DBMS > Cloudflare Workers KV vs. GridDB vs. MySQL vs. SQream DB vs. Trafodion

System Properties Comparison Cloudflare Workers KV vs. GridDB vs. MySQL vs. SQream DB vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Scalable in-memory time series database optimized for IoT and Big Data

Widely used open source RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

Document store
Spatial DBMS

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

Score	0.32
Rank	#283	Overall
	#40	Key-value stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

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

Technical documentation

developers.cloudflare.com/kv/api

docs.griddb.net

dev.mysql.com/doc

docs.sqream.com

trafodion.apache.org/documentation.html

Developer

Cloudflare

Toshiba Corporation

Oracle

since 2010, originally MySQL AB, then Sun

SQream Technologies

Apache Software Foundation, originally developed by HP

Initial release

2018

2013

1995

2017

2014

Current release

5.1, August 2022

8.4.0, April 2024

2022.1.6, December 2022

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL version 2. Commercial licenses with extended functionallity are 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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

C and C++

C++, CUDA, Haskell, Java, Scala

C++, Java

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

yes, ANSI Standard SQL 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

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

with proprietary extensions

yes

APIs and other access methods

HTTP REST
Proprietary protocol

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

.Net
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

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

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

C++
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions in Python

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID at container level

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.