DBMS > Badger vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. OceanBase

System Properties Comparison Badger vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. OceanBase

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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.

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

A distributed, high available RDBMS compatible with Oracle and MySQL

Primary database model

Key-value store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Wide column store

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.66
Rank	#147	Overall
	#68	Relational DBMS

Website

github.com/dgraph-io/badger

machbase.com

azure.microsoft.com/services/cosmos-db

en.oceanbase.com

Technical documentation

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

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

en.oceanbase.com/docs/oceanbase-database

Developer

DGraph Labs

Machbase

Microsoft

OceanBase

previously Alibaba and Ant Group

Initial release

2017

2013

2014

2010

Current release

V8.0, August 2023

4.3.0, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free test version available

commercial

Open Source

Commercial license available

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Server-side scripts

Stored procedures

JavaScript

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

Implicit feature of the cloud service

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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.

yes

volatile and lookup table

User concepts

Access control

simple password-based access control

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

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