DBMS > Hazelcast vs. Kinetica vs. RocksDB vs. SiteWhere

System Properties Comparison Hazelcast vs. Kinetica vs. RocksDB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Fully vectorized database across both GPUs and CPUs

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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
Time Series DBMS

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

hazelcast.com

www.kinetica.com

rocksdb.org

github.com/sitewhere/sitewhere

Technical documentation

hazelcast.org/imdg/docs

docs.kinetica.com

github.com/facebook/rocksdb/wiki

sitewhere1.sitewhere.io/index.html

Developer

Hazelcast

Kinetica

Facebook, Inc.

SiteWhere

Initial release

2008

2012

2013

2010

Current release

5.3.6, November 2023

7.1, August 2021

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

BSD

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, C++

C++

Java

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

HTTP REST

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes

Events

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

yes

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

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

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

GPU vRAM or System RAM

yes

User concepts

Access control

Role-based access control

Access rights for users and roles on table level

Users with fine-grained authorization concept

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