DBMS > Google Cloud Firestore vs. Microsoft Azure Data Explorer vs. MySQL vs. TimesTen vs. Trafodion

System Properties Comparison Google Cloud Firestore vs. Microsoft Azure Data Explorer vs. MySQL vs. TimesTen 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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Fully managed big data interactive analytics platform

Widely used open source RDBMS

In-Memory RDBMS compatible to Oracle

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store

Relational DBMS

column oriented

Relational DBMS

Key/Value like access via memcached API

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

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

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

firebase.google.com/products/firestore

azure.microsoft.com/services/data-explorer

www.mysql.com

www.oracle.com/database/technologies/related/timesten.html

trafodion.apache.org

Technical documentation

firebase.google.com/docs/firestore

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

dev.mysql.com/doc

docs.oracle.com/database/timesten-18.1

trafodion.apache.org/documentation.html

Developer

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Apache Software Foundation, originally developed by HP

Initial release

2017

2019

1995

1998

2014

Current release

cloud service with continuous releases

8.4.0, April 2024

11 Release 2 (11.2.2.8.0)

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C and C++

C++, Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Data scheme

schema-free

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

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

yes

APIs and other access methods

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.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

C
C++
Java
PL/SQL

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Yes, possible languages: KQL, Python, R

yes

proprietary syntax

PL/SQL

Java Stored Procedures

Triggers

yes, with Cloud Functions

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

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

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

ACID

not for MyISAM storage engine

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

by means of logfiles and checkpoints

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 rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Azure Active Directory Authentication

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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