DBMS > DataFS vs. Microsoft Azure AI Search vs. Tarantool

System Properties Comparison DataFS vs. Microsoft Azure AI Search vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

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

Primary database model

Object oriented DBMS

Search engine

Document store
Key-value store
Relational DBMS

Secondary database models

Graph DBMS

Vector DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	5.75
Rank	#58	Overall
	#7	Search engines

Score	1.63
Rank	#139	Overall
	#25	Document stores
	#24	Key-value stores
	#65	Relational DBMS

Website

newdatabase.com

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

www.tarantool.io

Technical documentation

dev.mobiland.com/Overview.xsp

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

www.tarantool.io/en/doc

Developer

Mobiland AG

Microsoft

Initial release

2018

2015

2008

Current release

1.1.263, October 2022

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

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

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 and C++

Server operating systems

Windows

hosted

BSD
Linux
macOS

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

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

Typing

predefined data types such as float or date

yes

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

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

SQL

Support of SQL

Full-featured ANSI SQL support

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

Open binary protocol

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
Java
JavaScript
Python

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

Server-side scripts

Stored procedures

Lua, C and SQL stored procedures

Triggers

no, except callback-events from server when changes happened

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

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

Implicit feature of the cloud service

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

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

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

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Windows-Profile

yes

using Azure authentication

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

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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