DBMS > Cloudflare Workers KV vs. IBM Db2 vs. OpenEdge vs. Splice Machine vs. SQLite

System Properties Comparison Cloudflare Workers KV vs. IBM Db2 vs. OpenEdge vs. Splice Machine vs. SQLite

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

Application development environment with integrated database management system

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store

Relational DBMS

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

Relational DBMS

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

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	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

www.ibm.com/products/db2

www.progress.com/openedge

splicemachine.com

www.sqlite.org

Technical documentation

developers.cloudflare.com/kv/api

www.ibm.com/docs/en/db2

documentation.progress.com/output/ua/OpenEdge_latest

splicemachine.com/how-it-works

www.sqlite.org/docs.html

Developer

Cloudflare

IBM

Progress Software Corporation

Splice Machine

Dwayne Richard Hipp

Initial release

2018

1983

host version

1984

2014

2000

Current release

12.1, October 2016

OpenEdge 12.2, March 2020

3.1, March 2021

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

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 and C++

Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Solaris
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

close to SQL 92

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP REST
Proprietary protocol

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

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

Progress proprietary ABL (Advanced Business Language)

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

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

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning

since Version 11.4

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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 and groups

Access rights for users, groups and roles according to SQL-standard

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