DBMS > Amazon SimpleDB vs. Cassandra vs. Memcached vs. Spark SQL

System Properties Comparison Amazon SimpleDB vs. Cassandra vs. Memcached vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Vector DBMS

starting with release V5

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/simpledb

cassandra.apache.org

www.memcached.org

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/simpledb

cassandra.apache.org/doc/latest

github.com/memcached/memcached/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Apache Software Foundation

Apache top level project, originally developped by Facebook

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Apache Software Foundation

Initial release

2007

2008

2003

2014

Current release

4.1.3, July 2023

1.6.27, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

BSD license

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Scala

Server operating systems

hosted

BSD
Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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

All columns are indexed automatically

restricted

only equality queries, not always the best performing solution

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Proprietary protocol

JDBC
ODBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

no "single point of failure"

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Representation of geographical distribution of servers is possible

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

Transaction concepts

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

Concurrent data updates can be detected by the application

Atomicity and isolation are supported for single operations

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)

Access rights for users can be defined per object

yes

using SASL (Simple Authentication and Security Layer) protocol

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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