DBMS > BaseX vs. Drizzle vs. Google Cloud Bigtable vs. ObjectBox vs. WakandaDB

System Properties Comparison BaseX vs. Drizzle vs. Google Cloud Bigtable vs. ObjectBox 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

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Extremely fast embedded database for small devices, IoT and Mobile

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

Primary database model

Native XML DBMS

Relational DBMS

Key-value store
Wide column store

Object oriented DBMS

Secondary database models

Time Series DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

basex.org

cloud.google.com/bigtable

objectbox.io

wakanda.github.io

Technical documentation

docs.basex.org

cloud.google.com/bigtable/docs

docs.objectbox.io

wakanda.github.io/doc

Developer

BaseX GmbH

Drizzle project, originally started by Brian Aker

Google

ObjectBox Limited

Wakanda SAS

Initial release

2007

2008

2015

2017

2012

Current release

10.7, August 2023

7.2.4, September 2012

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

BSD license

Open Source

GNU GPL

commercial

Open Source

Apache License 2.0

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

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Implementation language

Java

C++

C and C++

C++, JavaScript

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X

hosted

Android
iOS
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

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

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Proprietary native API

RESTful HTTP API

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C
C++
Java
PHP

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

JavaScript

Server-side scripts

Stored procedures

yes

Triggers

yes

via events

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

online/offline synchronization between client and server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

ACID

Atomic single-row operations

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.

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

More information provided by the system vendor

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