DBMS > Dragonfly vs. EsgynDB vs. Netezza vs. NSDb

System Properties Comparison Dragonfly vs. EsgynDB vs. Netezza vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

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

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	7.56
Rank	#48	Overall
	#31	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

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

www.esgyn.cn

www.ibm.com/products/netezza

nsdb.io

Technical documentation

www.dragonflydb.io/docs

nsdb.io/Architecture

Developer

DragonflyDB team and community contributors

Esgyn

IBM

Initial release

2023

2015

2000

2017

Current release

1.0, March 2023

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C++, Java

Java, Scala

Server operating systems

Linux

included in appliance

Linux
macOS

Data scheme

scheme-free

yes

Typing

predefined data types such as float or date

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

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

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

JDBC
ODBC
OLE DB

gRPC
HTTP REST
WebSocket

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
Scala

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

yes

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

yes

User concepts

Access control

Password-based authentication

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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More resources

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