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

System Properties Comparison DataFS vs. Datomic vs. Google Cloud Datastore 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.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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.

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	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

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

Website

newdatabase.com

www.datomic.com

cloud.google.com/datastore

www.hawkular.org

github.com/STSSoft/STSdb4

Technical documentation

dev.mobiland.com/Overview.xsp

docs.datomic.com

cloud.google.com/datastore/docs

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

Developer

Mobiland AG

Cognitect

Google

Community supported by Red Hat

STS Soft SC

Initial release

2018

2012

2008

2014

2011

Current release

1.1.263, October 2022

1.0.6735, June 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

limited edition free

commercial

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

Java, Clojure

Java

Server operating systems

Windows

All OS with a Java VM

hosted

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)

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

RESTful HTTP API

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

HTTP REST

.NET Client API

Supported programming languages

.Net
C
C#
C++
VB.Net

Clojure
Java

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

Go
Java
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

using Google App Engine

Triggers

no, except callback-events from server when changes happened

By using transaction functions

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

But extensive use of caching in the application peers

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

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.

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

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

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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