DBMS > Apache Druid vs. JaguarDB vs. Microsoft SQL Server vs. Spark SQL vs. Stardog

System Properties Comparison Apache Druid vs. JaguarDB vs. Microsoft SQL Server vs. Spark SQL vs. Stardog

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

Performant, highly scalable DBMS for AI and IoT applications

Microsofts flagship relational DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

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

Website

druid.apache.org

www.jaguardb.com

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

spark.apache.org/sql

www.stardog.com

Technical documentation

druid.apache.org/docs/latest/design

www.jaguardb.com/support.html

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

spark.apache.org/docs/latest/sql-programming-guide.html

docs.stardog.com

Developer

Apache Software Foundation and contributors

DataJaguar, Inc.

Microsoft

Apache Software Foundation

Stardog-Union

Initial release

2012

2015

1989

2014

2010

Current release

29.0.1, April 2024

3.3 July 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

GPL V3.0

commercial

restricted free version is available

Open Source

Apache 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

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

Implementation language

Java

C++

the server part. Clients available in other languages

C++

Scala

Java

Server operating systems

Linux
OS X
Unix

Linux

Linux
Windows

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL for querying

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL-like DML and DDL statements

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

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

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

JDBC
ODBC

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

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Java
Python
R
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

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

Sharding

manual/auto, time-based

Sharding

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

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

yes, but depending on the SQL-Server Edition

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate 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.

yes

User concepts

Access control

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

rights management via user accounts

fine grained access rights according to SQL-standard

Access rights for users and roles

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