DBMS > etcd vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. OrigoDB vs. Quasardb

System Properties Comparison etcd vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. OrigoDB vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

A fully ACID in-memory object graph database

Distributed, high-performance timeseries database

Primary database model

Key-value store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Object oriented DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	7.25
Rank	#54	Overall
	#5	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	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

etcd.io
github.com/etcd-io/etcd

machbase.com

azure.microsoft.com/services/cosmos-db

origodb.com

quasar.ai

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

origodb.com/docs

doc.quasar.ai/master

Developer

Machbase

Microsoft

Robert Friberg et al

quasardb

Initial release

2013

2014

2009

under the name LiveDB

2009

Current release

3.4, August 2019

V8.0, August 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free test version available

commercial

Open Source

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

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

FreeBSD
Linux
Windows

experimental

Linux
macOS
Windows

hosted

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

All properties auto-indexed by default

yes

with tags

SQL

Support of SQL

SQL-like query language

APIs and other access methods

gRPC
JSON over HTTP

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

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

.NET Client API
HTTP API
LINQ

HTTP API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

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

.Net

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

Server-side scripts

Stored procedures

JavaScript

yes

Triggers

yes, watching key changes

JavaScript

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

selectable replication factor

yes

Implicit feature of the cloud service

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Write ahead log

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

volatile and lookup table

yes

Transient mode

User concepts

Access control

simple password-based access control

Access rights can be defined down to the item level

Role based authorization

Cryptographically strong user authentication and audit trail

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