DBMS > BigObject vs. Hazelcast vs. RethinkDB vs. STSdb vs. VoltDB

System Properties Comparison BigObject vs. Hazelcast vs. RethinkDB vs. STSdb vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A widely adopted in-memory data grid

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

bigobject.io

hazelcast.com

rethinkdb.com

github.com/STSSoft/STSdb4

www.voltdb.com

Technical documentation

docs.bigobject.io

hazelcast.org/imdg/docs

rethinkdb.com/docs

docs.voltdb.com

Developer

BigObject, Inc.

Hazelcast

The Linux Foundation

since July 2017

STS Soft SC

VoltDB Inc.

Initial release

2015

2008

2009

2011

2010

Current release

5.3.6, November 2023

2.4.1, August 2020

4.0.8, September 2015

11.3, April 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2

Open Source

GPLv2, commercial license available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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++

Java, C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

Linux
OS X
Windows

Windows

Linux
OS X

for development

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

primitive types and user defined types (classes)

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 DML and DDL statements

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C#
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

Event Listeners, Executor Services

Java

Triggers

yes

Events

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Replicated Map

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

FOREIGN KEY constraints are not supported

Transaction concepts

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

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

Atomic single-document operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

MVCC based

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

yes

users and table-level permissions

Users and roles with access to stored procedures

More information provided by the system vendor

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More resources

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