DBMS > EsgynDB vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. NSDb vs. WakandaDB

System Properties Comparison EsgynDB vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. NSDb vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fast-speed and highly-scalable Graph DBMS

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph 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
	#41	Time Series DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.esgyn.cn

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/cosmos-db

nsdb.io

wakanda.github.io

Technical documentation

hugegraph.apache.org/docs

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

wakanda.github.io/doc

Developer

Esgyn

Baidu

Microsoft

Wakanda SAS

Initial release

2015

2018

2014

2017

2012

Current release

0.9

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended 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++, Java

Java

Java, Scala

C++, JavaScript

Server operating systems

Linux

Linux
macOS
Unix

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

also supports composite index and range index

yes

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

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

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Groovy
Java
Python

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

Java
Scala

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

JavaScript

yes

Triggers

JavaScript

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

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

yes

Implicit feature of the cloud service

none

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

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

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

Using Apache Lucene

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

fine grained access rights according to SQL-standard

Users, roles and permissions

Access rights can be defined down to the item level

yes

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