DBMS > ClickHouse vs. Kdb vs. Milvus

System Properties Comparison ClickHouse vs. Kdb vs. Milvus

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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.

High performance Time Series DBMS

A DBMS designed for efficient storage of vector data and vector similarity searches

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Vector DBMS

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	16.25
Rank	#37	Overall
	#22	Relational DBMS

Score	7.72
Rank	#45	Overall
	#2	Time Series DBMS
	#2	Vector DBMS

Score	3.02
Rank	#90	Overall
	#5	Vector DBMS

Website

clickhouse.com

kx.com

milvus.io

Technical documentation

clickhouse.com/docs

code.kx.com

milvus.io/docs/overview.md

Developer

Clickhouse Inc.

Kx Systems, a division of First Derivatives plc

Initial release

2016

2000

kdb was released 2000, kdb+ in 2003

2019

Current release

v24.6.2.17-stable, July 2024

3.6, May 2018

2.4.4, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free 32-bit version

Open Source

Apache Version 2.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

Secondary indexes

yes

table attribute 'grouped'

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (q)

APIs and other access methods

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

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

key based and custom

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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.

rights management via user accounts

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

upon request
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Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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