DBMS > Altibase vs. Apache IoTDB vs. EJDB vs. IBM Db2 vs. JaguarDB

System Properties Comparison Altibase vs. Apache IoTDB vs. EJDB vs. IBM Db2 vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Common in IBM host environments, 2 different versions for host and Windows/Linux

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Key-value store
Vector DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

altibase.com

iotdb.apache.org

github.com/Softmotions/ejdb

www.ibm.com/products/db2

www.jaguardb.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

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

github.com/Softmotions/ejdb/blob/master/README.md

www.ibm.com/docs/en/db2

www.jaguardb.com/support.html

Developer

Altibase

Apache Software Foundation

Softmotions

IBM

DataJaguar, Inc.

Initial release

1999

2018

2012

1983

host version

2015

Current release

v7.3, 2023, August 2023

1.1.0, April 2023

12.1, October 2016

3.3 July 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2.0

Open Source

GPLv2

commercial

free version is available

Open Source

GPL V3.0

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

C++

Java

C and C++

C++

the server part. Clients available in other languages

Server operating systems

AIX
HP-UX
Linux

All OS with a Java VM (>= 1.8)

server-less

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

ANSI SQL-92

SQL-like query language

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
Native API

in-process shared library

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC

Supported programming languages

C
C++
Java

C
C#
C++
Go
Java
Python
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Sharding

only with Windows/Unix/Linux Version

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes

with separate tools (MQ, InfoSphere)

Multi-source 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

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

rights management via user accounts

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