DBMS > Hypertable vs. Microsoft Azure Data Explorer vs. RDF4J vs. ToroDB

System Properties Comparison Hypertable vs. Microsoft Azure Data Explorer vs. RDF4J vs. ToroDB

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

Fully managed big data interactive analytics platform

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

column oriented

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

azure.microsoft.com/services/data-explorer

rdf4j.org

github.com/torodb/server

Technical documentation

docs.microsoft.com/en-us/azure/data-explorer

rdf4j.org/documentation

Developer

Hypertable Inc.

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

8Kdata

Initial release

2009

2019

2004

2016

Current release

0.9.8.11, March 2016

cloud service with continuous releases

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL-V3

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

C++

Java

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Linux
OS X
Unix
Windows

All OS with a Java 7 VM

Data scheme

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

yes

string, integer, double, boolean, date, object_id

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

restricted

only exact value or prefix value scans

all fields are automatically indexed

yes

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

APIs and other access methods

C++ API
Thrift

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Java
PHP
Python

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

User concepts

Access control

Azure Active Directory Authentication

Access rights for users and roles

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