DBMS > Apache Druid vs. Faircom DB vs. IBM Cloudant vs. IBM Db2 Event Store

System Properties Comparison Apache Druid vs. Faircom DB vs. IBM Cloudant vs. IBM Db2 Event Store

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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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Database as a Service offering based on Apache CouchDB

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Document store

Event Store
Time Series DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

druid.apache.org

www.faircom.com/products/faircom-db

www.ibm.com/products/cloudant

www.ibm.com/products/db2-event-store

Technical documentation

druid.apache.org/docs/latest/design

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.ibm.com/docs/Cloudant

www.ibm.com/docs/en/db2-event-store

Developer

Apache Software Foundation and contributors

FairCom Corporation

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Initial release

2012

1979

2010

2017

Current release

29.0.1, April 2024

V12, November 2020

2.0

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Restricted, free version available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

ANSI C, C++

Erlang

C and C++

Server operating systems

Linux
OS X
Unix

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux

Linux, macOS, Windows for the developer addition

Data scheme

yes

schema-less columns are supported

schema free, schema optional, schema required, partial schema,

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

View functions (Map-Reduce) in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication
Source-replica replication

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

No - written data is immutable

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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