DBMS > Amazon Neptune vs. Cubrid vs. HarperDB vs. mSQL vs. Sphinx

System Properties Comparison Amazon Neptune vs. Cubrid vs. HarperDB vs. mSQL vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Primary database model

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/neptune

cubrid.com (korean)
cubrid.org (english)

www.harperdb.io

hughestech.com.au/products/msql

sphinxsearch.com

Technical documentation

aws.amazon.com/neptune/developer-resources

cubrid.org/manuals

docs.harperdb.io/docs

sphinxsearch.com/docs

Developer

Amazon

CUBRID Corporation, CUBRID Foundation

HarperDB

Hughes Technologies

Sphinx Technologies Inc.

Initial release

2017

2008

2017

1994

2001

Current release

11.0, January 2021

3.1, August 2021

4.4, October 2021

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

free licenses can be provided

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

C, C++, Java

Node.js

C++

Server operating systems

hosted

Linux
Windows

Linux
OS X

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

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

yes

SQL-like data manipulation statements

A subset of ANSI SQL is implemented

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

SQL-like query language (SphinxQL)

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC

Proprietary protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Delphi
Java
Perl
PHP
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

Custom Functions

since release 3.1

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

none

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 within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Source-replica replication

yes

the nodes on which a table resides can be defined

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

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

yes, using LMDB

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.

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

Access rights for users and roles

More information provided by the system vendor

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More resources

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