DBMS > HugeGraph vs. NSDb

System Properties Comparison HugeGraph vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

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

Primary database model

Graph DBMS

Time Series DBMS

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

Score	0.39
Rank	#294	Overall
	#24	Graph DBMS

Score	0.00
Rank	#392	Overall
	#40	Time Series DBMS

Website

github.com/hugegraph

nsdb.io

Technical documentation

hugegraph.github.io/hugegraph-doc

nsdb.io/Architecture

Developer

Baidu

Initial release

2018

2017

Current release

0.9

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Scala

Server operating systems

Linux
macOS
Unix

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC
HTTP REST
WebSocket

Supported programming languages

Groovy
Java
Python

Java
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

yes

User concepts

Access control

Users, roles and permissions

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Recent citations in the news

NebulaGraph power growth with Series A funding round -
16 September 2022, Enterprise Times

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