DBMS > Cloudflare Workers KV vs. HarperDB vs. Microsoft Azure Cosmos DB vs. OrientDB

System Properties Comparison Cloudflare Workers KV vs. HarperDB vs. Microsoft Azure Cosmos DB vs. OrientDB

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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.

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Globally distributed, horizontally scalable, multi-model database service

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Key-value store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Graph DBMS
Key-value store

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#283	Overall
	#40	Key-value stores

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.02
Rank	#88	Overall
	#16	Document stores
	#6	Graph DBMS
	#12	Key-value stores

Website

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

www.harperdb.io

azure.microsoft.com/services/cosmos-db

orientdb.org

Technical documentation

developers.cloudflare.com/kv/api

docs.harperdb.io/docs

learn.microsoft.com/azure/cosmos-db

www.orientdb.com/docs/last/index.html

Developer

Cloudflare

HarperDB

Microsoft

OrientDB LTD; CallidusCloud; SAP

Initial release

2018

2017

2014

2010

Current release

3.1, August 2021

3.2.29, March 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

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.

Implementation language

Node.js

Java

Server operating systems

hosted

Linux
OS X

hosted

All OS with a Java JDK (>= JDK 6)

Data scheme

schema-free

dynamic schema

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

Typing

predefined data types such as float or date

yes

JSON data types

yes

JSON 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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

SQL-like query language, no joins

APIs and other access methods

HTTP REST
Proprietary protocol

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

JavaScript

Java, Javascript

Triggers

JavaScript

Hooks

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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 and roles

Access rights can be defined down to the item level

Access rights for users and roles; record level security configurable

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