DBMS > Badger vs. Datomic vs. MySQL vs. OrientDB vs. Quasardb

System Properties Comparison Badger vs. Datomic vs. MySQL vs. OrientDB vs. Quasardb

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

Widely used open source RDBMS

Multi-model DBMS (Document, Graph, Key/Value)

Distributed, high-performance timeseries database

Primary database model

Key-value store

Relational DBMS

Key/Value like access via memcached API

Document store
Graph DBMS
Key-value store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Website

github.com/dgraph-io/badger

Technical documentation

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

docs.datomic.com

dev.mysql.com/doc

www.orientdb.com/docs/last/index.html

doc.quasar.ai/master

Developer

DGraph Labs

Cognitect

Oracle

since 2010, originally MySQL AB, then Sun

OrientDB LTD; CallidusCloud; SAP

quasardb

Initial release

2017

2012

1995

2010

2009

Current release

1.0.6735, June 2023

8.3.0, January 2024

3.2.29, March 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C and C++

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language, no joins

SQL-like query language

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

HTTP API

Supported programming languages

Clojure
Java

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

proprietary syntax

Java, Javascript

Triggers

By using transaction functions

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

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

Multi-source replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationship in graphs

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

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

yes

by using LevelDB

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

Transient mode

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles; record level security configurable

Cryptographically strong user authentication and audit trail

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