DBMS > Bangdb vs. GigaSpaces vs. PouchDB vs. Valentina Server

System Properties Comparison Bangdb vs. GigaSpaces vs. PouchDB vs. Valentina Server

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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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

JavaScript DBMS with an API inspired by CouchDB

Object-relational database and reports server

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Graph DBMS
Search engine

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	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

Technical documentation

docs.bangdb.com

docs.gigaspaces.com/latest/landing.html

pouchdb.com/guides

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Sachin Sinha, BangDB

Gigaspaces Technologies

Apache Software Foundation

Paradigma Software

Initial release

2012

2000

2012

1999

Current release

BangDB 2.0, October 2021

15.5, September 2020

7.1.1, June 2019

5.7.5

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2; Commercial licenses available

Open Source

commercial

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, C++, .Net

JavaScript

Server operating systems

Linux

Linux
macOS
Solaris
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Windows

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.

XML can be used for describing objects metadata

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

via views

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-99 for query and DML statements

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HTTP REST

only for PouchDB Server
JavaScript API

ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
C++
Java
Python
Scala

JavaScript

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

Triggers

yes, Notifications (with Streaming only)

yes, event driven architecture

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

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual 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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

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

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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