DBMS > Cachelot.io vs. Google Cloud Datastore vs. IRONdb vs. RDF4J vs. TypeDB

System Properties Comparison Cachelot.io vs. Google Cloud Datastore vs. IRONdb vs. RDF4J vs. TypeDB

Editorial information provided by DB-Engines

Name

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

Description

In-memory caching system

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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.

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

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

Score	0.00
Rank	#396	Overall
	#64	Key-value stores

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.81
Rank	#217	Overall
	#19	Graph DBMS
	#99	Relational DBMS

Website

cachelot.io

cloud.google.com/datastore

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

rdf4j.org

typedb.com

Technical documentation

cloud.google.com/datastore/docs

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

rdf4j.org/documentation

typedb.com/docs

Developer

Google

Circonus LLC.

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

Vaticle

Initial release

2015

2008

2017

2004

2016

Current release

V0.10.20, January 2018

2.26.3, January 2024

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL Version 3, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C and C++

Java

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes, details here

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 (GQL)

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

APIs and other access methods

Memcached protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Java
PHP
Python

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

using Google App Engine

yes, in Lua

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

none

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

configurable replication factor, datacenter aware

none

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

substituted by the relationship feature

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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.

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

TypeDB is a polymorphic database with a conceptual data model, a strong subtyping...
» more

Competitive advantages

TypeDB provides a new level of expressivity, extensibility, interoperability, and...
» more

Typical application scenarios

Life sciences : TypeDB makes working with biological data much easier and accelerates...
» more

Licensing and pricing models

Apache f or language drivers, and AGPL and Commercial for the database server. The...
» more

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