DBMS > Hypertable vs. MarkLogic vs. Microsoft SQL Server vs. NSDb vs. OpenEdge

System Properties Comparison Hypertable vs. MarkLogic vs. Microsoft SQL Server vs. NSDb vs. OpenEdge

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

Operational and transactional Enterprise NoSQL database

Microsofts flagship relational DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Application development environment with integrated database management system

Primary database model

Wide column store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Website

www.marklogic.com

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

nsdb.io

www.progress.com/openedge

Technical documentation

docs.marklogic.com

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

nsdb.io/Architecture

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Hypertable Inc.

MarkLogic Corp.

Microsoft

Progress Software Corporation

Initial release

2009

2001

1989

2017

1984

Current release

0.9.8.11, March 2016

11.0, December 2022

SQL Server 2022, November 2022

OpenEdge 12.2, March 2020

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java, Scala

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

Linux
Windows

Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

restricted

only exact value or prefix value scans

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL92

yes

SQL-like query language

yes

close to SQL 92

APIs and other access methods

C++ API
Thrift

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)

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

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

Java
Scala

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes

yes, but depending on the SQL-Server Edition

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Users and groups

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