DBMS > Amazon Neptune vs. Badger vs. Drizzle vs. Splice Machine vs. WakandaDB

System Properties Comparison Amazon Neptune vs. Badger vs. Drizzle vs. Splice Machine vs. WakandaDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

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

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

aws.amazon.com/neptune

github.com/dgraph-io/badger

splicemachine.com

wakanda.github.io

Technical documentation

aws.amazon.com/neptune/developer-resources

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

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Amazon

DGraph Labs

Drizzle project, originally started by Brian Aker

Splice Machine

Wakanda SAS

Initial release

2017

2008

2014

2012

Current release

7.2.4, September 2012

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GNU GPL

Open Source

AGPL 3.0, commercial license available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++, JavaScript

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X

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

with proprietary extensions

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C++
Java
PHP

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

JavaScript

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

none

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

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

yes

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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