DBMS > CouchDB vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Table Storage vs. SQLite

System Properties Comparison CouchDB vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Table Storage vs. SQLite

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A fast-speed and highly-scalable Graph DBMS

TimeSeries DBMS for AIoT and BigData

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

couchdb.apache.org

github.com/hugegraph
hugegraph.apache.org

machbase.com

azure.microsoft.com/en-us/services/storage/tables

www.sqlite.org

Technical documentation

docs.couchdb.org/en/stable

hugegraph.apache.org/docs

machbase.com/dbms

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Baidu

Machbase

Microsoft

Dwayne Richard Hipp

Initial release

2005

2018

2013

2012

2000

Current release

3.3.3, December 2023

0.9

V8.0, August 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

free test version available

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Unix

Linux
macOS
Windows

hosted

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

via views

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Groovy
Java
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

View functions in JavaScript

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

atomic operations within a single document possible

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

via file-system locks

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

yes

User concepts

Access control

Access rights for users can be defined per database

Users, roles and permissions

simple password-based access control

Access rights based on private key authentication or shared access signatures

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