DBMS > Amazon Neptune vs. Brytlyt vs. PouchDB vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Brytlyt vs. PouchDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

JavaScript DBMS with an API inspired by CouchDB

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

Score	2.82
Rank	#108	Overall
	#6	Graph DBMS
	#4	RDF stores

Score	0.33
Rank	#269	Overall
	#125	Relational DBMS

Score	3.09
Rank	#98	Overall
	#18	Document stores

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

aws.amazon.com/neptune

www.brytlyt.com

pouchdb.com

spark.apache.org/sql

Technical documentation

aws.amazon.com/neptune/developer-resources

pouchdb.com/guides

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Brytlyt

Apache Software Foundation

Initial release

2017

2016

2012

2014

Current release

0.9, October 2016

7.1.1, June 2019

3.2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++ and CUDA

JavaScript

Scala

Server operating systems

hosted

Linux
OS X
Windows

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

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via views

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin 3.3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

JavaScript

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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