DBMS > Hawkular Metrics vs. Heroic vs. Linter vs. OpenTenBase

System Properties Comparison Hawkular Metrics vs. Heroic vs. Linter vs. OpenTenBase

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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.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

RDBMS for high security requirements

An enterprise-level distributed HTAP open source database based on PostgreSQL

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Website

www.hawkular.org

github.com/spotify/heroic

linter.ru

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

Technical documentation

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

spotify.github.io/heroic

docs.opentenbase.org
docs.opentenbase.org/en

Developer

Community supported by Red Hat

Spotify

relex.ru

OpenAtom Foundation, previously Tencent

Initial release

2014

1990

Current release

2.5, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

BSD-3

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 and C++

Server operating systems

Linux
OS X
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
Go
Java
PHP
Python

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Source-replica replication

yes

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

Eventual Consistency
Immediate 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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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