DBMS > Datomic vs. Google Cloud Bigtable vs. Kingbase vs. MariaDB vs. SwayDB

System Properties Comparison Datomic vs. Google Cloud Bigtable vs. Kingbase vs. MariaDB vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

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

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

www.datomic.com

cloud.google.com/bigtable

www.kingbase.com.cn

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

swaydb.simer.au

Technical documentation

docs.datomic.com

cloud.google.com/bigtable/docs

mariadb.com/kb/en/library

Developer

Cognitect

Google

BeiJing KINGBASE Information technologies inc.

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Simer Plaha

Initial release

2012

2015

1999

2009

Fork of MySQL, which was first released in 1995

2018

Current release

1.0.6735, June 2023

V8.0, August 2021

11.3.2, February 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

GPL version 2, commercial enterprise subscription available

Open Source

GNU Affero GPL V3.0

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

Java, Clojure

C and Java

C and C++

Scala

Server operating systems

All OS with a Java VM

hosted

Linux
Windows

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Data scheme

yes

schema-free

yes

Dynamic columns are supported

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

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

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
Java

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

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

yes

PL/SQL compatibility added with version 10.3

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

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

several options for horizontal partitioning and Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

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

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

ACID

not for MyISAM storage engine

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

not for in-memory storage engine

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

with MEMORY storage engine

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

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Market metrics

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Licensing and pricing models

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