DBMS > ClickHouse vs. GigaSpaces vs. RocksDB

System Properties Comparison ClickHouse vs. GigaSpaces vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Column-oriented Relational DBMS powering Yandex

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Secondary database models

Time Series DBMS

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Trend Chart

Score	14.97
Rank	#44	Overall
	#28	Relational DBMS

Score	1.59
Rank	#180	Overall
	#30	Document stores
	#8	Object oriented DBMS

Score	4.27
Rank	#92	Overall
	#15	Key-value stores

Website

clickhouse.tech

www.gigaspaces.com

rocksdb.org

Technical documentation

clickhouse.tech/docs

docs.gigaspaces.com/latest/landing.html

github.com/facebook/rocksdb/wiki

Developer

Clickhouse Inc.

Gigaspaces Technologies

Facebook, Inc.

Initial release

2016

2000

2013

Current release

v23.4.1.1943-stable, April 2023

15.5, September 2020

7.10.2, March 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net

C++

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

schema-free

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.

XML can be used for describing objects metadata

Secondary indexes

yes, when using the MergeTree engine

yes

SQL

Support of SQL

Close to ANSI SQL

SQL-99 for query and DML statements

APIs and other access methods

HTTP REST
JDBC
ODBC
Proprietary protocol

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

C++ API
Java API

Supported programming languages

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
Scala

3rd party library

.Net
C++
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

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

User concepts

Access control

Access rights for users and roles

Role-based access control

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