DBMS > HBase vs. Microsoft SQL Server vs. MonetDB vs. Postgres-XL vs. RisingWave

System Properties Comparison HBase vs. Microsoft SQL Server vs. MonetDB vs. Postgres-XL vs. RisingWave

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

hbase.apache.org

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

www.monetdb.org

www.postgres-xl.org

www.risingwave.com/database

Technical documentation

hbase.apache.org/book.html

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

www.monetdb.org/Documentation

www.postgres-xl.org/documentation

docs.risingwave.com/docs/current/intro

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

MonetDB BV

RisingWave Labs

Initial release

2008

1989

2004

2014

since 2012, originally named StormDB

2022

Current release

2.3.4, January 2021

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

10 R1, October 2018

1.2, September 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

Open Source

Mozilla public license

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Rust

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Docker
Linux
macOS

Data scheme

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

Standard SQL-types and JSON

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

distributed, parallel query execution

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

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

JDBC
PostgreSQL wire protocol

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

yes, in SQL, C, R

user defined functions

UDFs in Python or Java

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 via remote tables

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

MVCC

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

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Users and Roles

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