DBMS > Badger vs. DataFS vs. Derby vs. Spark SQL

System Properties Comparison Badger vs. DataFS vs. Derby vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Primary database model

Key-value store

Object oriented DBMS

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dgraph-io/badger

newdatabase.com

db.apache.org/derby

spark.apache.org/sql

Technical documentation

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

dev.mobiland.com/Overview.xsp

db.apache.org/derby/manuals/index.html

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

Developer

DGraph Labs

Mobiland AG

Apache Software Foundation

Initial release

2017

2018

1997

2014

Current release

1.1.263, October 2022

10.17.1.0, November 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2

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

Java

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

yes

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica 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

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

Windows-Profile

fine grained access rights according to SQL-standard

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