DBMS > Badger vs. OrigoDB vs. YottaDB

System Properties Comparison Badger vs. OrigoDB vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

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

A fully ACID in-memory object graph database

A fast and solid embedded Key-value store

Primary database model

Key-value store

Document store
Object oriented DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#19	Object oriented DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

github.com/dgraph-io/badger

origodb.com

yottadb.com

Technical documentation

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

origodb.com/docs

yottadb.com/resources/documentation

Developer

DGraph Labs

Robert Friberg et al

YottaDB, LLC

Initial release

2017

2009

under the name LiveDB

2001

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Docker
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

by using the Octo plugin

APIs and other access methods

.NET Client API
HTTP API
LINQ

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

.Net

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Role based authorization

Users and groups based on OS-security mechanisms

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