DBMS > Hawkular Metrics vs. SAP IQ vs. TimescaleDB vs. TimesTen vs. ToroDB

System Properties Comparison Hawkular Metrics vs. SAP IQ vs. TimescaleDB vs. TimesTen vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB 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.

Columnar RDBMS optimized for Big Data analytics

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

In-Memory RDBMS compatible to Oracle

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

column-oriented

Secondary database models

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	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.hawkular.org

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.timescale.com

www.oracle.com/database/technologies/related/timesten.html

github.com/torodb/server

Technical documentation

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

help.sap.com/docs/SAP_IQ

docs.timescale.com

docs.oracle.com/database/timesten-18.1

Developer

Community supported by Red Hat

SAP, formerly Sybase

Timescale

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

8Kdata

Initial release

2014

1994

2017

1998

2016

Current release

16.1 SPS04, April 2019

2.15.0, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

Open Source

AGPL-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.

Implementation language

Java

Server operating systems

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

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

yes

string, integer, double, boolean, date, object_id

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

full PostgreSQL SQL syntax

yes

APIs and other access methods

HTTP REST

ADO.NET
JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

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

PL/SQL

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes, across time and space (hash partitioning) attributes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

SAP/Sybase Replication Server

Source-replica replication with hot standby and reads on replicas

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

by means of logfiles and checkpoints

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

Access rights for users and roles

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