DBMS > Amazon Redshift vs. AnzoGraph DB vs. NSDb vs. Sphinx vs. Valentina Server

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. NSDb vs. Sphinx vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Object-relational database and reports server

Primary database model

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

nsdb.io

sphinxsearch.com

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

nsdb.io/Architecture

sphinxsearch.com/docs

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

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Sphinx Technologies Inc.

Paradigma Software

Initial release

2012

2018

2017

2001

1999

Current release

2.3, January 2021

3.5.1, February 2023

5.7.5

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

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

Java, Scala

C++

Server operating systems

hosted

Linux

Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

gRPC
HTTP REST
WebSocket

Proprietary protocol

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

Java
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed 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

Durability

Support for making data persistent

yes

Using Apache Lucene

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

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