DBMS > GBase vs. IRONdb vs. Microsoft Azure Data Explorer

System Properties Comparison GBase vs. IRONdb vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

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

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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	1.49
Rank	#142	Overall
	#64	Relational DBMS

Score	2.94
Rank	#85	Overall
	#46	Relational DBMS

Website

www.gbase.cn

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/data-explorer

Technical documentation

docs.circonus.com/irondb/category/getting-started

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

Developer

General Data Technology Co., Ltd.

Circonus LLC.

Microsoft

Initial release

2004

2017

2019

Current release

GBase 8a, GBase 8s, GBase 8c

V0.10.20, January 2018

cloud service with continuous releases

License

Commercial or 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, Java, Python

C and C++

Server operating systems

Linux

hosted

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

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

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

all fields are automatically indexed

SQL

Support of SQL

Standard with numerous extensions

SQL-like query language (Circonus Analytics Query Language: CAQL)

Kusto Query Language (KQL), SQL subset

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

HTTP API

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

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

yes

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

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

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency
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

yes

Azure Active Directory Authentication

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