DBMS > Dragonfly vs. Kinetica vs. Lovefield

System Properties Comparison Dragonfly vs. Kinetica vs. Lovefield

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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

Fully vectorized database across both GPUs and CPUs

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

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

www.kinetica.com

google.github.io/lovefield

Technical documentation

www.dragonflydb.io/docs

docs.kinetica.com

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

Developer

DragonflyDB team and community contributors

Kinetica

Google

Initial release

2023

2012

2014

Current release

1.0, March 2023

7.1, August 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 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, C++

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Data scheme

scheme-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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC
RESTful HTTP API

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++
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

yes

triggers when inserted values for one or more columns fall within a specified range

Using read-only observers

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

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 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

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

using MemoryDB

User concepts

Access control

Password-based authentication

Access rights for users and roles on table level

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More resources

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