DBMS > Amazon SimpleDB vs. Hyprcubd vs. jBASE vs. SQLite vs. Vitess

System Properties Comparison Amazon SimpleDB vs. Hyprcubd vs. jBASE vs. SQLite vs. Vitess

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

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

Serverless Time Series DBMS

A robust multi-value DBMS comprising development tools and middleware

Widely used embeddable, in-process RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
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	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

aws.amazon.com/simpledb

hyprcubd.com (offline)

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.sqlite.org

vitess.io

Technical documentation

docs.aws.amazon.com/simpledb

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

www.sqlite.org/docs.html

vitess.io/docs

Developer

Amazon

Hyprcubd, Inc.

Rocket Software (formerly Zumasys)

Dwayne Richard Hipp

The Linux Foundation, PlanetScale

Initial release

2007

1991

2000

2013

Current release

5.7

3.45.3 (15 April 2024), April 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Public Domain

Open Source

Apache Version 2.0, commercial licenses available

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

Server operating systems

hosted

AIX
Linux
Windows

server-less

Docker
Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

optional

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language

Embedded SQL for jBASE in BASIC

yes

SQL-92 is not fully supported

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

gRPC (https)

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net
Basic
Jabbascript
Java

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

table locks or row locks depending on storage engine

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)

token access

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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