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DBMS > ClickHouse vs. Milvus

System Properties Comparison ClickHouse vs. Milvus

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Our visitors often compare ClickHouse and Milvus with PostgreSQL, Elasticsearch and MongoDB.

Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionA 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 DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelRelational DBMSVector DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score15.17
Rank#37  Overall
#22  Relational DBMS
Score3.12
Rank#89  Overall
#4  Vector DBMS
Websiteclickhouse.commilvus.io
Technical documentationclickhouse.com/­docsmilvus.io/­docs/­overview.md
DeveloperClickhouse Inc.
Initial release20162019
Current releasev24.6.2.17-stable, July 20242.4.4, May 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase 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.
  • 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.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageC++C++, Go
Server operating systemsFreeBSD
Linux
macOS
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyes
Typing infopredefined data types such as float or dateyesVector, Numeric and String
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyesno
SQL infoSupport of SQLClose to ANSI SQL (SQL/JSON + extensions)no
APIs and other access methodsgRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol
RESTful HTTP API
Supported programming languagesC# info3rd party library
C++
Elixir info3rd party library
Go info3rd party library
Java info3rd party library
JavaScript (Node.js) info3rd party library
Kotlin info3rd party library
Nim info3rd party library
Perl info3rd party library
PHP info3rd party library
Python info3rd party library
R info3rd party library
Ruby info3rd party library
Rust
Scala info3rd party library
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesno
Triggersnono
Partitioning methods infoMethods for storing different data on different nodeskey based and customSharding
Replication methods infoMethods for redundantly storing data on multiple nodesAsynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlAccess 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.Role based access control and fine grained access rights
More information provided by the system vendor
ClickHouseMilvus
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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Related products and services
3rd partiesDoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
» more

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
ClickHouseMilvus
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

From Open Source to SaaS: the Journey of ClickHouse
16 January 2024, InfoQ.com

ClickHouse Cloud Is Now Generally Available on Microsoft Azure
25 June 2024, Business Wire

Snowflake vs. BigQuery vs. ClickHouse: Mastering Cost-Effective Business Analytics
6 December 2023, hackernoon.com

ClickHouse: Optimizing Real-Time Data Analysis with Online Analytical Processing
11 October 2023, The New Stack

Can LLMs Replace Data Analysts? Getting Answers Using SQL
22 December 2023, Towards Data Science

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26 March 2024, The New Stack

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IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, ibm.com

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

Using Evaluations to Optimize a RAG Pipeline: from Chunkings and Embeddings to LLMs
9 July 2024, Towards Data Science

provided by Google News



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