DBMS > Apache Druid vs. ClickHouse vs. IBM Db2 Event Store vs. Quasardb

System Properties Comparison Apache Druid vs. ClickHouse vs. IBM Db2 Event Store vs. Quasardb

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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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Distributed Event Store optimized for Internet of Things use cases

Distributed, high-performance timeseries database

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Secondary database models

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	16.34
Rank	#38	Overall
	#23	Relational DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

druid.apache.org

clickhouse.com

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

quasar.ai

Technical documentation

druid.apache.org/docs/latest/design

clickhouse.com/docs

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

doc.quasar.ai/master

Developer

Apache Software Foundation and contributors

Clickhouse Inc.

IBM

quasardb

Initial release

2012

2016

2017

2009

Current release

29.0.1, April 2024

v24.4.1.2088-stable, May 2024

2.0

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

commercial

free developer edition available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
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DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

Java

C++

C and C++

C++

Server operating systems

Linux
OS X
Unix

FreeBSD
Linux
macOS

Linux

Linux, macOS, Windows for the developer addition

BSD
Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL for querying

Close to ANSI SQL (SQL/JSON + extensions)

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

key based and custom

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Active-active shard replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

yes

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

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

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 and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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