DBMS > Cachelot.io vs. DolphinDB vs. Microsoft Azure Data Explorer vs. Microsoft SQL Server

System Properties Comparison Cachelot.io vs. DolphinDB vs. Microsoft Azure Data Explorer vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Fully managed big data interactive analytics platform

Microsofts flagship relational DBMS

Primary database model

column oriented

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

Document store
Graph DBMS
Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

cachelot.io

www.dolphindb.com

azure.microsoft.com/services/data-explorer

www.microsoft.com/en-us/sql-server

Technical documentation

docs.dolphindb.cn/en/help200/index.html

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

learn.microsoft.com/en-US/sql/sql-server

Developer

DolphinDB, Inc

Microsoft

Initial release

2015

2018

2019

1989

Current release

v2.00.4, January 2022

cloud service with continuous releases

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

free community version available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Server operating systems

FreeBSD
Linux
OS X

Linux
Windows

hosted

Linux
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

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

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

Memcached protocol

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

User concepts

Access control

Administrators, Users, Groups

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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