DBMS > Cubrid vs. HarperDB vs. Linter vs. Milvus vs. Qdrant

System Properties Comparison Cubrid vs. HarperDB vs. Linter vs. Milvus vs. Qdrant

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

RDBMS for high security requirements

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

A high-performance vector database with neural network or semantic-based matching

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

cubrid.com (korean)
cubrid.org (english)

www.harperdb.io

linter.ru

milvus.io

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

cubrid.org/manuals

docs.harperdb.io/docs

milvus.io/docs/overview.md

qdrant.tech/documentation

Developer

CUBRID Corporation, CUBRID Foundation

HarperDB

relex.ru

Qdrant

Initial release

2008

2017

1990

2019

2021

Current release

11.0, January 2021

3.1, August 2021

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

Open Source

Apache Version 2.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C, C++, Java

Node.js

C and C++

C++, Go

Rust

Server operating systems

Linux
Windows

Linux
OS X

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Docker
Linux
macOS
Windows

Data scheme

yes

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

Vector, Numeric and String

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

Custom Functions

since release 3.1

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

the nodes on which a table resides can be defined

Source-replica replication

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

Key-based authentication

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

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

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.

Related products and services

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