DBMS > Hazelcast vs. Heroic vs. Linter vs. Postgres-XL vs. SiteWhere

System Properties Comparison Hazelcast vs. Heroic vs. Linter vs. Postgres-XL vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

RDBMS for high security requirements

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

M2M integration platform for persisting/querying time series data

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

Document store
Spatial DBMS

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Website

hazelcast.com

github.com/spotify/heroic

linter.ru

www.postgres-xl.org

github.com/sitewhere/sitewhere

Technical documentation

hazelcast.org/imdg/docs

spotify.github.io/heroic

www.postgres-xl.org/documentation

sitewhere1.sitewhere.io/index.html

Developer

Hazelcast

Spotify

relex.ru

SiteWhere

Initial release

2008

2014

1990

2014

since 2012, originally named StormDB

2010

Current release

5.3.6, November 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

Open Source

Mozilla public license

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 and C++

Java

Server operating systems

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

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

the object must implement a serialization strategy

yes

XML type, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

HTTP REST

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

user defined functions

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

MVCC

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

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast Showcases Real-Time Data Platform at 2024 Gartner Summit
15 May 2024, Datanami

Hazelcast appoints Anthony Griffin as Chief Architect -
11 June 2024, Enterprise Times

Hazelcast Versus Redis: A Practical Comparison
4 January 2024, Database Trends and Applications

provided by Google News

SiteWhere: An open platform for connected devices
11 July 2017, Open Source For You

11 Best Open source IoT Platforms To Develop Smart Projects
9 March 2023, H2S Media

provided by Google News

Share this page