DBMS > ClickHouse vs. etcd vs. Firebolt vs. GBase vs. Hazelcast

System Properties Comparison ClickHouse vs. etcd vs. Firebolt vs. GBase vs. Hazelcast

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.

A distributed reliable key-value store

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A widely adopted in-memory data grid

Primary database model

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Website

clickhouse.com

etcd.io
github.com/etcd-io/etcd

www.firebolt.io

www.gbase.cn

hazelcast.com

Technical documentation

clickhouse.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.firebolt.io

hazelcast.org/imdg/docs

Developer

Clickhouse Inc.

Firebolt Analytics Inc.

General Data Technology Co., Ltd.

Hazelcast

Initial release

2016

2020

2004

2008

Current release

v24.4.1.2088-stable, May 2024

3.4, August 2019

GBase 8a, GBase 8s, GBase 8c

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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.
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.

Implementation language

C++

C, Java, Python

Java

Server operating systems

FreeBSD
Linux
macOS

FreeBSD
Linux
Windows

experimental

hosted

Linux

All OS with a Java VM

Data scheme

yes

schema-free

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

Standard with numerous extensions

SQL-like query language

APIs and other access methods

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

gRPC
JSON over HTTP

.Net
ODBC
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP 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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Go
JavaScript (Node.js)
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions

yes

Event Listeners, Executor Services

Triggers

yes, watching key changes

yes

Events

Partitioning methods

Methods for storing different data on different nodes

key based and custom

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

depending on storage layer

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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.

yes

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.