DBMS > Dragonfly vs. Google BigQuery vs. Hazelcast vs. Linter vs. NuoDB

System Properties Comparison Dragonfly vs. Google BigQuery vs. Hazelcast vs. Linter vs. NuoDB

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

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

RDBMS for high security requirements

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

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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Trend Chart

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	0.87
Rank	#201	Overall
	#94	Relational DBMS

Website

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

cloud.google.com/bigquery

hazelcast.com

linter.ru

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

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

doc.nuodb.com

Developer

DragonflyDB team and community contributors

Google

Hazelcast

relex.ru

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2023

2010

2008

1990

2013

Current release

1.0, March 2023

5.3.6, November 2023

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

limited edition free

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C and C++

C++

Server operating systems

Linux

hosted

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

scheme-free

yes

schema-free

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

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
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

Java, SQL

Triggers

publish/subscribe channels provide some trigger functionality

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

Source-replica replication

yes

Managed transparently by NuoDB

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate 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

Since BigQuery is designed for querying data

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

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

MVCC

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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