DBMS > EsgynDB vs. HugeGraph vs. LMDB vs. RRDtool

System Properties Comparison EsgynDB vs. HugeGraph vs. LMDB vs. RRDtool

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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

A high performant, light-weight, embedded key-value database library

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

www.esgyn.cn

github.com/hugegraph
hugegraph.apache.org

www.symas.com/symas-embedded-database-lmdb

oss.oetiker.ch/rrdtool

Technical documentation

hugegraph.apache.org/docs

www.lmdb.tech/doc

oss.oetiker.ch/rrdtool/doc

Developer

Esgyn

Baidu

Symas

Tobias Oetiker

Initial release

2015

2018

2011

1999

Current release

0.9

0.9.32, January 2024

1.8.0, 2022

License

Commercial or Open Source

commercial

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

C++, Java

Java

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

Server operating systems

Linux

Linux
macOS
Unix

Linux
Unix
Windows

HP-UX
Linux

Data scheme

yes

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

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Groovy
Java
Python

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

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

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate 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

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

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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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