DBMS > Amazon DynamoDB vs. Apache Druid vs. Apache Phoenix vs. GridGain vs. Hawkular Metrics

System Properties Comparison Amazon DynamoDB vs. Apache Druid vs. Apache Phoenix vs. GridGain vs. Hawkular Metrics

Editorial information provided by DB-Engines

Name

Description

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Document store
Key-value store

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

druid.apache.org/docs/latest/design

phoenix.apache.org

www.gridgain.com/docs/index.html

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Amazon

Apache Software Foundation and contributors

Apache Software Foundation

GridGain Systems, Inc.

Community supported by Red Hat

Initial release

2012

2014

2007

2014

Current release

29.0.1, April 2024

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

GridGain 8.5.1

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Java, C++, .Net

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux
Unix
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-less columns are supported

yes

late-bound, schema-on-read capabilities

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL for querying

yes

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

APIs and other access methods

RESTful HTTP API

JDBC
RESTful HTTP/JSON API

JDBC

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

HTTP REST

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

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

Triggers

yes

by integration with AWS Lambda

yes (cache interceptors and events)

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication
Source-replica replication

yes (replicated cache)

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

yes (compute grid and hadoop accelerator)

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

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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)

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Security Hooks for custom implementations

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