DBMS > Dragonfly vs. Lovefield vs. SiriDB vs. Solr vs. SWC-DB

System Properties Comparison Dragonfly vs. Lovefield vs. SiriDB vs. Solr vs. SWC-DB

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

Embeddable relational database for web apps written in pure JavaScript

Open Source Time Series DBMS

A widely used distributed, scalable search engine based on Apache Lucene

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Spatial DBMS

Time Series DBMS

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

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

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

google.github.io/lovefield

siridb.com

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

www.dragonflydb.io/docs

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

docs.siridb.com

solr.apache.org/resources.html

Developer

DragonflyDB team and community contributors

Google

Cesbit

Apache Software Foundation

Alex Kashirin

Initial release

2023

2014

2017

2006

2020

Current release

1.0, March 2023

2.1.12, February 2017

9.6.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache 2.0

Open Source

MIT License

Open Source

Apache Version 2

Open Source

GPL V3

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++

JavaScript

Java

C++

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

scheme-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

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

yes

Numeric data

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

HTTP API

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

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

JavaScript

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

Lua

Java plugins

Triggers

publish/subscribe channels provide some trigger functionality

Using read-only observers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

ACID

optimistic locking

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

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

simple rights management via user accounts

yes

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