DBMS > HBase vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Cosmos DB

System Properties Comparison HBase vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

A fast-speed and highly-scalable Graph DBMS

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Wide column store

Graph DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

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

Website

hbase.apache.org

github.com/hugegraph
hugegraph.apache.org

machbase.com

azure.microsoft.com/services/cosmos-db

Technical documentation

hbase.apache.org/book.html

hugegraph.apache.org/docs

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Baidu

Machbase

Microsoft

Initial release

2008

2018

2013

2014

Current release

2.3.4, January 2021

0.9

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

free test version available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
macOS
Unix

Linux
macOS
Windows

hosted

Data scheme

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

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

also supports composite index and range index

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

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

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

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

Groovy
Java
Python

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

asynchronous Gremlin script jobs

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

Implicit feature of the cloud service

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users, roles and permissions

simple password-based access control

Access rights can be defined down to the item level

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