DBMS > Hawkular Metrics vs. RRDtool vs. StarRocks vs. Transwarp ArgoDB

System Properties Comparison Hawkular Metrics vs. RRDtool vs. StarRocks vs. Transwarp ArgoDB

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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.

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Search engine

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Score	0.12
Rank	#348	Overall
	#151	Relational DBMS

Website

www.hawkular.org

oss.oetiker.ch/rrdtool

www.starrocks.io

www.transwarp.cn/en/product/argodb

Technical documentation

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

oss.oetiker.ch/rrdtool/doc

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

Community supported by Red Hat

Tobias Oetiker

The Linux Foundation

since Feb 2023

Transwarp

Initial release

2014

1999

2020

Current release

1.8.0, 2022

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL V2 and FLOSS

Open Source

Apache Version 2.0

commercial

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

Implementations in Java (e.g. RRD4J) and C# available

C++, Java

Server operating systems

Linux
OS X
Windows

HP-UX
Linux

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

in-process shared library
Pipes

JDBC
MySQL protocol

Supported programming languages

Go
Java
Python
Ruby

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

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

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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