DBMS > Amazon Neptune vs. Cubrid vs. RocksDB vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Cubrid vs. RocksDB vs. Spark SQL

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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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/neptune

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

rocksdb.org

spark.apache.org/sql

Technical documentation

aws.amazon.com/neptune/developer-resources

cubrid.org/manuals

github.com/facebook/rocksdb/wiki

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

Developer

Amazon

CUBRID Corporation, CUBRID Foundation

Facebook, Inc.

Apache Software Foundation

Initial release

2017

2008

2013

2014

Current release

11.0, January 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Apache 2.0

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

C++

Scala

Server operating systems

hosted

Linux
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
OLE DB

C++ API
Java API

JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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 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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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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