DBMS > Atos Standard Common Repository vs. Hawkular Metrics vs. InterSystems Caché vs. Lovefield vs. TimesTen

System Properties Comparison Atos Standard Common Repository vs. Hawkular Metrics vs. InterSystems Caché vs. Lovefield vs. TimesTen

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

A multi-model DBMS and application server

Embeddable relational database for web apps written in pure JavaScript

In-Memory RDBMS compatible to Oracle

Primary database model

Document store
Key-value store

Time Series DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.hawkular.org

www.intersystems.com/products/cache

google.github.io/lovefield

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

docs.intersystems.com

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.oracle.com/database/timesten-18.1

Developer

Atos Convergence Creators

Community supported by Red Hat

InterSystems

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2014

1997

2014

1998

Current release

1703

2018.1.4, May 2020

2.1.12, February 2017

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

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

JavaScript

Server operating systems

Linux

Linux
OS X
Windows

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

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

tested with Chrome, Firefox, IE, Safari

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

depending on used data model

yes

Typing

predefined data types such as float or date

optional

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

LDAP

HTTP REST

.NET Client API
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages with LDAP bindings

Go
Java
Python
Ruby

C#
C++
Java

JavaScript

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

via Hawkular Alerting

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

LDAP bind authentication

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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