DBMS > Amazon Redshift vs. JanusGraph vs. Microsoft Azure AI Search vs. Microsoft Azure Synapse Analytics vs. Sphinx

System Properties Comparison Amazon Redshift vs. JanusGraph vs. Microsoft Azure AI Search vs. Microsoft Azure Synapse Analytics vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Search-as-a-service for web and mobile app development

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Secondary database models

Vector DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

janusgraph.org

azure.microsoft.com/en-us/services/search

azure.microsoft.com/services/synapse-analytics

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/redshift

docs.janusgraph.org

learn.microsoft.com/en-us/azure/search

docs.microsoft.com/azure/synapse-analytics

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Sphinx Technologies Inc.

Initial release

2012

2017

2015

2016

2001

Current release

0.6.3, February 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence 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

Java

C++

Server operating systems

hosted

Linux
OS X
Unix
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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

restricted

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

RESTful HTTP API

ADO.NET
JDBC
ODBC

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java
Python

C#
Java
JavaScript
Python

C#
Java
PHP

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graphs

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

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

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

User authentification and security via Rexster Graph Server

yes

using Azure authentication

yes

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