DBMS > Derby vs. Dragonfly vs. Hive

System Properties Comparison Derby vs. Dragonfly vs. Hive

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	5.30
Rank	#66	Overall
	#36	Relational DBMS

Score	0.42
Rank	#271	Overall
	#38	Key-value stores

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Website

db.apache.org/derby

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

hive.apache.org

Technical documentation

db.apache.org/derby/manuals/index.html

www.dragonflydb.io/docs

cwiki.apache.org/confluence/display/Hive/Home

Developer

Apache Software Foundation

DragonflyDB team and community contributors

Apache Software Foundation

initially developed by Facebook

Initial release

1997

2023

2012

Current release

10.17.1.0, November 2023

1.0, March 2023

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

BSL 1.1

Open Source

Apache Version 2

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

Java

C++

Java

Server operating systems

All OS with a Java VM

Linux

All OS with a Java VM

Data scheme

yes

scheme-free

yes

Typing

predefined data types such as float or date

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC
Thrift

Supported programming languages

Java

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
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Lua

yes

user defined functions and integration of map-reduce

Triggers

yes

publish/subscribe channels provide some trigger functionality

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

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

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

fine grained access rights according to SQL-standard

Password-based authentication

Access rights for users, groups and roles

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More resources

DB-Engines blog posts

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