DBMS > Hawkular Metrics vs. Microsoft SQL Server vs. Spark SQL vs. SpatiaLite vs. TimescaleDB

System Properties Comparison Hawkular Metrics vs. Microsoft SQL Server vs. Spark SQL vs. SpatiaLite vs. TimescaleDB

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.

Microsofts flagship relational DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Spatial extension of SQLite

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.hawkular.org

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

spark.apache.org/sql

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

www.timescale.com

Technical documentation

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

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

spark.apache.org/docs/latest/sql-programming-guide.html

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

docs.timescale.com

Developer

Community supported by Red Hat

Microsoft

Apache Software Foundation

Alessandro Furieri

Timescale

Initial release

2014

1989

2014

2008

2017

Current release

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

5.0.0, August 2020

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

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

Open Source

Apache 2.0

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++

Scala

C++

Server operating systems

Linux
OS X
Windows

Linux
Windows

Linux
OS X
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

yes

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

APIs and other access methods

HTTP REST

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

JDBC
ODBC

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

Supported programming languages

Go
Java
Python
Ruby

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

Java
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

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

yes, utilizing Spark Core

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes, but depending on the SQL-Server Edition

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

fine grained access rights according to SQL-standard

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