DBMS > 4D vs. Amazon Redshift vs. Datomic vs. Dragonfly vs. NSDb

System Properties Comparison 4D vs. Amazon Redshift vs. Datomic vs. Dragonfly vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Primary database model

Relational DBMS

Key-value store

Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.4d.com

aws.amazon.com/redshift

www.datomic.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

nsdb.io

Technical documentation

developer.4d.com

docs.aws.amazon.com/redshift

docs.datomic.com

www.dragonflydb.io/docs

nsdb.io/Architecture

Developer

4D, Inc

Amazon (based on PostgreSQL)

Cognitect

DragonflyDB team and community contributors

Initial release

1984

2012

2023

2017

Current release

v20, April 2023

1.0.6735, June 2023

1.0, March 2023

License

Commercial or Open Source

commercial

limited edition free

Open Source

BSL 1.1

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, Clojure

C++

Java, Scala

Server operating systems

OS X
Windows

hosted

All OS with a Java VM

Linux

Linux
macOS

Data scheme

yes

scheme-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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.

yes

Secondary indexes

yes

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

close to SQL 92

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC
HTTP REST
WebSocket

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

Clojure
Java

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Java
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

Transaction Functions

Lua

Triggers

yes

By using transaction functions

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

But extensive use of caching in the application peers

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Users and groups

fine grained access rights according to SQL-standard

Password-based authentication

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