DBMS > Microsoft SQL Server vs. Oracle Berkeley DB vs. Trino vs. YDB

System Properties Comparison Microsoft SQL Server vs. Oracle Berkeley DB vs. Trino vs. YDB

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Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Widely used in-process key-value store

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Score	0.39
Rank	#269	Overall
	#43	Document stores
	#126	Relational DBMS

Website

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/berkeleydb.html

trino.io

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/cd/E17076_05/html/index.html

trino.io/broadcast
trino.io/docs/current

ydb.tech/en/docs

Developer

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Trino Software Foundation

Yandex

Initial release

1989

1994

2012

2020 rebranded from PrestoSQL

2019

Current release

SQL Server 2022, November 2022

18.1.40, May 2020

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

commercial license available

Open Source

Apache Version 2.0

Open Source

Apache 2.0; commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

for devlopment

Linux

Data scheme

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, 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

only with the Berkeley DB XML edition

Secondary indexes

yes

depending on connected data-source

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-like query language (YQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
RESTful HTTP API
Trino CLI

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, depending on connected data-source

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

depending on connected data-source

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Source-replica replication

depending on connected data-source

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on connected data-source

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

depending on connected data-source

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

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

SQL standard access control

Access rights defined for Yandex Cloud users

More information provided by the system vendor

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