DBMS > Microsoft SQL Server vs. Qdrant vs. Rockset vs. Vertica

System Properties Comparison Microsoft SQL Server vs. Qdrant vs. Rockset vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

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

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

Spatial DBMS
Time Series DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

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

github.com/qdrant/qdrant
qdrant.tech

rockset.com

www.vertica.com

Technical documentation

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

qdrant.tech/documentation

docs.rockset.com

vertica.com/documentation

Developer

Microsoft

Qdrant

Rockset

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1989

2021

2019

2005

Current release

SQL Server 2022, November 2022

12.0.3, January 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

C++

Rust

C++

Server operating systems

Linux
Windows

Docker
Linux
macOS
Windows

hosted

Linux

Data scheme

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

all fields are automatically indexed

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Read-only SQL queries, including JOINs

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

HTTP REST

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

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

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

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

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Automatic sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Collection-level replication

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Access rights for users and organizations can be defined via Rockset console

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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