DBMS > MarkLogic vs. Microsoft SQL Server vs. NCache vs. Oracle Berkeley DB

System Properties Comparison MarkLogic vs. Microsoft SQL Server vs. NCache vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Operational and transactional Enterprise NoSQL database

Microsofts flagship relational DBMS

Open-Source and Enterprise in-memory Key-Value Store

Widely used in-process key-value store

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

www.marklogic.com

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

www.alachisoft.com/ncache

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

Technical documentation

docs.marklogic.com

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

www.alachisoft.com/resources/docs

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

Developer

MarkLogic Corp.

Microsoft

Alachisoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2001

1989

2005

1994

Current release

11.0, December 2022

SQL Server 2022, November 2022

5.3.3, April 2024

18.1.40, May 2020

License

Commercial or Open Source

commercial

restricted free version is available

commercial

restricted free version is available

Open Source

Enterprise Edition available

Open Source

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#, .NET, .NET Core, Java

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

Server operating systems

Linux
OS X
Windows

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

Schema can be enforced

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL

Support of SQL

yes

SQL92

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

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

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

.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

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

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

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

yes, with selectable consistency level

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

optimistic locking and pessimistic locking

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.

yes, with Range Indexes

yes

User concepts

Access control

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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