DBMS > Amazon DocumentDB vs. Google Cloud Datastore vs. Oracle NoSQL vs. RDF4J

System Properties Comparison Amazon DocumentDB vs. Google Cloud Datastore vs. Oracle NoSQL vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

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

Primary database model

Document store

Document store
Key-value store
Relational DBMS

RDF store

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

aws.amazon.com/documentdb

cloud.google.com/datastore

www.oracle.com/database/nosql/technologies/nosql

rdf4j.org

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/datastore/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

rdf4j.org/documentation

Developer

Google

Oracle

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

Initial release

2019

2008

2011

2004

Current release

24.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Java

Server operating systems

hosted

Linux
Solaris SPARC/x86

Linux
OS X
Unix
Windows

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

yes, details here

optional

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 DML and DDL statements

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

C
C#
Go
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication using Paxos

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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.

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic single-document operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

configurable

ACID within a storage node (=shard)

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.

yes

off heap cache

User concepts

Access control

Access rights for users and roles

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

Access rights for users and roles

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