DBMS > DataFS vs. Hawkular Metrics vs. TimescaleDB vs. YDB

System Properties Comparison DataFS vs. Hawkular Metrics vs. TimescaleDB vs. YDB

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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.

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Object oriented DBMS

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Graph DBMS

Relational DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

newdatabase.com

www.hawkular.org

www.timescale.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

dev.mobiland.com/Overview.xsp

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

docs.timescale.com

ydb.tech/en/docs

Developer

Mobiland AG

Community supported by Red Hat

Timescale

Yandex

Initial release

2018

2014

2017

2019

Current release

1.1.263, October 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0; commercial license available

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

Server operating systems

Windows

Linux
OS X
Windows

Linux

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

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

full PostgreSQL SQL syntax

SQL-like query language (YQL)

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
C
C#
C++
VB.Net

Go
Java
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

no, except callback-events from server when changes happened

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

based on Cassandra

yes, across time and space (hash partitioning) attributes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication with hot standby and reads on replicas

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate 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 capabilities

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

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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