DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. IRONdb vs. jBASE

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. IRONdb vs. jBASE

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A robust multi-value DBMS comprising development tools and middleware

Primary database model

Document store

Time Series DBMS

Multivalue DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	1.36
Rank	#157	Overall
	#3	Multivalue DBMS

Website

cloud.google.com/datastore

www.hawkular.org

www.circonus.com/solutions/time-series-database/

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

cloud.google.com/datastore/docs

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

docs.circonus.com/irondb/category/getting-started

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Google

Community supported by Red Hat

Circonus LLC.

Rocket Software (formerly Zumasys)

Initial release

2008

2014

2017

1991

Current release

V0.10.20, January 2018

5.7

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

C and C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

AIX
Linux
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

text, numeric, histograms

optional

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

SQL-like query language (GQL)

SQL-like query language (Circonus Analytics Query Language: CAQL)

Embedded SQL for jBASE in BASIC

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP REST

HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Go
Java
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Basic
Jabbascript
Java

Server-side scripts

Stored procedures

using Google App Engine

yes, in Lua

yes

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Automatic, metric affinity per node

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

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