DBMS > Apache Druid vs. FatDB vs. OushuDB vs. Spark SQL

System Properties Comparison Apache Druid vs. FatDB vs. OushuDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS
Time Series DBMS

Document store
Key-value store

Relational DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

druid.apache.org

www.oushu.com/product/oushuDB

spark.apache.org/sql

Technical documentation

druid.apache.org/docs/latest/design

www.oushu.com/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation and contributors

FatCloud

Oushu

Apache Software Foundation

Initial release

2012

2014

Current release

29.0.1, April 2024

4.0.1, August 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Scala

Server operating systems

Linux
OS X
Unix

Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

Typing

predefined data types such as float or date

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 for querying

Via inetgration in SQL Server

Full-featured ANSI SQL support

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C++

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

Can implement custom security layer via applications

Kerberos, SSL and role based access

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