DBMS > Hawkular Metrics vs. Hyprcubd vs. Spark SQL vs. STSdb vs. TerminusDB

System Properties Comparison Hawkular Metrics vs. Hyprcubd vs. Spark SQL vs. STSdb vs. TerminusDB

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Serverless Time Series DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store
RDF store

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

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

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

www.hawkular.org

hyprcubd.com (offline)

spark.apache.org/sql

github.com/STSSoft/STSdb4

terminusdb.com

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

terminusdb.github.io/terminusdb/#

Developer

Community supported by Red Hat

Hyprcubd, Inc.

Apache Software Foundation

STS Soft SC

DataChemist Ltd.

Initial release

2014

2011

2018

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Prolog, Rust

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

primitive types and user defined types (classes)

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.

Secondary indexes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language (WOQL)

APIs and other access methods

HTTP REST

gRPC (https)

JDBC
ODBC

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Go
Java
Python
Ruby

Java
Python
R
Scala

C#
Java

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes, utilizing Spark Core

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

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 journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

token access

Role-based access control

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