DBMS > Amazon SimpleDB vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB vs. searchxml

System Properties Comparison Amazon SimpleDB vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB vs. searchxml

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Native XML DBMS
Search engine

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

aws.amazon.com/simpledb

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.searchxml.net/category/products

Technical documentation

docs.aws.amazon.com/simpledb

docs.heavy.ai

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

www.searchxml.net/support/handouts

Developer

Amazon

HEAVY.AI, Inc.

Kinetica

Microsoft

informationpartners gmbh

Initial release

2007

2016

2012

2014

2015

Current release

5.10, January 2022

7.1, August 2021

1.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

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

C++ and CUDA

C, C++

C++

Server operating systems

hosted

Linux

hosted

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All columns are indexed automatically

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes

on the application server

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

Round robin

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

yes

Implicit feature of the cloud service

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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 depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

Multi-item ACID transactions with snapshot isolation within a partition

multiple readers, single writer

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

GPU vRAM or System RAM

User concepts

Access control

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

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Access rights can be defined down to the item level

Domain, group and role-based access control at the document level and for application services

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