DBMS > Apache Drill vs. DataFS vs. H2GIS vs. Realm

System Properties Comparison Apache Drill vs. DataFS vs. H2GIS vs. Realm

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Spatial extension of H2

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Document store
Relational DBMS

Object oriented DBMS

Spatial DBMS

Document store

Secondary database models

Graph DBMS

Relational DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Website

Technical documentation

drill.apache.org/docs

dev.mobiland.com/Overview.xsp

www.h2gis.org/docs/home

realm.io/docs

Developer

Apache Software Foundation

Mobiland AG

CNRS

Realm, acquired by MongoDB in May 2019

Initial release

2012

2018

2013

2014

Current release

1.20.3, January 2023

1.1.263, October 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

LGPL 3.0

Open Source

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

Server operating systems

Linux
OS X
Windows

Windows

Android
Backend: server-less
iOS
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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 SELECT statement is SQL:2003 compliant

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

Supported programming languages

C++

.Net
C
C#
C++
VB.Net

Java

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

yes

based on H2

runs within the applications so server-side scripts are unnecessary

Triggers

no, except callback-events from server when changes happened

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

In-Memory realm

User concepts

Access control

Depending on the underlying data source

Windows-Profile

yes

based on H2

yes

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More resources

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Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
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30 January 2019, businesswire.com

Apache Drill Adds New Data Formats
28 March 2022, iProgrammer

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

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