DBMS > Graph Engine vs. Kinetica vs. NSDb vs. PouchDB

System Properties Comparison Graph Engine vs. Kinetica vs. NSDb vs. PouchDB

Please select another system to include it in the comparison.

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

Fully vectorized database across both GPUs and CPUs

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

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

Technical documentation

www.graphengine.io/docs/manual

docs.kinetica.com

nsdb.io/Architecture

pouchdb.com/guides

Developer

Microsoft

Kinetica

Apache Software Foundation

Initial release

2010

2012

2017

2012

Current release

7.1, August 2021

7.1.1, June 2019

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2.0

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

.NET and C

C, C++

Java, Scala

JavaScript

Server operating systems

.NET

Linux

Linux
macOS

server-less, requires a JavaScript environment (browser, Node.js)

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

yes

all fields are automatically indexed

yes

via views

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C#
C++
F#
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Java
Scala

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions

View functions in JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

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

yes

Using Apache Lucene

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

show all

Recent citations in the news

Trinity
30 October 2010, Microsoft

Open source Microsoft Graph Engine takes on Neo4j
13 February 2017, InfoWorld

IBM releases Graph, a service that can outperform SQL databases
27 July 2016, GeekWire

Aerospike Is Now a Graph Database, Too
21 June 2023, Datanami

The graph analytics landscape 2019
27 February 2019, Data Science Central

provided by Google News

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica: AI is a ‘killer app’ for data analytics
2 May 2023, Blocks & Files

Kinetica Taps Dell for Hardware
12 June 2018, Finovate

provided by Google News

Getting Started with PouchDB Client-Side JavaScript Database
7 September 2016, SitePoint

Create Offline Web Apps Using Service Workers & PouchDB
7 March 2017, SitePoint

Building an Offline First App with PouchDB
10 March 2014, SitePoint

provided by Google News

Share this page