DBMS > Apache Druid vs. HyperSQL vs. IBM Db2 Event Store

System Properties Comparison Apache Druid vs. HyperSQL 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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Event Store
Time Series DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	3.90
Rank	#85	Overall
	#46	Relational DBMS

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

druid.apache.org

hsqldb.org

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

Technical documentation

druid.apache.org/docs/latest/design

hsqldb.org/web/hsqlDocsFrame.html

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

Developer

Apache Software Foundation and contributors

IBM

Initial release

2012

2001

2017

Current release

29.0.1, April 2024

2.7.2, June 2023

2.0

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

based on BSD license

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

Linux, macOS, Windows for the developer addition

Data scheme

yes

schema-less columns are supported

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 for querying

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

Java, SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

No - written data is immutable

Durability

Support for making data persistent

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

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