DBMS > Microsoft Azure AI Search vs. Microsoft SQL Server vs. Tarantool vs. TerminusDB

System Properties Comparison Microsoft Azure AI Search vs. Microsoft SQL Server vs. Tarantool vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

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

Primary database model

Search engine

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

Secondary database models

Vector DBMS

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

with Tarantool/GIS extension

Document store
RDF store

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

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

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

www.microsoft.com/en-us/sql-server

www.tarantool.io

terminusdb.com

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

www.tarantool.io/en/doc

terminusdb.github.io/terminusdb/#

Developer

Microsoft

DataChemist Ltd.

Initial release

2015

1989

2008

2018

Current release

SQL Server 2022, November 2022

2.10.0, May 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Prolog, Rust

Server operating systems

hosted

Linux
Windows

BSD
Linux
macOS

Linux

Data scheme

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

SQL

Support of SQL

yes

Full-featured ANSI SQL support

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Open binary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
Java
JavaScript
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

JavaScript
Python

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Lua, C and SQL stored procedures

yes

Triggers

yes

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

in-memory journaling

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

using Azure authentication

fine grained access rights according to SQL-standard

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

Role-based access control

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