DBMS > Bangdb vs. Hazelcast vs. RavenDB vs. VoltDB

System Properties Comparison Bangdb vs. Hazelcast vs. RavenDB vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

A widely adopted in-memory data grid

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Document store
Graph DBMS
Time Series DBMS

Key-value store

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Document store

JSON support with IMDG 3.12

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

Technical documentation

docs.bangdb.com

hazelcast.org/imdg/docs

ravendb.net/docs

docs.voltdb.com

Developer

Sachin Sinha, BangDB

Hazelcast

Hibernating Rhinos

VoltDB Inc.

Initial release

2012

2008

2010

Current release

BangDB 2.0, October 2021

5.3.6, November 2023

5.4, July 2022

11.3, April 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2; commercial licenses available

Open Source

AGPL version 3, 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

C, C++

Java

Java, C++

Server operating systems

Linux

All OS with a Java VM

Linux
macOS
Raspberry Pi
Windows

Linux
OS X

for development

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

SQL-like query language (RQL)

yes

only a subset of SQL 99

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Java

Triggers

yes, Notifications (with Streaming only)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

yes

Replicated Map

Multi-source replication

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

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

ACID, Cluster-wide transaction available

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Role-based access control

Authorization levels configured per client per database

Users and roles with access to stored procedures

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