DBMS > Hyprcubd vs. Microsoft Azure Data Explorer vs. Realm vs. SpaceTime

System Properties Comparison Hyprcubd vs. Microsoft Azure Data Explorer vs. Realm vs. SpaceTime

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Serverless Time Series DBMS

Fully managed big data interactive analytics platform

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

Relational DBMS

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

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

hyprcubd.com (offline)

azure.microsoft.com/services/data-explorer

realm.io

www.mireo.com/spacetime

Technical documentation

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

realm.io/docs

Developer

Hyprcubd, Inc.

Microsoft

Realm, acquired by MongoDB in May 2019

Mireo

Initial release

2019

2014

2020

Current release

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

commercial

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++

Server operating systems

hosted

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

A subset of ANSI SQL is implemented

APIs and other access methods

gRPC (https)

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

RESTful HTTP API

Supported programming languages

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

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Python

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

runs within the applications so server-side scripts are unnecessary

Triggers

yes

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

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Fixed-grid hypercubes

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.

none

Real-time block device replication (DRBD)

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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.

yes

In-Memory realm

User concepts

Access control

token access

Azure Active Directory Authentication

yes

More information provided by the system vendor

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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