DBMS > Amazon Neptune vs. mSQL vs. Spark SQL vs. Sphinx

System Properties Comparison Amazon Neptune vs. mSQL vs. Spark SQL vs. Sphinx

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Graph DBMS
RDF store

Relational DBMS

Search engine

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	1.72
Rank	#136	Overall
	#66	Relational DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Score	6.72
Rank	#63	Overall
	#7	Search engines

Website

aws.amazon.com/neptune

hughestech.com.au/products/msql

spark.apache.org/sql

sphinxsearch.com

Technical documentation

aws.amazon.com/neptune/developer-resources

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

sphinxsearch.com/docs

Developer

Amazon

Hughes Technologies

Apache Software Foundation

Sphinx Technologies Inc.

Initial release

2017

1994

2014

2001

Current release

4.4, October 2021

3.2.0, October 2021

3.4.1, July 2021

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

C++

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin 3.3

JDBC
ODBC

Proprietary protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

User concepts

Access control

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

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.