DBMS > Badger vs. DataFS vs. HEAVY.AI vs. Splice Machine

System Properties Comparison Badger vs. DataFS vs. HEAVY.AI vs. Splice Machine

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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.

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

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

Primary database model

Key-value store

Object oriented DBMS

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

github.com/dgraph-io/badger

newdatabase.com

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

splicemachine.com

Technical documentation

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

dev.mobiland.com/Overview.xsp

docs.heavy.ai

splicemachine.com/how-it-works

Developer

DGraph Labs

Mobiland AG

HEAVY.AI, Inc.

Splice Machine

Initial release

2017

2018

2016

2014

Current release

1.1.263, October 2022

5.10, January 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

AGPL 3.0, 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

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Windows

Linux

Linux
OS X
Solaris
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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift
Vega

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

yes

Java

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

Sharding

Round robin

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

yes, multi-version concurrency control (MVCC)

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

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

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