DBMS > Amazon Redshift vs. Kinetica vs. OpenMLDB vs. Qdrant

System Properties Comparison Amazon Redshift vs. Kinetica vs. OpenMLDB vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

An open-source machine learning database that provides a feature platform for training and inference

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

Primary database model

Relational DBMS

Time Series DBMS

Vector DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	1.28
Rank	#167	Overall
	#8	Vector DBMS

Website

aws.amazon.com/redshift

www.kinetica.com

openmldb.ai

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

openmldb.ai/docs/zh/main

qdrant.tech/documentation

Developer

Amazon (based on PostgreSQL)

Kinetica

4 Paradigm Inc.

Qdrant

Initial release

2012

2020

2021

Current release

7.1, August 2021

2024-2 February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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.

Implementation language

C, C++

C++, Java, Scala

Rust

Server operating systems

hosted

Linux

Docker
Linux
macOS
Windows

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

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

restricted

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java
Python
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

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

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Key-based authentication

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