DBMS > Graph Engine vs. NSDb vs. SWC-DB

System Properties Comparison Graph Engine vs. NSDb vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

A high performance, scalable Wide Column DBMS

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

www.graphengine.io

nsdb.io

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

www.graphengine.io/docs/manual

nsdb.io/Architecture

Developer

Microsoft

Alex Kashirin

Initial release

2010

2017

2020

Current release

0.5, April 2021

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

.NET and C

Java, Scala

C++

Server operating systems

.NET

Linux
macOS

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Proprietary protocol
Thrift

Supported programming languages

C#
C++
F#
Visual Basic

Java
Scala

C++

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

Using Apache Lucene

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

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More resources

Recent citations in the news

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