DBMS > Dragonfly vs. Oracle Berkeley DB vs. RisingWave

System Properties Comparison Dragonfly vs. Oracle Berkeley DB vs. RisingWave

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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

Widely used in-process key-value store

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

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

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.58
Rank	#242	Overall
	#111	Relational DBMS

Website

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

www.oracle.com/database/technologies/related/berkeleydb.html

www.risingwave.com/database

Technical documentation

www.dragonflydb.io/docs

docs.oracle.com/cd/E17076_05/html/index.html

docs.risingwave.com/docs/current/intro

Developer

DragonflyDB team and community contributors

Oracle

originally developed by Sleepycat, which was acquired by Oracle

RisingWave Labs

Initial release

2023

1994

2022

Current release

1.0, March 2023

18.1.40, May 2020

1.2, September 2023

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

commercial license available

Open Source

Apache Version 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, Java, C++ (depending on the Berkeley DB edition)

Rust

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Docker
Linux
macOS

Data scheme

scheme-free

schema-free

yes

Typing

predefined data types such as float or date

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

Standard SQL-types and JSON

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
PostgreSQL wire protocol

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

UDFs in Python or Java

Triggers

publish/subscribe channels provide some trigger functionality

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

Durability

Support for making data persistent

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

Users and Roles

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