DBMS > Amazon DynamoDB vs. HBase vs. MonetDB vs. Oracle Coherence vs. RocksDB

System Properties Comparison Amazon DynamoDB vs. HBase vs. MonetDB vs. Oracle Coherence vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

A relational database management system that stores data in columns

Oracles in-memory data grid solution

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

Primary database model

Document store
Key-value store

Wide column store

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

aws.amazon.com/dynamodb

hbase.apache.org

www.monetdb.org

www.oracle.com/java/coherence

rocksdb.org

Technical documentation

docs.aws.amazon.com/dynamodb

hbase.apache.org/book.html

www.monetdb.org/Documentation

docs.oracle.com/en/middleware/standalone/coherence

github.com/facebook/rocksdb/wiki

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Powerset

MonetDB BV

Oracle

Facebook, Inc.

Initial release

2012

2008

2004

2007

2013

Current release

2.3.4, January 2021

Dec2023 (11.49), December 2023

14.1, August 2023

9.2.1, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

Open Source

Mozilla Public License 2.0

commercial

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

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JCache
JPA
RESTful HTTP API

C++ API
Java API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

.Net
C++
Java

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes, in SQL, C, R

Triggers

yes

by integration with AWS Lambda

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

Source-replica replication available in experimental status

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

Single row ACID (across millions of columns)

ACID

configurable

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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 can be defined via the AWS Identity and Access Management (IAM)

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

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

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