DBMS > Hawkular Metrics vs. openGauss vs. Solr

System Properties Comparison Hawkular Metrics vs. openGauss vs. Solr

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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.

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Time Series DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.hawkular.org

gitee.com/opengauss
opengauss.org

solr.apache.org

Technical documentation

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

docs.opengauss.org/en
gitee.com/opengauss/docs

solr.apache.org/resources.html

Developer

Community supported by Red Hat

Huawei and openGauss community

Apache Software Foundation

Initial release

2014

2019

2006

Current release

3.0, March 2022

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

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, C++, Java

Java

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

ANSI SQL 2011

Solr Parallel SQL Interface

APIs and other access methods

HTTP REST

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Go
Java
Python
Ruby

C
C++
Java

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Java plugins

Triggers

yes

via Hawkular Alerting

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning (by range, list and hash)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

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

yes

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More resources

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