DBMS > chDB vs. Dragonfly vs. Drizzle vs. RethinkDB vs. SwayDB

System Properties Comparison chDB vs. Dragonfly vs. Drizzle vs. RethinkDB vs. SwayDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An embedded SQL OLAP Engine powered by ClickHouse

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	0.07
Rank	#376	Overall
	#158	Relational DBMS

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

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

github.com/chdb-io/chdb

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

rethinkdb.com

swaydb.simer.au

Technical documentation

doc.chdb.io

www.dragonflydb.io/docs

rethinkdb.com/docs

Developer

DragonflyDB team and community contributors

Drizzle project, originally started by Brian Aker

The Linux Foundation

since July 2017

Simer Plaha

Initial release

2023

2008

2009

2018

Current release

1.0, March 2023

7.2.4, September 2012

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSL 1.1

Open Source

GNU GPL

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

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++

Scala

Server operating systems

server-less

Linux

FreeBSD
Linux
OS X

Linux
OS X
Windows

Data scheme

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

string, binary, float, bool, date, geometry

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

Close to ANSI SQL (SQL/JSON + extensions)

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC

Supported programming languages

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

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++
Java
PHP

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Kotlin
Scala

Server-side scripts

Stored procedures

Lua

Triggers

publish/subscribe channels provide some trigger functionality

hooks for callbacks inside the server can be used.

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Atomic single-document operations

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

MVCC based

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

Password-based authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes

users and table-level permissions

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More resources

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4 August 2014, Paul Andlinger

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