DBMS > Drizzle vs. EJDB vs. HEAVY.AI vs. WakandaDB

System Properties Comparison Drizzle vs. EJDB vs. HEAVY.AI vs. WakandaDB

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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

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

github.com/Softmotions/ejdb

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

wakanda.github.io

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.heavy.ai

wakanda.github.io/doc

Developer

Drizzle project, originally started by Brian Aker

Softmotions

HEAVY.AI, Inc.

Wakanda SAS

Initial release

2008

2012

2016

2012

Current release

7.2.4, September 2012

5.10, January 2022

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition 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

C++

C++ and CUDA

C++, JavaScript

Server operating systems

FreeBSD
Linux
OS X

server-less

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

JDBC

in-process shared library

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Server-side scripts

Stored procedures

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

yes

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More resources

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