DBMS > Heroic vs. Ingres vs. LeanXcale vs. Microsoft Azure Data Explorer vs. RDF4J

System Properties Comparison Heroic vs. Ingres vs. LeanXcale vs. Microsoft Azure Data Explorer vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Well established RDBMS

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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.

Primary database model

Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

column oriented

RDF store

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	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

github.com/spotify/heroic

www.actian.com/databases/ingres

www.leanxcale.com

azure.microsoft.com/services/data-explorer

rdf4j.org

Technical documentation

spotify.github.io/heroic

docs.actian.com/ingres

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

rdf4j.org/documentation

Developer

Spotify

Actian Corporation

LeanXcale

Microsoft

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

Initial release

2014

1974

originally developed at University Berkely in early 1970s

2015

2019

2004

Current release

11.2, May 2022

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

RDF Schemas

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

via Elasticsearch

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

through Apache Derby

Kusto Query Language (KQL), SQL subset

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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
Scala

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

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

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Ingres Replicator

yes

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

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

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

MVCC

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

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