DBMS > Amazon Redshift vs. Qdrant vs. SpatiaLite

System Properties Comparison Amazon Redshift vs. Qdrant vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Spatial extension of SQLite

Primary database model

Relational DBMS

Vector DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

aws.amazon.com/redshift

github.com/qdrant/qdrant
qdrant.tech

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.aws.amazon.com/redshift

qdrant.tech/documentation

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Amazon (based on PostgreSQL)

Qdrant

Alessandro Furieri

Initial release

2012

2021

2008

Current release

5.0.0, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Rust

C++

Server operating systems

hosted

Docker
Linux
macOS
Windows

server-less

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

yes

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

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

All languages supporting JDBC/ODBC

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

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Collection-level replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

User concepts

Access control

fine grained access rights according to SQL-standard

Key-based authentication

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