DBMS > Microsoft Azure Data Explorer vs. Newts vs. RDF4J vs. Sphinx

System Properties Comparison Microsoft Azure Data Explorer vs. Newts vs. RDF4J vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Fully managed big data interactive analytics platform

Time Series DBMS based on Cassandra

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

Open source search engine for searching in data from different sources, e.g. relational databases

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	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

azure.microsoft.com/services/data-explorer

opennms.github.io/newts

rdf4j.org

sphinxsearch.com

Technical documentation

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

github.com/OpenNMS/newts/wiki

rdf4j.org/documentation

sphinxsearch.com/docs

Developer

Microsoft

OpenNMS Group

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

Sphinx Technologies Inc.

Initial release

2019

2014

2004

2001

Current release

cloud service with continuous releases

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL version 2, commercial licence 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

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

RDF Schemas

yes

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

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

all fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

SQL-like query language (SphinxQL)

APIs and other access methods

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

HTTP REST
Java API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Proprietary protocol

Supported programming languages

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

Java

Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Sharding

based on Cassandra

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

The original contents of fields are not stored in the Sphinx index.

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

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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