DBMS > Apache Druid vs. Cassandra vs. Firebird vs. GridGain

System Properties Comparison Apache Druid vs. Cassandra vs. Firebird vs. GridGain

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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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Firebird is an open source RDBMS forked from Borland's InterBase

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Relational DBMS
Time Series DBMS

Wide column store

Relational DBMS

Key-value store
Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

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	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

cassandra.apache.org/doc/latest

www.firebirdsql.org/en/reference-manuals

www.gridgain.com/docs/index.html

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top level project, originally developped by Facebook

Firebird Foundation

GridGain Systems, Inc.

Initial release

2012

2008

2000

As fork of Borland's InterBase

2007

Current release

29.0.1, April 2024

4.1.3, July 2023

5.0.0, January 2024

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache version 2

Open Source

Initial Developer's Public License

commercial

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C and C++

Java, C++, .Net

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
OS X
Solaris
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.

yes

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL for querying

SQL-like SELECT, DML and DDL statements (CQL)

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

PSQL

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

Access rights for users can be defined per object

Users with fine-grained authorization concept

Security Hooks for custom implementations

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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