DBMS > Apache Druid vs. DataFS vs. EsgynDB vs. Oracle Berkeley DB vs. RocksDB

System Properties Comparison Apache Druid vs. DataFS vs. EsgynDB vs. Oracle Berkeley DB vs. RocksDB

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

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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Widely used in-process key-value store

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS
Time Series DBMS

Object oriented DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Secondary database models

Graph 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	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

druid.apache.org

newdatabase.com

www.esgyn.cn

www.oracle.com/database/technologies/related/berkeleydb.html

rocksdb.org

Technical documentation

druid.apache.org/docs/latest/design

dev.mobiland.com/Overview.xsp

docs.oracle.com/cd/E17076_05/html/index.html

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation and contributors

Mobiland AG

Esgyn

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Facebook, Inc.

Initial release

2012

2018

2015

1994

2013

Current release

29.0.1, April 2024

1.1.263, October 2022

18.1.40, May 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

commercial license available

Open Source

BSD

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

C++, Java

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
OS X
Unix

Windows

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-less columns are supported

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

yes

schema-free

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
JDBC
ODBC

C++ API
Java API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/ADO.Net

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

no, except callback-events from server when changes happened

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Proprietary Sharding system

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication between multi datacenters

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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.

yes

User concepts

Access control

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

Windows-Profile

fine grained access rights according to SQL-standard

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