DBMS > Dragonfly vs. EsgynDB vs. Lovefield vs. Stardog vs. XTDB

System Properties Comparison Dragonfly vs. EsgynDB vs. Lovefield vs. Stardog vs. XTDB

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

Embeddable relational database for web apps written in pure JavaScript

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

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

www.esgyn.cn

google.github.io/lovefield

www.stardog.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.dragonflydb.io/docs

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

docs.stardog.com

www.xtdb.com/docs

Developer

DragonflyDB team and community contributors

Esgyn

Google

Stardog-Union

Juxt Ltd.

Initial release

2023

2015

2014

2010

2019

Current release

1.0, March 2023

2.1.12, February 2017

7.3.0, May 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

MIT License

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

JavaScript

Java

Clojure

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

scheme-free

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

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

yes

yes, extensible-data-notation format

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

HTTP REST
JDBC

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

JavaScript

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

publish/subscribe channels provide some trigger functionality

Using read-only observers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

none

Multi-source replication in HA-Cluster

yes, each node contains all data

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 in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

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

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Password-based authentication

fine grained access rights according to SQL-standard

Access rights for users and roles

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More resources

Recent citations in the news

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provided by Google News

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