DBMS > Amazon Neptune vs. Hypertable vs. OrientDB vs. SQLite vs. Valentina Server

System Properties Comparison Amazon Neptune vs. Hypertable vs. OrientDB vs. SQLite vs. Valentina Server

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

An open source BigTable implementation based on distributed file systems such as Hadoop

Multi-model DBMS (Document, Graph, Key/Value)

Widely used embeddable, in-process RDBMS

Object-relational database and reports server

Primary database model

Graph DBMS
RDF store

Wide column store

Document store
Graph DBMS
Key-value store

Relational DBMS

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	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

aws.amazon.com/neptune

orientdb.org

www.sqlite.org

www.valentina-db.net

Technical documentation

aws.amazon.com/neptune/developer-resources

www.orientdb.com/docs/last/index.html

www.sqlite.org/docs.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon

Hypertable Inc.

OrientDB LTD; CallidusCloud; SAP

Dwayne Richard Hipp

Paradigma Software

Initial release

2017

2009

2010

2000

1999

Current release

0.9.8.11, March 2016

3.2.29, March 2024

3.46.0 (23 May 2024), May 2024

5.7.5

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

Open Source

Apache version 2

Open Source

Public Domain

commercial

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++

Java

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

All OS with a Java JDK (>= JDK 6)

server-less

Linux
OS X
Windows

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like query language, no joins

yes

SQL-92 is not fully supported

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

C++ API
Thrift

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C++
Java
Perl
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

Java, Javascript

yes

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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.

selectable replication factor on file system level

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationship 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

via file-system locks

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)

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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