DBMS > Apache Druid vs. Apache Phoenix vs. EXASOL vs. Microsoft SQL Server vs. Qdrant

System Properties Comparison Apache Druid vs. Apache Phoenix vs. EXASOL vs. Microsoft SQL Server vs. Qdrant

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A scale-out RDBMS with evolutionary schema built on Apache HBase

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Vector DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Website

druid.apache.org

phoenix.apache.org

www.exasol.com

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

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

druid.apache.org/docs/latest/design

phoenix.apache.org

www.exasol.com/resources

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

qdrant.tech/documentation

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Exasol

Microsoft

Qdrant

Initial release

2012

2014

2000

1989

2021

Current release

30.0.0, June 2024

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Rust

Server operating systems

Linux
OS X
Unix

Linux
Unix
Windows

Linux
Windows

Docker
Linux
macOS
Windows

Data scheme

yes

schema-less columns are supported

yes

late-bound, schema-on-read capabilities

yes

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.

yes

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

.Net
JDBC
ODBC
WebSocket

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Java
Lua
Python
R

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

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

Server-side scripts

Stored procedures

user defined functions

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency, tunable 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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

Key-based authentication

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