DBMS > Brytlyt vs. GeoMesa vs. Microsoft SQL Server vs. Qdrant vs. Stardog

System Properties Comparison Brytlyt vs. GeoMesa vs. Microsoft SQL Server vs. Qdrant vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Microsofts flagship relational DBMS

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

brytlyt.io

www.geomesa.org

www.microsoft.com/en-us/sql-server

github.com/qdrant/qdrant
qdrant.tech

www.stardog.com

Technical documentation

docs.brytlyt.io

www.geomesa.org/documentation/stable/user/index.html

learn.microsoft.com/en-US/sql/sql-server

qdrant.tech/documentation

docs.stardog.com

Developer

Brytlyt

CCRi and others

Microsoft

Qdrant

Stardog-Union

Initial release

2016

2014

1989

2021

2010

Current release

5.0, August 2023

4.0.5, February 2024

SQL Server 2022, November 2022

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++ and CUDA

Scala

C++

Rust

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

Docker
Linux
macOS
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

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.

yes

specific XML-type available, but no XML query functionality.

yes

Import/export of XML data possible

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

depending on storage layer

yes, but depending on the SQL-Server Edition

Collection-level replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate Consistency

Eventual Consistency, tunable consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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.

depending on storage layer

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

Key-based authentication

Access rights for users and roles

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