DBMS > Dragonfly vs. GeoMesa vs. RRDtool

System Properties Comparison Dragonfly vs. GeoMesa vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Spatial DBMS

Time Series DBMS

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.geomesa.org

oss.oetiker.ch/rrdtool

Technical documentation

www.dragonflydb.io/docs

www.geomesa.org/documentation/stable/user/index.html

oss.oetiker.ch/rrdtool/doc

Developer

DragonflyDB team and community contributors

CCRi and others

Tobias Oetiker

Initial release

2023

2014

1999

Current release

1.0, March 2023

4.0.5, February 2024

1.8.0, 2022

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache License 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++

Scala

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

Server operating systems

Linux

HP-UX
Linux

Data scheme

scheme-free

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

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

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

in-process shared library
Pipes

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
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

Lua

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

depending on storage layer

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

depending on storage layer

yes

User concepts

Access control

Password-based authentication

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

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

show all

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