DBMS > Microsoft Azure Data Explorer vs. MySQL vs. ScyllaDB vs. Spark SQL vs. Trafodion

System Properties Comparison Microsoft Azure Data Explorer vs. MySQL vs. ScyllaDB vs. Spark SQL vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Fully managed big data interactive analytics platform

Widely used open source RDBMS

Cassandra and DynamoDB compatible wide column store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

column oriented

Relational DBMS

Key/Value like access via memcached API

Wide column store

Relational DBMS

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

Document store
Spatial DBMS

Key-value store

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

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

azure.microsoft.com/services/data-explorer

Technical documentation

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

dev.mysql.com/doc

docs.scylladb.com

spark.apache.org/docs/latest/sql-programming-guide.html

trafodion.apache.org/documentation.html

Developer

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

ScyllaDB

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2019

1995

2015

2014

Current release

cloud service with continuous releases

8.4.0, April 2024

ScyllaDB Open Source 5.4.1, January 2024

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Open Source (AGPL), commercial license available

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

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Implementation language

C and C++

C++

Scala

C++, Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Linux

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free

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

all fields are automatically indexed

yes

cluster global secondary indices

yes

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

SQL-like DML and DDL statements (CQL)

SQL-like DML and DDL statements

yes

APIs and other access methods

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

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

yes

proprietary syntax

yes, Lua

Java Stored Procedures

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

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.

Multi-source replication
Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 tables

User concepts

Access control

Azure Active Directory Authentication

Users with fine-grained authorization concept

no user groups or roles

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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