DBMS > Graph Engine vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Sphinx vs. TerminusDB

System Properties Comparison Graph Engine vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Sphinx vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Globally distributed, horizontally scalable, multi-model database service

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Graph DBMS
Key-value store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Graph DBMS

Secondary database models

Vector DBMS

Spatial DBMS

Document store
RDF store

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

www.graphengine.io

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

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

terminusdb.com

Technical documentation

www.graphengine.io/docs/manual

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

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

terminusdb.github.io/terminusdb/#

Developer

Microsoft

Sphinx Technologies Inc.

DataChemist Ltd.

Initial release

2010

2015

2014

2001

2018

Current release

3.5.1, February 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

GPL version 2, commercial licence available

Open Source

GPL V3

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

.NET and C

C++

Prolog, Rust

Server operating systems

.NET

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
C++
F#
Visual Basic

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

JavaScript
Python

Server-side scripts

Stored procedures

yes

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

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

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

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

yes

in-memory journaling

In-memory capabilities

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

yes

User concepts

Access control

yes

using Azure authentication

Access rights can be defined down to the item level

Role-based access control

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