DBMS > Dragonfly vs. GBase vs. KeyDB vs. MySQL vs. OpenTSDB

System Properties Comparison Dragonfly vs. GBase vs. KeyDB vs. MySQL vs. OpenTSDB

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 RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Widely used open source RDBMS

Scalable Time Series DBMS based on HBase

Primary database model

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

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

www.gbase.cn

github.com/Snapchat/KeyDB
keydb.dev

www.mysql.com

opentsdb.net

Technical documentation

www.dragonflydb.io/docs

docs.keydb.dev

dev.mysql.com/doc

opentsdb.net/docs/build/html/index.html

Developer

DragonflyDB team and community contributors

General Data Technology Co., Ltd.

EQ Alpha Technology Ltd.

Oracle

since 2010, originally MySQL AB, then Sun

currently maintained by Yahoo and other contributors

Initial release

2023

2004

2019

1995

2011

Current release

1.0, March 2023

GBase 8a, GBase 8s, GBase 8c

8.4.0, April 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

BSD-3

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

LGPL

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.

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Implementation language

C++

C, Java, Python

C++

C and C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

scheme-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

numeric data for metrics, strings for tags

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

by using the Redis Search module

yes

SQL

Support of SQL

Standard with numerous extensions

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
C API
JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Telnet API

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

Lua

user defined functions

Lua

yes

proprietary syntax

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Optimistic locking, atomic execution of commands blocks and scripts

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

yes

simple password-based access control and ACL

Users with fine-grained authorization concept

no user groups or roles

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