DBMS > Microsoft Azure AI Search vs. OpenTenBase vs. Sadas Engine vs. Tarantool

System Properties Comparison Microsoft Azure AI Search vs. OpenTenBase vs. Sadas Engine vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

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

An enterprise-level distributed HTAP open source database based on PostgreSQL

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Search engine

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Vector DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	0.08
Rank	#370	Overall
	#156	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

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

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

www.sadasengine.com

www.tarantool.io

Technical documentation

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

docs.opentenbase.org
docs.opentenbase.org/en

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.tarantool.io/en/doc

Developer

Microsoft

OpenAtom Foundation, previously Tencent

SADAS s.r.l.

Initial release

2015

2006

2008

Current release

2.5, January 2024

8.0

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

BSD-3

commercial

free trial version available

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++

C and C++

Server operating systems

hosted

Linux

AIX
Linux
Windows

BSD
Linux
macOS

Data scheme

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

yes

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Open binary protocol

Supported programming languages

C#
Java
JavaScript
Python

C
Go
Java
PHP
Python

.Net
C
C#
C++
Groovy
Java
PHP
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

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

horizontal partitioning

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

yes

none

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

managed by 'Learn by Usage'

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

using Azure authentication

Access rights for users, groups and roles 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

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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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