DBMS > Adabas vs. Apache IoTDB vs. IBM Db2 Event Store vs. Lovefield vs. Oracle Berkeley DB

System Properties Comparison Adabas vs. Apache IoTDB vs. IBM Db2 Event Store vs. Lovefield vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

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

Distributed Event Store optimized for Internet of Things use cases

Embeddable relational database for web apps written in pure JavaScript

Widely used in-process key-value store

Primary database model

Multivalue DBMS

Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

iotdb.apache.org

www.ibm.com/products/db2-event-store

google.github.io/lovefield

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

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

www.ibm.com/docs/en/db2-event-store

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Software AG

Apache Software Foundation

IBM

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

1971

2018

2017

2014

1994

Current release

1.1.0, April 2023

2.0

2.1.12, February 2017

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

commercial license available

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++

JavaScript

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

All OS with a Java VM (>= 1.8)

Linux

Linux, macOS, Windows for the developer addition

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

yes

through the embedded Spark runtime

SQL-like query language

via JavaScript builder pattern

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

JDBC
Native API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Natural

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

in Natural

yes

Triggers

yes

Using read-only observers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

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

Active-active shard replication

none

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

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual 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

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

yes

fine grained access rights according to SQL-standard

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