DBMS > Apache Drill vs. Cloudflare Workers KV vs. DataFS vs. NSDb

System Properties Comparison Apache Drill vs. Cloudflare Workers KV vs. DataFS vs. NSDb

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Editorial information provided by DB-Engines

Name

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.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Document store
Relational DBMS

Key-value store

Object oriented DBMS

Time Series DBMS

Secondary database models

Graph DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

drill.apache.org

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

newdatabase.com

nsdb.io

Technical documentation

drill.apache.org/docs

developers.cloudflare.com/kv/api

dev.mobiland.com/Overview.xsp

nsdb.io/Architecture

Developer

Apache Software Foundation

Cloudflare

Mobiland AG

Initial release

2012

2018

2017

Current release

1.20.3, January 2023

1.1.263, October 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

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, Scala

Server operating systems

Linux
OS X
Windows

hosted

Windows

Linux
macOS

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP REST
Proprietary protocol

.NET Client API
Proprietary client DLL
WinRT client

gRPC
HTTP REST
WebSocket

Supported programming languages

C++

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
C
C#
C++
VB.Net

Java
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

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

Using Apache Lucene

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

Windows-Profile

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More resources

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Apache Drill Eliminates ETL, Data Transformation for MapR Database
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