DBMS > Hawkular Metrics vs. Hive vs. Microsoft SQL Server vs. SpatiaLite

System Properties Comparison Hawkular Metrics vs. Hive vs. Microsoft SQL Server vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Microsofts flagship relational DBMS

Spatial extension of SQLite

Primary database model

Time Series DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.hawkular.org

hive.apache.org

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

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

cwiki.apache.org/confluence/display/Hive/Home

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

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Community supported by Red Hat

Apache Software Foundation

initially developed by Facebook

Microsoft

Alessandro Furieri

Initial release

2014

2012

1989

2008

Current release

3.1.3, April 2022

SQL Server 2022, November 2022

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
Windows

server-less

Data scheme

schema-free

yes

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift

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

Supported programming languages

Go
Java
Python
Ruby

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

selectable replication factor

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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.

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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