DBMS > Hazelcast vs. IRONdb vs. RDF4J vs. Trino

System Properties Comparison Hazelcast vs. IRONdb vs. RDF4J vs. Trino

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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 widely adopted in-memory data grid

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

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

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

hazelcast.com

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

rdf4j.org

trino.io

Technical documentation

hazelcast.org/imdg/docs

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

rdf4j.org/documentation

trino.io/broadcast
trino.io/docs/current

Developer

Hazelcast

Circonus LLC.

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

Trino Software Foundation

Initial release

2008

2017

2004

2012

2020 rebranded from PrestoSQL

Current release

5.3.6, November 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2.0

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++

Java

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Linux
macOS

for devlopment

Data scheme

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

the object must implement a serialization strategy

Secondary indexes

yes

depending on connected data-source

SQL

Support of SQL

SQL-like query language

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

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Java
PHP
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes, in Lua

yes

yes, depending on connected data-source

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

configurable replication factor, datacenter aware

none

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate consistency per node, eventual consistency across nodes

depending on connected data-source

Foreign keys

Referential integrity

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Isolation support depends on the API used

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

depending on connected data-source

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

SQL standard access control

More information provided by the system vendor

News

23 is a go, keeping pace with Java
17 September 2024

65: Performance boosts
12 September 2024

64: Control with Open Policy Agent OPA
22 August 2024

63: Querying with JS
1 August 2024

62: A lakehouse that simply works at Prezi
11 July 2024

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