DBMS > Dragonfly vs. etcd vs. Oracle vs. Riak TS

System Properties Comparison Dragonfly vs. etcd vs. Oracle vs. Riak TS

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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

A distributed reliable key-value store

Widely used RDBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

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

etcd.io
github.com/etcd-io/etcd

www.oracle.com/database

Technical documentation

www.dragonflydb.io/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.oracle.com/en/database

www.tiot.jp/riak-docs/riak/ts/latest

Developer

DragonflyDB team and community contributors

Oracle

Open Source, formerly Basho Technologies

Initial release

2023

1980

2015

Current release

1.0, March 2023

3.4, August 2019

23c, September 2023

3.0.0, September 2022

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

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 and C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X

Data scheme

scheme-free

schema-free

yes

Schemaless in JSON and XML columns

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.

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

with proprietary extensions

yes, limited

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC
JSON over HTTP

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API
Native Erlang Interface

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
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Lua

PL/SQL

also stored procedures in Java possible

Erlang

Triggers

publish/subscribe channels provide some trigger functionality

yes, watching key changes

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Password-based authentication

fine grained access rights according to SQL-standard

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