DBMS > Dragonfly vs. Faircom EDGE vs. Lovefield vs. Microsoft Azure Synapse Analytics vs. RDF4J

System Properties Comparison Dragonfly vs. Faircom EDGE vs. Lovefield vs. Microsoft Azure Synapse Analytics vs. RDF4J

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Embeddable relational database for web apps written in pure JavaScript

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

RDF store

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

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

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

www.faircom.com/products/faircom-edge

google.github.io/lovefield

azure.microsoft.com/services/synapse-analytics

rdf4j.org

Technical documentation

www.dragonflydb.io/docs

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

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

docs.microsoft.com/azure/synapse-analytics

rdf4j.org/documentation

Developer

DragonflyDB team and community contributors

FairCom Corporation

Google

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2023

1979

2014

2016

2004

Current release

1.0, March 2023

V3, October 2020

2.1.12, February 2017

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Restricted, free version available

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C++

ANSI C, C++

JavaScript

C++

Java

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

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

tested with Chrome, Firefox, IE, Safari

hosted

Linux
OS X
Unix
Windows

Data scheme

scheme-free

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

yes

RDF Schemas

Typing

predefined data types such as float or date

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

yes, ANSI Standard SQL Types

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

ANSI SQL queries

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

JavaScript

C#
Java
PHP

Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

.Net, JavaScript, C/C++

Transact SQL

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

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

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Password-based authentication

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

yes

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