DBMS > ClickHouse vs. etcd vs. Kinetica vs. XTDB

System Properties Comparison ClickHouse vs. etcd vs. Kinetica vs. XTDB

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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.

A distributed reliable key-value store

Fully vectorized database across both GPUs and CPUs

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

clickhouse.com

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

www.kinetica.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

clickhouse.com/docs

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

docs.kinetica.com

www.xtdb.com/docs

Developer

Clickhouse Inc.

Kinetica

Juxt Ltd.

Initial release

2016

2012

2019

Current release

v24.6.2.17-stable, July 2024

3.4, August 2019

7.1, August 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

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.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

C, C++

Clojure

Server operating systems

FreeBSD
Linux
macOS

FreeBSD
Linux
Windows

experimental

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

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

gRPC
JSON over HTTP

JDBC
ODBC
RESTful HTTP API

HTTP REST
JDBC

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

C++
Java
JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes, watching key changes

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

none

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.

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

GPU vRAM or System RAM

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 and roles on table level

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