DBMS > Apache Druid vs. Ehcache vs. Microsoft SQL Server vs. Postgres-XL vs. Stardog

System Properties Comparison Apache Druid vs. Ehcache vs. Microsoft SQL Server vs. Postgres-XL 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

A widely adopted Java cache with tiered storage options

Microsofts flagship relational DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
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	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

druid.apache.org

www.ehcache.org

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

www.postgres-xl.org

www.stardog.com

Technical documentation

druid.apache.org/docs/latest/design

www.ehcache.org/documentation

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

www.postgres-xl.org/documentation

docs.stardog.com

Developer

Apache Software Foundation and contributors

Terracotta Inc, owned by Software AG

Microsoft

Stardog-Union

Initial release

2012

2009

1989

2014

since 2012, originally named StormDB

2010

Current release

30.0.0, June 2024

3.10.0, March 2022

SQL Server 2022, November 2022

10 R1, October 2018

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

Open Source

Mozilla public license

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)

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Implementation language

Java

C++

Java

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

Linux
Windows

Linux
macOS

Linux
macOS
Windows

Data scheme

yes

schema-less columns are supported

schema-free

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

XML type, but no XML query functionality

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

yes

distributed, parallel query execution

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

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JCache

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

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

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

Java

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.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

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Cache Event Listeners

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

by using Terracotta Server

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

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

by using Terracotta Server

yes, but depending on the SQL-Server Edition

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

Tunable Consistency (Strong, Eventual, Weak)

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

yes

supports JTA and can work as an XA resource

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

fine grained access rights according to SQL-standard

Access rights for users and roles

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