DBMS > Amazon DynamoDB vs. GridGain vs. HBase vs. Newts vs. PouchDB

System Properties Comparison Amazon DynamoDB vs. GridGain vs. HBase vs. Newts vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Time Series DBMS based on Cassandra

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Document store
Key-value store

Key-value store
Relational DBMS

Wide column store

Time Series DBMS

Document store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

aws.amazon.com/dynamodb

www.gridgain.com

hbase.apache.org

opennms.github.io/newts

pouchdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

www.gridgain.com/docs/index.html

hbase.apache.org/book.html

github.com/OpenNMS/newts/wiki

pouchdb.com/guides

Developer

Amazon

GridGain Systems, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

OpenNMS Group

Apache Software Foundation

Initial release

2012

2007

2008

2014

2012

Current release

GridGain 8.5.1

2.3.4, January 2021

7.1.1, June 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

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++, .Net

Java

JavaScript

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

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

Data scheme

schema-free

yes

schema-free, schema definition possible

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.

yes

Secondary indexes

yes

via views

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
RESTful HTTP API
Thrift

HTTP REST
Java API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java

JavaScript

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

Coprocessors in Java

View functions in JavaScript

Triggers

yes

by integration with AWS Lambda

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

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

ACID

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Security Hooks for custom implementations

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

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