DBMS > Apache IoTDB vs. ClickHouse vs. Firebird vs. VoltDB

System Properties Comparison Apache IoTDB vs. ClickHouse vs. Firebird vs. VoltDB

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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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

Technical documentation

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

clickhouse.com/docs

www.firebirdsql.org/en/reference-manuals

docs.voltdb.com

Developer

Apache Software Foundation

Clickhouse Inc.

Firebird Foundation

VoltDB Inc.

Initial release

2018

2016

2000

As fork of Borland's InterBase

2010

Current release

1.1.0, April 2023

v24.4.1.2088-stable, May 2024

5.0.0, January 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Initial Developer's Public License

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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.

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Implementation language

Java

C++

C and C++

Java, C++

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

yes

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

Close to ANSI SQL (SQL/JSON + extensions)

yes

only a subset of SQL 99

APIs and other access methods

JDBC
Native API

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

PSQL

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

key based and custom

none

Sharding

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

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

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

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Users with fine-grained authorization concept

Users and roles with access to stored procedures

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