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

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

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.

Operational and transactional Enterprise NoSQL database

Microsofts flagship relational DBMS

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational 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	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

druid.apache.org

phoenix.apache.org

www.exasol.com

www.progress.com/marklogic

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

Technical documentation

druid.apache.org/docs/latest/design

phoenix.apache.org

www.exasol.com/resources

www.progress.com/marklogic/documentation

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

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Exasol

MarkLogic Corp.

Microsoft

Initial release

2012

2014

2000

2001

1989

Current release

30.0.0, June 2024

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

11.0, December 2022

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

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

Linux
Unix
Windows

Linux
OS X
Windows

Linux
Windows

Data scheme

yes

schema-less columns are supported

yes

late-bound, schema-on-read capabilities

yes

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL92

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

.Net
JDBC
ODBC
WebSocket

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

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

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

Server-side scripts

Stored procedures

user defined functions

yes

via XQuery or JavaScript

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

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

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Hadoop integration

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

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

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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