DBMS > AlaSQL vs. Google Cloud Datastore vs. Microsoft Azure SQL Database vs. RDF4J vs. Riak TS

System Properties Comparison AlaSQL vs. Google Cloud Datastore vs. Microsoft Azure SQL Database vs. RDF4J vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

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

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

alasql.org

cloud.google.com/datastore

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

rdf4j.org

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/datastore/docs

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

rdf4j.org/documentation

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Andrey Gershun & Mathias R. Wulff

Google

Microsoft

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

Open Source, formerly Basho Technologies

Initial release

2014

2008

2010

2004

2015

Current release

V12

3.0.0, September 2022

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

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

JavaScript

C++

Java

Erlang

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

schema-free

yes

RDF Schemas

schema-free

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

restricted

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL-like query language (GQL)

yes

yes, limited

APIs and other access methods

JavaScript API

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

HTTP API
Native Erlang Interface

Supported programming languages

JavaScript

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

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

using Google App Engine

Transact SQL

yes

Erlang

Triggers

yes

Callbacks using the Google Apps Engine

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes, with always 3 replicas available

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

links between datasets can be stored

Transaction concepts

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

yes

only for local storage and DOM-storage

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

by using IndexedDB, SQL.JS or proprietary FileStorage

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.

yes

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

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