DBMS > Bangdb vs. PouchDB vs. SingleStore vs. YottaDB

System Properties Comparison Bangdb vs. PouchDB vs. SingleStore vs. YottaDB

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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

JavaScript DBMS with an API inspired by CouchDB

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

A fast and solid embedded Key-value store

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

Relational DBMS

using the Octo plugin

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

Technical documentation

docs.bangdb.com

pouchdb.com/guides

docs.singlestore.com

yottadb.com/resources/documentation

Developer

Sachin Sinha, BangDB

Apache Software Foundation

SingleStore Inc.

YottaDB, LLC

Initial release

2012

2013

2001

Current release

BangDB 2.0, October 2021

7.1.1, June 2019

8.5, January 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

commercial

free developer edition available

Open Source

AGPL 3.0

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C, C++

JavaScript

C++, Go

Server operating systems

Linux

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

Linux

64 bit version required

Docker
Linux

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

via views

yes

SQL

Support of SQL

SQL like support with command line tool

yes

but no triggers and foreign keys

by using the Octo plugin

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C
C#
C++
Java
Python

JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Triggers

yes, Notifications (with Streaming only)

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

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication

stores two copies of each physical data partition on two separate nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

Fine grained access control via users, groups and roles

Users and groups based on OS-security mechanisms

More information provided by the system vendor

Specific characteristics

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Key customers

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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More resources

DB-Engines blog posts

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