DBMS > Apache Druid vs. GridGain vs. Hazelcast vs. IBM Db2 warehouse vs. MySQL

System Properties Comparison Apache Druid vs. GridGain vs. Hazelcast vs. IBM Db2 warehouse vs. MySQL

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

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

A widely adopted in-memory data grid

Cloud-based data warehousing service

Widely used open source RDBMS

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial 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	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

druid.apache.org

www.gridgain.com

hazelcast.com

www.ibm.com/products/db2/warehouse

www.mysql.com

Technical documentation

druid.apache.org/docs/latest/design

www.gridgain.com/docs/index.html

hazelcast.org/imdg/docs

dev.mysql.com/doc

Developer

Apache Software Foundation and contributors

GridGain Systems, Inc.

Hazelcast

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2007

2008

2014

1995

Current release

29.0.1, April 2024

GridGain 8.5.1

5.3.6, November 2023

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Java, C++, .Net

Java

C and C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

yes

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

the object must implement a serialization strategy

Import/export of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

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

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

yes

Event Listeners, Executor Services

PL/SQL, SQL PL

yes

proprietary syntax

Triggers

yes (cache interceptors and events)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes (replicated cache)

yes

Replicated Map

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Security Hooks for custom implementations

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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