DBMS > GridGain vs. Hawkular Metrics vs. Prometheus vs. Warp 10

System Properties Comparison GridGain vs. Hawkular Metrics vs. Prometheus vs. Warp 10

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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.

Open-source Time Series DBMS and monitoring system

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

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

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	7.92
Rank	#51	Overall
	#2	Time Series DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

Technical documentation

www.gridgain.com/docs/index.html

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

prometheus.io/docs

www.warp10.io/content/02_Getting_started

Developer

GridGain Systems, Inc.

Community supported by Red Hat

SenX

Initial release

2007

2014

2015

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache License 2.0

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

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

yes

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

Import of XML data possible

Secondary indexes

yes

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

RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

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

yes

WarpScript

Triggers

yes (cache interceptors and events)

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

based on Cassandra

yes

by Federation

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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

Security Hooks for custom implementations

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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