DBMS > EsgynDB vs. Hawkular Metrics vs. HBase vs. RocksDB

System Properties Comparison EsgynDB vs. Hawkular Metrics vs. HBase vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

Technical documentation

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

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

Developer

Esgyn

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

Initial release

2015

2014

2008

2013

Current release

2.3.4, January 2021

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

BSD

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

C++, Java

Java

C++

Server operating systems

Linux

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

yes

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP REST

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Coprocessors in Java

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

yes

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

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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