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

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

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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.

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

github.com/dgraph-io/badger

db.apache.org/derby

spark.apache.org/sql

vitess.io

Technical documentation

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

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

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

vitess.io/docs

Developer

DGraph Labs

Apache Software Foundation

The Linux Foundation, PlanetScale

Initial release

2017

1997

2014

2013

Current release

10.17.1.0, November 2023

3.5.0 ( 2.13), September 2023

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

Apache Version 2.0, commercial licenses available

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

All OS with a Java VM

Linux
OS X
Windows

Docker
Linux
macOS

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC

JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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