DBMS > Amazon Neptune vs. Oracle vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon Neptune vs. Oracle vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Widely used RDBMS

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/neptune

www.oracle.com/database

sphinxsearch.com

splicemachine.com

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.oracle.com/en/database

sphinxsearch.com/docs

splicemachine.com/how-it-works

Developer

Amazon

Oracle

Sphinx Technologies Inc.

Splice Machine

Initial release

2017

1980

2001

2014

Current release

23c, September 2023

3.5.1, February 2023

3.1, March 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license 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 and C++

C++

Java

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

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

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (SphinxQL)

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

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.

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Yes, via Full Spark Integration

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

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

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

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

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, groups and roles according to SQL-standard

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