DBMS > Cloudflare Workers KV vs. Datomic vs. Microsoft SQL Server vs. MonetDB vs. Teradata Aster

System Properties Comparison Cloudflare Workers KV vs. Datomic vs. Microsoft SQL Server vs. MonetDB vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed 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.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

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

www.datomic.com

www.microsoft.com/en-us/sql-server

www.monetdb.org

Technical documentation

developers.cloudflare.com/kv/api

docs.datomic.com

learn.microsoft.com/en-US/sql/sql-server

www.monetdb.org/Documentation

Developer

Cloudflare

Cognitect

Microsoft

MonetDB BV

Teradata

Initial release

2018

2012

1989

2004

2005

Current release

1.0.7075, December 2023

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C++

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

APIs and other access methods

HTTP REST
Proprietary protocol

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Clojure
Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, in SQL, C, R

R packages

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes, but depending on the SQL-Server Edition

none

Source-replica replication available in experimental status

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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