DBMS > IRONdb vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SAP HANA vs. TerminusDB

System Properties Comparison IRONdb vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SAP HANA vs. TerminusDB

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

Microsofts flagship relational DBMS

Widely used in-process key-value store

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
RDF store

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

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

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

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

www.microsoft.com/en-us/sql-server

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

www.sap.com/products/hana.html

terminusdb.com

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

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

help.sap.com/hana

terminusdb.github.io/terminusdb/#

Developer

Circonus LLC.

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP

DataChemist Ltd.

Initial release

2017

1989

1994

2010

2018

Current release

V0.10.20, January 2018

SQL Server 2022, November 2022

18.1.40, May 2020

2.0 SPS07 (April 4, 2023), April 2023

11.0.0, January 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

commercial license available

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

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Implementation language

C and C++

C++

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

Prolog, Rust

Server operating systems

Linux

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Appliance or cloud-service

Linux

Data scheme

schema-free

yes

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

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

SQL-like query language (WOQL)

APIs and other access methods

HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

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

.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

JavaScript
Python

Server-side scripts

Stored procedures

yes, in Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

SQLScript, R

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

Source-replica replication

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

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 journaling

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

Role-based access control

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