DBMS > Cloudflare Workers KV vs. Cubrid vs. DataFS vs. Spark SQL

System Properties Comparison Cloudflare Workers KV vs. Cubrid vs. DataFS vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Primary database model

Secondary database models

Graph DBMS

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

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.cloudflare.com/developer-platform/workers-kv

cubrid.com (korean)
cubrid.org (english)

newdatabase.com

spark.apache.org/sql

Technical documentation

developers.cloudflare.com/kv/api

cubrid.org/manuals

dev.mobiland.com/Overview.xsp

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

Developer

Cloudflare

CUBRID Corporation, CUBRID Foundation

Mobiland AG

Apache Software Foundation

Initial release

2018

2008

2018

2014

Current release

11.0, January 2021

1.1.263, October 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

C, C++, Java

Scala

Server operating systems

hosted

Linux
Windows

Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST
Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

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

.Net
C
C#
C++
VB.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

fine grained access rights according to SQL-standard

Windows-Profile

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