DBMS > BigchainDB vs. Manticore Search vs. Microsoft Azure Table Storage vs. MySQL vs. PouchDB

System Properties Comparison BigchainDB vs. Manticore Search vs. Microsoft Azure Table Storage vs. MySQL vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Multi-storage database for search, including full-text search.

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Key/Value like access via memcached API

Document store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

www.bigchaindb.com

manticoresearch.com

azure.microsoft.com/en-us/services/storage/tables

www.mysql.com

pouchdb.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

manual.manticoresearch.com

dev.mysql.com/doc

pouchdb.com/guides

Developer

Manticore Software

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2016

2017

2012

1995

2012

Current release

6.0, February 2023

8.4.0, April 2024

7.1.1, June 2019

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

GPL version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Python

C++

C and C++

JavaScript

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

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

Data scheme

schema-free

Fixed schema

schema-free

yes

schema-free

Typing

predefined data types such as float or date

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

via views

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

CLI Client
RESTful HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Synchronous replication based on Galera library

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

optimistic locking

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

The original contents of fields are not stored in the Manticore index.

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

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