DBMS > Badger vs. Graph Engine vs. HugeGraph vs. RRDtool

System Properties Comparison Badger vs. Graph Engine vs. HugeGraph vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A fast-speed and highly-scalable Graph DBMS

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

Key-value store

Graph DBMS
Key-value store

Graph DBMS

Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

github.com/dgraph-io/badger

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

oss.oetiker.ch/rrdtool

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

oss.oetiker.ch/rrdtool/doc

Developer

DGraph Labs

Microsoft

Baidu

Tobias Oetiker

Initial release

2017

2010

2018

1999

Current release

0.9

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

MIT License

Open Source

Apache Version 2.0

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

.NET and C

Java

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

.NET

Linux
macOS
Unix

HP-UX
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

in-process shared library
Pipes

Supported programming languages

C#
C++
F#
Visual Basic

Groovy
Java
Python

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

yes

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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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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