DBMS > GeoMesa vs. Graph Engine vs. Graphite vs. IRONdb

System Properties Comparison GeoMesa vs. Graph Engine vs. Graphite vs. IRONdb

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Primary database model

Spatial DBMS

Graph DBMS
Key-value store

Time Series DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

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

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Website

www.geomesa.org

www.graphengine.io

github.com/graphite-project/graphite-web

www.circonus.com/solutions/time-series-database/

Technical documentation

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

www.graphengine.io/docs/manual

graphite.readthedocs.io

docs.circonus.com/irondb/category/getting-started

Developer

CCRi and others

Microsoft

Chris Davis

Circonus LLC.

Initial release

2014

2010

2006

2017

Current release

5.0.0, May 2024

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

MIT License

Open Source

Apache 2.0

commercial

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

Scala

.NET and C

Python

C and C++

Server operating systems

.NET

Linux
Unix

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

yes

text, numeric, histograms

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

RESTful HTTP API

HTTP API
Sockets

HTTP API

Supported programming languages

C#
C++
F#
Visual Basic

JavaScript (Node.js)
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Server-side scripts

Stored procedures

yes

yes, in Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

horizontal partitioning

none

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

none

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

none

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

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.

depending on storage layer

yes

User concepts

Access control

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

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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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