DBMS > Amazon Neptune vs. OpenMLDB vs. Postgres-XL vs. searchxml

System Properties Comparison Amazon Neptune vs. OpenMLDB vs. Postgres-XL vs. searchxml

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

An open-source machine learning database that provides a feature platform for training and inference

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

aws.amazon.com/neptune

openmldb.ai

www.postgres-xl.org

www.searchxml.net/category/products

Technical documentation

aws.amazon.com/neptune/developer-resources

openmldb.ai/docs/zh/main

www.postgres-xl.org/documentation

www.searchxml.net/support/handouts

Developer

Amazon

4 Paradigm Inc.

informationpartners gmbh

Initial release

2017

2020

2014

since 2012, originally named StormDB

2015

Current release

2024-2 February 2024

10 R1, October 2018

1.0

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

commercial

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++, Java, Scala

C++

Server operating systems

hosted

Linux

Linux
macOS

Windows

Data scheme

schema-free

Fixed schema

yes

schema-free

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
SQLAlchemy

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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.

Source-replica replication

yes

sychronisation to multiple collections

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

MVCC

multiple readers, single writer

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

Domain, group and role-based access control at the document level and for application services

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