DBMS > 4D vs. Apache IoTDB vs. Cloudflare Workers KV vs. Couchbase vs. Lovefield

System Properties Comparison 4D vs. Apache IoTDB vs. Cloudflare Workers KV vs. Couchbase vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

www.4d.com

iotdb.apache.org

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

www.couchbase.com

google.github.io/lovefield

Technical documentation

developer.4d.com

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

developers.cloudflare.com/kv/api

docs.couchbase.com

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

4D, Inc

Apache Software Foundation

Cloudflare

Couchbase, Inc.

Google

Initial release

1984

2018

2011

2014

Current release

v20, April 2023

1.1.0, April 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

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

C, C++, Go and Erlang

JavaScript

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

hosted

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

HTTP REST
Proprietary protocol

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

JavaScript

Server-side scripts

Stored procedures

yes

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Triggers

yes

via the TAP protocol

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Automatic Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Multi-source replication

including cross data center replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

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

yes

Ephemeral buckets

yes

using MemoryDB

User concepts

Access control

Users and groups

yes

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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