to 169 database management systems
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