DBMS > Hawkular Metrics vs. HEAVY.AI vs. ITTIA vs. LokiJS

System Properties Comparison Hawkular Metrics vs. HEAVY.AI vs. ITTIA vs. LokiJS

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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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Edge Time Series DBMS with Real-Time Stream Processing

In-memory JavaScript DBMS

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.44
Rank	#267	Overall
	#23	Time Series DBMS

Score	0.49
Rank	#260	Overall
	#42	Document stores

Website

www.hawkular.org

github.com/heavyai/heavydb
www.heavy.ai

www.ittia.com

github.com/techfort/LokiJS

Technical documentation

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

docs.heavy.ai

techfort.github.io/LokiJS

Developer

Community supported by Red Hat

HEAVY.AI, Inc.

ITTIA L.L.C.

Initial release

2014

2016

2007

2014

Current release

5.10, January 2022

8.7

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

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++ and CUDA

C and C++

JavaScript

Server operating systems

Linux
OS X
Windows

Linux

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

schema-free

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

via views

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API
WebSocket

JavaScript API

Supported programming languages

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

yes

View functions in JavaScript

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

yes

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

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

atomic operations within a single collection possible

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

Database file passwords

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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