DBMS > Cachelot.io vs. Google Cloud Datastore vs. Microsoft Azure SQL Database vs. PostGIS vs. RDF4J

System Properties Comparison Cachelot.io vs. Google Cloud Datastore vs. Microsoft Azure SQL Database vs. PostGIS vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

Database as a Service offering with high compatibility to Microsoft SQL Server

Spatial extension of PostgreSQL

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

cachelot.io

cloud.google.com/datastore

azure.microsoft.com/en-us/products/azure-sql/database

postgis.net

rdf4j.org

Technical documentation

cloud.google.com/datastore/docs

docs.microsoft.com/en-us/azure/azure-sql

postgis.net/documentation

rdf4j.org/documentation

Developer

Google

Microsoft

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

Initial release

2015

2008

2010

2005

2004

Current release

V12

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

GPL v2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

Memcached protocol

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

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

Transact SQL

user defined functions

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

based on PostgreSQL

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes, with always 3 replicas available

yes

based on PostgreSQL

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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)

fine grained access rights according to SQL-standard

yes

based on PostgreSQL

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

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