DBMS > Badger vs. Datomic vs. Splice Machine vs. WakandaDB

System Properties Comparison Badger vs. Datomic vs. Splice Machine 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.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

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

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

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

github.com/dgraph-io/badger

www.datomic.com

splicemachine.com

wakanda.github.io

Technical documentation

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

docs.datomic.com

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

DGraph Labs

Cognitect

Splice Machine

Wakanda SAS

Initial release

2017

2012

2014

2012

Current release

1.0.6735, June 2023

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

Open Source

AGPL 3.0, commercial license available

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

Java, Clojure

Java

C++, JavaScript

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

APIs and other access methods

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

Clojure
Java

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

JavaScript

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Java

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

none

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)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

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

yes

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