DBMS > Cloudflare Workers KV vs. Google Cloud Datastore vs. HyperSQL

System Properties Comparison Cloudflare Workers KV vs. Google Cloud Datastore vs. HyperSQL

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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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Primary database model

Key-value store

Document store

Relational DBMS

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

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Website

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

cloud.google.com/datastore

hsqldb.org

Technical documentation

developers.cloudflare.com/kv/api

cloud.google.com/datastore/docs

hsqldb.org/web/hsqlDocsFrame.html

Developer

Cloudflare

Google

Initial release

2018

2008

2001

Current release

2.7.2, June 2023

License

Commercial or Open Source

commercial

Open Source

based on BSD license

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

Java

Server operating systems

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

HTTP REST
Proprietary protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

using Google App Engine

Java, SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

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, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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