DBMS > EsgynDB vs. Microsoft Azure AI Search vs. searchxml vs. Vertica

System Properties Comparison EsgynDB vs. Microsoft Azure AI Search vs. searchxml vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

DBMS for structured and unstructured content wrapped with an application server

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Search engine

Native XML DBMS
Search engine

Relational DBMS

Column oriented

Secondary database models

Vector DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	5.75
Rank	#58	Overall
	#7	Search engines

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

www.esgyn.cn

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

www.searchxml.net/category/products

www.vertica.com

Technical documentation

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

www.searchxml.net/support/handouts

vertica.com/documentation

Developer

Esgyn

Microsoft

informationpartners gmbh

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

2005

Current release

1.0

12.0.3, January 2023

License

Commercial or Open Source

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

Server operating systems

Linux

hosted

Windows

Linux

Data scheme

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
Java
JavaScript
Python

C++

most other programming languages supported via APIs

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

on the application server

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Implicit feature of the cloud service

yes

sychronisation to multiple collections

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes

using Azure authentication

Domain, group and role-based access control at the document level and for application services

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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