DBMS > Hawkular Metrics vs. Hive vs. RDF4J

System Properties Comparison Hawkular Metrics vs. Hive vs. RDF4J

Please select another system to include it in the comparison.

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Time Series DBMS

Relational DBMS

RDF store

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

Score	0.06
Rank	#303	Overall
	#26	Time Series DBMS

Score	86.05
Rank	#14	Overall
	#9	Relational DBMS

Score	0.40
Rank	#219	Overall
	#10	RDF stores

Website

www.hawkular.org

hive.apache.org

rdf4j.org

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

docs.rdf4j.org

Developer

Community supported by Red Hat

Apache Software Foundation info

initially developed by Facebook

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2014

2012

2004

Current release

3.1.2, August 2019

License

Commercial or Open Source

Open Source info

Apache 2.0

Open Source info

Apache Version 2

Open Source info

Eclipse Distribution License (EDL), v1.0.

Cloud-based only info

Only available as a cloud service

DBaaS offerings (sponsored links) info

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

XML support info

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift

Java API
RIO info

RDF Input/Output
Sail API
SeRQL info

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
Python
Ruby

C++
Java
PHP
Python

Java
PHP
Python

Server-side scripts info

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods info

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Replication methods info

Methods for redundantly storing data on multiple nodes

selectable replication factor info

based on Cassandra

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts info

Methods to ensure consistency in a distributed system

Eventual Consistency info

based on Cassandra
Immediate Consistency info

based on Cassandra

Eventual Consistency

Foreign keys info

Referential integrity

Transaction concepts info

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

ACID

Isolation support depends on the API used

Concurrency info

Support for concurrent manipulation of data

yes

Durability info

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities info

Is there an option to define some or all structures to be held in-memory only.

User concepts info

Access control

Access rights for users, groups and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.