DBMS > ClickHouse vs. Datastax Enterprise vs. GigaSpaces vs. GridDB

System Properties Comparison ClickHouse vs. Datastax Enterprise vs. GigaSpaces vs. GridDB

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Editorial information provided by DB-Engines

Name

Description

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.

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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

Scalable in-memory time series database optimized for IoT and Big Data

Primary database model

Relational DBMS

Wide column store

Document store
Object oriented DBMS

Values are user defined objects

Time Series DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Graph DBMS
Search engine

Key-value store
Relational DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Website

clickhouse.com

www.datastax.com/products/datastax-enterprise

www.gigaspaces.com

griddb.net

Technical documentation

clickhouse.com/docs

docs.datastax.com

docs.gigaspaces.com/latest/landing.html

docs.griddb.net

Developer

Clickhouse Inc.

DataStax

Gigaspaces Technologies

Toshiba Corporation

Initial release

2016

2011

2000

2013

Current release

v24.4.1.2088-stable, May 2024

6.8, April 2020

15.5, September 2020

5.1, August 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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.

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

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

C++

Java

Java, C++, .Net

C++

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X

Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements (CQL); Spark SQL

SQL-99 for query and DML statements

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

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

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON 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
Rust
Scala

3rd party library

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C++
Java
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

configurable replication factor, datacenter aware, advanced replication for edge computing

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

ACID at container level

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. 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 for users can be defined per object

Role-based access control

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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GitHub trending repository
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Licensing and pricing models

Annual subscription
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Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.