DBMS > Hawkular Metrics vs. Oracle Berkeley DB vs. RethinkDB vs. STSdb

System Properties Comparison Hawkular Metrics vs. Oracle Berkeley DB vs. RethinkDB vs. STSdb

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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.

Widely used in-process key-value store

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Key-value store

Secondary database models

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	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.hawkular.org

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

rethinkdb.com

github.com/STSSoft/STSdb4

Technical documentation

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

docs.oracle.com/cd/E17076_05/html/index.html

rethinkdb.com/docs

Developer

Community supported by Red Hat

Oracle

originally developed by Sleepycat, which was acquired by Oracle

The Linux Foundation

since July 2017

STS Soft SC

Initial release

2014

1994

2009

2011

Current release

18.1.40, May 2020

2.4.1, August 2020

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial license available

Open Source

Apache Version 2

Open Source

GPLv2, commercial license available

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, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

primitive types and user defined types (classes)

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP REST

.NET Client API

Supported programming languages

Go
Java
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C#
Java

Server-side scripts

Stored procedures

Triggers

yes

via Hawkular Alerting

yes

only for the SQL API

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes

users and table-level permissions

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