DBMS > Cloudflare Workers KV vs. IBM Db2 vs. MySQL vs. Solr

System Properties Comparison Cloudflare Workers KV vs. IBM Db2 vs. MySQL vs. Solr

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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.

Common in IBM host environments, 2 different versions for host and Windows/Linux

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Key-value store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Key/Value like access via memcached API

Search engine

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Spatial DBMS

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	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

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

www.ibm.com/products/db2

www.mysql.com

solr.apache.org

Technical documentation

developers.cloudflare.com/kv/api

www.ibm.com/docs/en/db2

dev.mysql.com/doc

solr.apache.org/resources.html

Developer

Cloudflare

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2018

1983

host version

1995

2006

Current release

12.1, October 2016

8.4.0, April 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

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 and C++

Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

HTTP REST
Proprietary protocol

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

yes

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