DBMS > Amazon DynamoDB vs. Apache Kylin vs. Faircom DB vs. jBASE vs. Lovefield

System Properties Comparison Amazon DynamoDB vs. Apache Kylin vs. Faircom DB vs. jBASE vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

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

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A robust multi-value DBMS comprising development tools and middleware

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

Multivalue DBMS

Relational 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	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

aws.amazon.com/dynamodb

kylin.apache.org

www.faircom.com/products/faircom-db

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

google.github.io/lovefield

Technical documentation

docs.aws.amazon.com/dynamodb

kylin.apache.org/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Amazon

Apache Software Foundation, originally contributed from eBay Inc

FairCom Corporation

Rocket Software (formerly Zumasys)

Google

Initial release

2012

2015

1979

1991

2014

Current release

3.1.0, July 2020

V12, November 2020

5.7

2.1.12, February 2017

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

Restricted, free version available

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

ANSI C, C++

JavaScript

Server operating systems

hosted

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

AIX
Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

schema free, schema optional, schema required, partial schema,

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

optional

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

ANSI SQL for queries (using Apache Calcite)

yes, ANSI SQL with proprietary extensions

Embedded SQL for jBASE in BASIC

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Basic
Jabbascript
Java

JavaScript

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

by integration with AWS Lambda

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

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

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

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 with additional protections for files

Access rights can be defined down to the item level

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