DBMS > Dgraph vs. EsgynDB vs. H2 vs. RRDtool

System Properties Comparison Dgraph vs. EsgynDB vs. H2 vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Graph DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

dgraph.io

www.esgyn.cn

www.h2database.com

oss.oetiker.ch/rrdtool

Technical documentation

dgraph.io/docs

www.h2database.com/html/main.html

oss.oetiker.ch/rrdtool/doc

Developer

Dgraph Labs, Inc.

Esgyn

Thomas Mueller

Tobias Oetiker

Initial release

2016

2015

2005

1999

Current release

2.2.220, July 2023

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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

C++, Java

Java

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

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

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

SQL

Support of SQL

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

in-process shared library
Pipes

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Java

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

Java Stored Procedures

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

With clustering: 2 database servers on different computers operate on identical copies of a database

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

by using the rrdcached daemon

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

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

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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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