DBMS > Badger vs. Dragonfly vs. NSDb vs. SiteWhere

System Properties Comparison Badger vs. Dragonfly vs. NSDb vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

M2M integration platform for persisting/querying time series data

Primary database model

Key-value store

Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

github.com/dgraph-io/badger

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

nsdb.io

github.com/sitewhere/sitewhere

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.dragonflydb.io/docs

nsdb.io/Architecture

sitewhere1.sitewhere.io/index.html

Developer

DGraph Labs

DragonflyDB team and community contributors

SiteWhere

Initial release

2017

2023

2017

2010

Current release

1.0, March 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSL 1.1

Open Source

Apache Version 2.0

Open Source

Common Public Attribution License Version 1.0

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++

Java, Scala

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

scheme-free

predefined scheme

Typing

predefined data types such as float or date

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

yes: int, bigint, decimal, string

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.

Secondary indexes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC
HTTP REST
WebSocket

HTTP REST

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

Java
Scala

Server-side scripts

Stored procedures

Lua

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

Eventual Consistency

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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 with fine-grained authorization concept

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