DBMS > DataFS vs. Google Cloud Datastore vs. Graphite vs. Hawkular Metrics vs. STSdb

System Properties Comparison DataFS vs. Google Cloud Datastore vs. Graphite vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

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

Website

newdatabase.com

cloud.google.com/datastore

github.com/graphite-project/graphite-web

www.hawkular.org

github.com/STSSoft/STSdb4

Technical documentation

dev.mobiland.com/Overview.xsp

cloud.google.com/datastore/docs

graphite.readthedocs.io

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

Developer

Mobiland AG

Google

Chris Davis

Community supported by Red Hat

STS Soft SC

Initial release

2018

2008

2006

2014

2011

Current release

1.1.263, October 2022

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Python

Java

Server operating systems

Windows

hosted

Linux
Unix

Linux
OS X
Windows

Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

Numeric data only

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like query language (GQL)

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

HTTP API
Sockets

HTTP REST

.NET Client API

Supported programming languages

.Net
C
C#
C++
VB.Net

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

JavaScript (Node.js)
Python

Go
Java
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

using Google App Engine

Triggers

no, except callback-events from server when changes happened

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

selectable replication factor

based on Cassandra

none

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

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.

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Concurrency

Support for concurrent manipulation of data

yes

locking

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.

User concepts

Access control

Windows-Profile

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

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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