DBMS > IRONdb vs. Microsoft SQL Server vs. PouchDB vs. RDF4J vs. Splice Machine

System Properties Comparison IRONdb vs. Microsoft SQL Server vs. PouchDB vs. RDF4J vs. Splice Machine

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

JavaScript DBMS with an API inspired by CouchDB

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

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

pouchdb.com

rdf4j.org

splicemachine.com

Technical documentation

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

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

pouchdb.com/guides

rdf4j.org/documentation

splicemachine.com/how-it-works

Developer

Circonus LLC.

Microsoft

Apache Software Foundation

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Splice Machine

Initial release

2017

1989

2012

2004

2014

Current release

V0.10.20, January 2018

SQL Server 2022, November 2022

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C++

JavaScript

Java

Server operating systems

Linux

Linux
Windows

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

Linux
OS X
Unix
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

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

Secondary indexes

yes

via views

yes

SQL

Support of SQL

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

yes

APIs and other access methods

HTTP API

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

HTTP REST

only for PouchDB Server
JavaScript API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
Native Spark Datasource
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

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

JavaScript

Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes, in Lua

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

View functions in JavaScript

yes

Java

Triggers

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

Sharding

with a proxy-based framework, named couchdb-lounge

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

in-memory storage is supported as well

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

Access rights for users, groups and roles according to SQL-standard

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