DBMS > Dragonfly vs. Ehcache vs. RavenDB vs. RocksDB vs. SWC-DB

System Properties Comparison Dragonfly vs. Ehcache vs. RavenDB vs. RocksDB 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

A widely adopted Java cache with tiered storage options

Open Source Operational and Transactional Enterprise NoSQL Document Database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series 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	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

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

www.ehcache.org

ravendb.net

rocksdb.org

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

Technical documentation

www.dragonflydb.io/docs

www.ehcache.org/documentation

ravendb.net/docs

github.com/facebook/rocksdb/wiki

Developer

DragonflyDB team and community contributors

Terracotta Inc, owned by Software AG

Hibernating Rhinos

Facebook, Inc.

Alex Kashirin

Initial release

2023

2009

2010

2013

2020

Current release

1.0, March 2023

3.10.0, March 2022

5.4, July 2022

9.2.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2; commercial licenses available

Open Source

AGPL version 3, commercial license available

Open Source

BSD

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++

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

scheme-free

schema-free

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 query language (RQL)

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JCache

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

C++ API
Java 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

Java

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

C
C++
Go
Java
Perl
Python
Ruby

C++

Server-side scripts

Stored procedures

Lua

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

by using Terracotta Server

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Tunable Consistency (Strong, Eventual, Weak)

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

yes

supports JTA and can work as an XA resource

ACID, Cluster-wide transaction available

yes

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

In-memory capabilities

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

yes

User concepts

Access control

Password-based authentication

Authorization levels configured per client per database

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