DBMS > Interbase vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. MySQL vs. Spark SQL

System Properties Comparison Interbase vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. MySQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Light-weight proven RDBMS

originally from Borland

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

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Vector DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

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	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.embarcadero.com/products/interbase

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

azure.microsoft.com/services/cosmos-db

www.mysql.com

spark.apache.org/sql

Technical documentation

docs.embarcadero.com/products/interbase

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

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Embarcadero

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

1984

2015

2014

1995

2014

Current release

InterBase 2020, December 2019

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Scala

Server operating systems

Android
iOS
Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

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.

export as XML data possible

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C#
Java
JavaScript
Python

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Interbase procedure and trigger language

JavaScript

yes

proprietary syntax

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

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

yes

using Azure authentication

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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