DBMS > Apache Druid vs. BaseX vs. Google Cloud Datastore vs. Quasardb vs. RDF4J

System Properties Comparison Apache Druid vs. BaseX vs. Google Cloud Datastore vs. Quasardb vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Distributed, high-performance timeseries database

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

Primary database model

Relational DBMS
Time Series DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	1.56
Rank	#141	Overall
	#4	Native XML DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

druid.apache.org

basex.org

cloud.google.com/datastore

quasar.ai

rdf4j.org

Technical documentation

druid.apache.org/docs/latest/design

docs.basex.org

cloud.google.com/datastore/docs

doc.quasar.ai/master

rdf4j.org/documentation

Developer

Apache Software Foundation and contributors

BaseX GmbH

Google

quasardb

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

Initial release

2012

2007

2008

2009

2004

Current release

30.0.0, June 2024

11.2, August 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

BSD license

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Java

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

hosted

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

XQuery supports types

yes, details here

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL for querying

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Java
PHP
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Triggers

yes

via events

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Multi-source replication using Paxos

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

multiple readers, single writer

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 LevelDB

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

Transient mode

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Users with fine-grained authorization concept on 4 levels

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

Cryptographically strong user authentication and audit trail

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