DBMS > Apache IoTDB vs. Firebird vs. GeoMesa vs. Memcached vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. Firebird vs. GeoMesa vs. Memcached vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Firebird is an open source RDBMS forked from Borland's InterBase

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

In-memory key-value store, originally intended for caching

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.firebirdsql.org/en/reference-manuals

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

github.com/memcached/memcached/wiki

docs.timescale.com

Developer

Apache Software Foundation

Firebird Foundation

CCRi and others

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Timescale

Initial release

2018

2000

As fork of Borland's InterBase

2014

2003

2017

Current release

1.1.0, April 2023

5.0.0, January 2024

5.0.0, May 2024

1.6.27, May 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Initial Developer's Public License

Open Source

Apache License 2.0

Open Source

BSD license

Open Source

Apache 2.0

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

Java

C and C++

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
Native API

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

Proprietary protocol

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

PSQL

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

depending on storage layer

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

depending on storage layer

none

Repcached, a Memcached patch, provides this functionallity

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

depending on storage layer

Immediate Consistency

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

Features a multi-generational MVCC architecture, readers do not block writers

yes

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

User concepts

Access control

yes

Users with fine-grained authorization concept

yes

depending on the DBMS used for storage

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

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