DBMS > HugeGraph vs. NSDb vs. OushuDB vs. RRDtool

System Properties Comparison HugeGraph vs. NSDb vs. OushuDB vs. RRDtool

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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

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

github.com/hugegraph
hugegraph.apache.org

nsdb.io

www.oushu.com/product/oushuDB

oss.oetiker.ch/rrdtool

Technical documentation

hugegraph.apache.org/docs

nsdb.io/Architecture

www.oushu.com/documentation

oss.oetiker.ch/rrdtool/doc

Developer

Baidu

Oushu

Tobias Oetiker

Initial release

2018

2017

1999

Current release

0.9

4.0.1, August 2020

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

GPL V2 and FLOSS

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Java, Scala

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux
macOS
Unix

Linux
macOS

Linux

HP-UX
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

also supports composite index and range index

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC
HTTP REST
WebSocket

JDBC
ODBC

in-process shared library
Pipes

Supported programming languages

Groovy
Java
Python

Java
Scala

C
C++

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

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

by using the rrdcached daemon

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

Users, roles and permissions

Kerberos, SSL and role based access

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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