DBMS > Apache Drill vs. Cloudflare Workers KV vs. Drizzle vs. ReductStore

System Properties Comparison Apache Drill vs. Cloudflare Workers KV vs. Drizzle vs. ReductStore

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Document store
Relational DBMS

Key-value store

Relational DBMS

Time Series DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

drill.apache.org

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

github.com/reductstore
www.reduct.store

Technical documentation

drill.apache.org/docs

developers.cloudflare.com/kv/api

www.reduct.store/docs

Developer

Apache Software Foundation

Cloudflare

Drizzle project, originally started by Brian Aker

ReductStore LLC

Initial release

2012

2018

2008

2023

Current release

1.20.3, January 2023

7.2.4, September 2012

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GNU GPL

Open Source

Business Source License 1.1

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++

C++, Rust

Server operating systems

Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP REST
Proprietary protocol

JDBC

HTTP API

Supported programming languages

C++

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

C
C++
Java
PHP

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual 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

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

User concepts

Access control

Depending on the underlying data source

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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More resources

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