DBMS > IRONdb vs. Oracle Berkeley DB vs. SAP HANA vs. SAP IQ vs. SAP SQL Anywhere

System Properties Comparison IRONdb vs. Oracle Berkeley DB vs. SAP HANA vs. SAP IQ vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

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

Description

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

Widely used in-process key-value store

In-memory, column based data store. Available as appliance or cloud service

Columnar RDBMS optimized for Big Data analytics

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

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

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

www.sap.com/products/hana.html

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

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

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

help.sap.com/hana

help.sap.com/docs/SAP_IQ

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Circonus LLC.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP

SAP, formerly Sybase

SAP

formerly Sybase

Initial release

2017

1994

2010

1994

1992

Current release

V0.10.20, January 2018

18.1.40, May 2020

2.0 SPS07 (April 4, 2023), April 2023

16.1 SPS04, April 2019

17, July 2015

License

Commercial or Open Source

commercial

Open Source

commercial license available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

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

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Appliance or cloud-service

AIX
HP-UX
Linux
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

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

.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

C
C#
C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

SQLScript, R

yes

yes, in C/C++, Java, .Net or Perl

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

yes

shared disk or shared nothing architectures with SAP IQ Multiplexer

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Source-replica replication

yes

SAP/Sybase Replication Server

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

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

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

yes

fine grained access rights according to SQL-standard

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