DBMS > Alibaba Cloud TSDB vs. ITTIA vs. Microsoft Azure Data Explorer vs. ToroDB

System Properties Comparison Alibaba Cloud TSDB vs. ITTIA vs. Microsoft Azure Data Explorer vs. ToroDB

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Editorial information provided by DB-Engines

Name

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

Description

A stable, reliable, and cost-effective online high-performance time series database service

Edge Time Series DBMS with Real-Time Stream Processing

Fully managed big data interactive analytics platform

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

Primary database model

Time Series DBMS

Relational DBMS

column oriented

Document store

Secondary database models

Relational DBMS

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	0.24
Rank	#306	Overall
	#26	Time Series DBMS

Score	0.39
Rank	#273	Overall
	#24	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.alibabacloud.com/product/hitsdb

www.ittia.com

azure.microsoft.com/services/data-explorer

github.com/torodb/server

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

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

Developer

Alibaba

ITTIA L.L.C.

Microsoft

8Kdata

Initial release

2007

2019

2016

Current release

8.7

cloud service with continuous releases

License

Commercial or Open Source

commercial

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 and C++

Java

Server operating systems

hosted

Linux
Windows

hosted

All OS with a Java 7 VM

Data scheme

schema-free

Fixed schema

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

Numeric data and Strings

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

yes

all fields are automatically indexed

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

HTTP REST

JDBC
ODBC
RESTful HTTP API
WebSocket

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

Supported programming languages

Java

C
C++
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

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

Sharding

Implicit feature of the cloud service

Sharding

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.

Source-replica replication

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Access managed on TSDB instance level

Database file passwords

Azure Active Directory Authentication

Access rights for users and roles

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