DBMS > Badger vs. HEAVY.AI vs. MonetDB vs. WakandaDB

System Properties Comparison Badger vs. HEAVY.AI vs. MonetDB vs. WakandaDB

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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.

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

A relational database management system that stores data in columns

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Key-value store

Relational DBMS

Object oriented DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

github.com/dgraph-io/badger

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

www.monetdb.org

wakanda.github.io

Technical documentation

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

docs.heavy.ai

www.monetdb.org/Documentation

wakanda.github.io/doc

Developer

DGraph Labs

HEAVY.AI, Inc.

MonetDB BV

Wakanda SAS

Initial release

2017

2016

2004

2012

Current release

5.10, January 2022

Dec2023 (11.49), December 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla Public License 2.0

Open Source

AGPLv3, extended commercial license 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

C++ and CUDA

C++, JavaScript

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

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 2003 with some extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript

Server-side scripts

Stored procedures

yes, in SQL, C, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

none

Source-replica replication available in experimental status

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

fine grained access rights according to SQL-standard

yes

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