DBMS > Apache Druid vs. Fujitsu Enterprise Postgres vs. JaguarDB vs. Quasardb vs. SiteWhere

System Properties Comparison Apache Druid vs. Fujitsu Enterprise Postgres vs. JaguarDB vs. Quasardb vs. SiteWhere

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

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Performant, highly scalable DBMS for AI and IoT applications

Distributed, high-performance timeseries database

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

Secondary database models

Document store
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	0.31
Rank	#285	Overall
	#129	Relational DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

druid.apache.org

www.postgresql.fastware.com

www.jaguardb.com

quasar.ai

github.com/sitewhere/sitewhere

Technical documentation

druid.apache.org/docs/latest/design

www.postgresql.fastware.com/product-manuals

www.jaguardb.com/support.html

doc.quasar.ai/master

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation and contributors

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

DataJaguar, Inc.

quasardb

SiteWhere

Initial release

2012

2015

2009

2010

Current release

29.0.1, April 2024

Fujitsu Enterprise Postgres 14, January 2022

3.3 July 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL V3.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Common Public Attribution License Version 1.0

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++

Java

Server operating systems

Linux
OS X
Unix

Linux
Windows

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

integer and binary

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.

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL for querying

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

JDBC
ODBC

HTTP API

HTTP REST

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

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

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

partitioning by range, list and by hash

Sharding

consistent hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

Multi-source replication

Source-replica replication with selectable replication factor

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

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

rights management via user accounts

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
» more

Licensing and pricing models

Core based licensing
» more

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