DBMS > Apache Druid vs. ClickHouse vs. Geode vs. Machbase Neo

System Properties Comparison Apache Druid vs. ClickHouse vs. Geode vs. Machbase Neo

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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.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

TimeSeries DBMS for AIoT and BigData

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

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	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

clickhouse.com/docs

geode.apache.org/docs

machbase.com/dbms

Developer

Apache Software Foundation and contributors

Clickhouse Inc.

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Machbase

Initial release

2012

2016

2002

2013

Current release

29.0.1, April 2024

v24.4.1.2088-stable, May 2024

1.1, February 2017

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

free test version available

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.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

Java

C++

Java

Server operating systems

Linux
OS X
Unix

FreeBSD
Linux
macOS

All OS with a Java VM

the JDK (8 or later) is also required

Linux
macOS
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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (OQL)

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

Java Client API
Memcached protocol
RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

.Net
All JVM based languages
C++
Groovy
Java
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

key based and custom

Sharding

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.

Multi-source replication

selectable replication factor

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

Transaction concepts

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

yes, on a single node

Concurrency

Support for concurrent manipulation of data

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

volatile and lookup table

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.

Access rights per client and object definable

simple password-based access control

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