DBMS > EsgynDB vs. Microsoft Azure AI Search vs. PostgreSQL vs. Riak KV vs. SWC-DB

System Properties Comparison EsgynDB vs. Microsoft Azure AI Search vs. PostgreSQL vs. Riak KV vs. SWC-DB

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Distributed, fault tolerant key-value store

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Search engine

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Wide column store

Secondary database models

Vector DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.esgyn.cn

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

www.postgresql.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

www.postgresql.org/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Esgyn

Microsoft

PostgreSQL Global Development Group

www.postgresql.org/developer

OpenSource, formerly Basho Technologies

Alex Kashirin

Initial release

2015

1989

1989: Postgres, 1996: PostgreSQL

2009

2020

Current release

16.3, May 2024

3.2.0, December 2022

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++, Java

Erlang

C++

Server operating systems

Linux

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X

Linux

Data scheme

yes

schema-free

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
Java
JavaScript
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C++

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Implicit feature of the cloud service

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

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

fine grained access rights according to SQL-standard

yes, using Riak Security

More information provided by the system vendor

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