DBMS > Dragonfly vs. Hazelcast vs. NuoDB vs. Snowflake vs. Teradata Aster

System Properties Comparison Dragonfly vs. Hazelcast vs. NuoDB vs. Snowflake vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

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

A widely adopted in-memory data grid

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Cloud-based data warehousing service for structured and semi-structured data

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.87
Rank	#201	Overall
	#94	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

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

hazelcast.com

www.3ds.com/nuodb-distributed-sql-database

www.snowflake.com

Technical documentation

www.dragonflydb.io/docs

hazelcast.org/imdg/docs

doc.nuodb.com

docs.snowflake.net/manuals/index.html

Developer

DragonflyDB team and community contributors

Hazelcast

Dassault Systèmes

originally NuoDB, Inc.

Snowflake Computing Inc.

Teradata

Initial release

2023

2008

2013

2014

2005

Current release

1.0, March 2023

5.3.6, November 2023

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2; commercial licenses available

commercial

limited edition free

commercial

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++

Java

C++

Server operating systems

Linux

All OS with a Java VM

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

hosted

Linux

Data scheme

scheme-free

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

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

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

the object must implement a serialization strategy

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

Lua

yes

Event Listeners, Executor Services

Java, SQL

user defined functions

R packages

Triggers

publish/subscribe channels provide some trigger functionality

yes

Events

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

data is dynamically stored/cached on the nodes where it is read/written

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

yes

Managed transparently by NuoDB

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

one or two-phase-commit; repeatable reads; read commited

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

MVCC

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Temporary table

User concepts

Access control

Password-based authentication

Role-based access control

Standard SQL roles/ privileges, Administrative Users

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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