DBMS > Amazon DynamoDB vs. Hawkular Metrics vs. Memcached vs. Vertica

System Properties Comparison Amazon DynamoDB vs. Hawkular Metrics vs. Memcached vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

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

In-memory key-value store, originally intended for caching

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Key-value store

Time Series DBMS

Key-value store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/dynamodb

www.hawkular.org

www.memcached.org

www.vertica.com

Technical documentation

docs.aws.amazon.com/dynamodb

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

github.com/memcached/memcached/wiki

vertica.com/documentation

Developer

Amazon

Community supported by Red Hat

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2014

2003

2005

Current release

1.6.29, June 2024

12.0.3, January 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache 2.0

Open Source

BSD license

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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.

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP API

HTTP REST

Proprietary protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Go
Java
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

by integration with AWS Lambda

yes

via Hawkular Alerting

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

none

Repcached, a Memcached patch, provides this functionallity

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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.

User concepts

Access control

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

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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