DBMS > IRONdb vs. Oracle Rdb vs. Splice Machine vs. TigerGraph

System Properties Comparison IRONdb vs. Oracle Rdb vs. Splice Machine vs. TigerGraph

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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

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS

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

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

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

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

splicemachine.com

www.tigergraph.com

Technical documentation

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

www.oracle.com/database/technologies/related/rdb-doc.html

splicemachine.com/how-it-works

docs.tigergraph.com

Developer

Circonus LLC.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Splice Machine

Initial release

2017

1984

2014

2017

Current release

V0.10.20, January 2018

7.4.1.1, 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

commercial

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 and C++

Java

C++

Server operating systems

Linux

HP Open VMS

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, 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.

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL-like query language (GSQL)

APIs and other access methods

HTTP API

JDBC
Native Spark Datasource
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

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

C++
Java

Server-side scripts

Stored procedures

yes, in Lua

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

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

Relationships in graphs

Transaction concepts

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

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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

Role-based access control

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More resources

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