DBMS > Amazon DocumentDB vs. HBase vs. MonetDB vs. PouchDB vs. RocksDB

System Properties Comparison Amazon DocumentDB vs. HBase vs. MonetDB vs. PouchDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Wide-column store based on Apache Hadoop and on concepts of BigTable

A relational database management system that stores data in columns

JavaScript DBMS with an API inspired by CouchDB

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/documentdb

Technical documentation

aws.amazon.com/documentdb/resources

hbase.apache.org/book.html

www.monetdb.org/Documentation

pouchdb.com/guides

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

MonetDB BV

Apache Software Foundation

Facebook, Inc.

Initial release

2019

2008

2004

2012

2013

Current release

2.3.4, January 2021

Dec2023 (11.49), December 2023

7.1.1, June 2019

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Mozilla Public License 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Solaris
Windows

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

Linux

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

via views

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Java API
RESTful HTTP API
Thrift

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes, in SQL, C, R

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding via remote tables

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication
Source-replica replication

none

Source-replica replication available in experimental status

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

Single row ACID (across millions of columns)

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

User concepts

Access control

Access rights for users and roles

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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