DBMS > GridGain vs. Hawkular Metrics vs. RethinkDB vs. Riak KV

System Properties Comparison GridGain vs. Hawkular Metrics vs. RethinkDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

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

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

Distributed, fault tolerant key-value store

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Spatial DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	2.58
Rank	#105	Overall
	#20	Document stores

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

www.gridgain.com

www.hawkular.org

rethinkdb.com

Technical documentation

www.gridgain.com/docs/index.html

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

rethinkdb.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

GridGain Systems, Inc.

Community supported by Red Hat

The Linux Foundation

since July 2017

OpenSource, formerly Basho Technologies

Initial release

2007

2014

2009

Current release

GridGain 8.5.1

2.4.1, August 2020

3.2.0, December 2022

License

Commercial or Open Source

commercial, open source

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

Apache version 2, commercial enterprise edition

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++, .Net, Python, REST, SQL

Java

C++

Erlang

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

restricted

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP REST

HTTP API
Native Erlang Interface

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Erlang

Triggers

yes (cache interceptors and events)

yes

via Hawkular Alerting

Client-side triggers through changefeeds

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

range based

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

based on Cassandra

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

Role-based access control
Security Hooks for custom implementations

yes

users and table-level permissions

yes, using Riak Security

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More resources

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