DBMS > Hawkular Metrics vs. IRONdb vs. Microsoft SQL Server vs. SpatiaLite vs. Typesense

System Properties Comparison Hawkular Metrics vs. IRONdb vs. Microsoft SQL Server vs. SpatiaLite vs. Typesense

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Microsofts flagship relational DBMS

Spatial extension of SQLite

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

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	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	0.79
Rank	#210	Overall
	#14	Search engines

Website

www.hawkular.org

www.circonus.com/solutions/time-series-database/

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

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

typesense.org

Technical documentation

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

docs.circonus.com/irondb/category/getting-started

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

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

typesense.org/docs

Developer

Community supported by Red Hat

Circonus LLC.

Microsoft

Alessandro Furieri

Initial release

2014

2017

1989

2008

2015

Current release

V0.10.20, January 2018

SQL Server 2022, November 2022

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

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

Open Source

GPL V3

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.

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X
Windows

Linux

Linux
Windows

server-less

Linux

Data scheme

schema-free

yes

schema-free

pre-defined schema optional

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

HTTP REST

HTTP API

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

RESTful HTTP API

Supported programming languages

Go
Java
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

yes, in Lua

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

Automatic, metric affinity per node

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

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

none

Multi-source replication using RAFT

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 per node, eventual consistency across nodes

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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