Perchè un programmatore ama anche i database NoSQL

Mar 23, 2016Download as PPTX, PDF0 likes314 views

Per quale motivo i programmatori parlano tanto di NoSql? Non amano più Sql Server e il linguaggio Sql in generale? No. La complessità delle applicazioni Web e Cloud necessitano di soluzioni complesse, che soddisfano potenzialità e vincoli imposti dal mondo web. Oggi infatti si parla di Polyglot Persistence, di CQRS e altro. Obiettivo di questa sessione è far comprendere i nuovi principi cui aderiscono i web developers e abbassare l' "impedance mismatch" che sembra essersi creato con i dba e e db devs.

Marco Parenzan
Microsoft Azure MVP
@marco_parenzan
marco [dot] parenzan [at] 1nn0va [dot] it

Speaker info/Marco Parenzan
 www.slideshare.net/marco.parenzan
 www.github.com/marcoparenzan
 marco [dot] parenzan [at] 1nn0va [dot] it
 www.innovazionefvg.net
 www.facebook.com/1nn0va
 @marco_parenzan
Formazione ,Divulgazione e Consulenza con 1nn0va
Microsoft MVP 2015 for Microsoft Azure
Cloud Architect, NET developer
Loves Html5 Game Programming and Internet of Things

Perchè un programmatore ama anche i database NoSQL

MVC
Browser
MVVM
View
View
View
Backend Presentation

Business Logic
Data Access
Presentation
Contract DA/BL
Contract BL/P

Set-theory-based
Two-dimensional relations (or tables)
Tables have columns
Column data is strongly typed, types enforced by system
Projections (SELECT…)
Tables have tuples (or rows)
Selections (WHERE…)
Cartesian products (JOIN …)

Robust, stable, years of proving itself
Homogeneous data conforms to a structured / rigid
schema
Flexible query-ability
Very consistent & durable data
ACID transactions

Impedance mismatch (ORM mapping)
Requirement for flexible data
(Typically) Scale up

http://azure.microsoft.com/en-us/documentation/infographics/azure/

PaaS
Azure SQL Database
MySQL
IaaS
SQL Server on VMs
Oracle Database
MySQL
PostgreSQL
And so many more ….

http://azure.microsoft.com/en-us/documentation/infographics/cloud-design-patterns/

https://msdn.microsoft.com/en-us/library/dn568099.aspx

“all the information created, replicated, and consumed in a
single year,” the number has grown from 130 billion
gigabytes in 2010 to 2.8 trillion gigabytes in 2012.

SmartTVs, Personal Fitness Trackers, Fridges, Hot Tubs,
Toasters, Industrial applications, Connected Cars, Home
sensors & automation, Manufacturing, Agriculture, Medical
etc. etc. etc.

MVC
Data Session
Controller
View
Business
Logic
Model

$For this kind of document - { “id": “13244_user", "firstName": "John", "lastName": "Smith", "age": 25, "address": { "streetAddress": "21 2nd Str", "city": "New York", "state": "NY", "postalCode": "10021" }, “mobileNumber": "212 555-1234" }$

Part of the NoSQL family of databases
Built for simplicity, scale and performance
Non-relational, no schema enforced
Flexible query options

Promotes code first development
Resilient to iterative schema changes
Richer query and indexing (compared to KV stores)
Low impedance as object / JSON store; no ORM required
It just works
It’s fast (to get going, and in txs)

Complex relational data
Complex map/reduce aggregations
Distributed transactions
OLAP

PaaS
Azure DocumentDB
MongoLab, MongoDB Inc, MongoHQ
RavenHQ
Iaas
MongoDB
CouchDB
Couchbase
ArangoDB
And countless more …

Competing Consumers pattern
all consumers read from same stream (queue)
Message consumed by a single consumer

Service Bus: A layer of abstraction between clients and
services to relay inputs and outputs.
Inputs and outputs are “Messages”

Messages are persisted quickly
Messages are asynchronous by their nature
Messages are still there even if processing goes offline
Messages that can’t be processed can be replayed later

Queued access to DB
Insert
Update
Insert
Delete
DB
Customer
Activities
Data Persistence

CQRS
Event Handler
UI
Event
Command Handler
Event
Device
Queue
Topics/Subscription
Event Hub
Write
Model
Search
Model
Read
Model

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- Azure IoT Central provides a fully managed platform for building IoT solutions that is compliant with the Azure IoT platform. - It offers predictable pricing per device, forces useful modeling practices like device twins and plug and play, and provides industry templates to accelerate solution building. - While it handles much of the complexity, it also maintains compatibility with customizing solutions using the full Azure IoT platform and other Azure services.

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IoT scenarios necessarily pass through the Edge component and the Raspberry PI is a great way to explore this world. If we need to receive IoT events from sensors, how do I implement an MQTT endpoint? Kafka is a clever way to do this. And how do I process the data in Kafka? Spark is another clever way of doing this. How do we write custom code for these environments? .NET, now in version 6 is another clever way to do it! And maybe, we also communicate with Azure. We'll see in this session if we can make it all work!

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Building IoT infrastructure on edge with .net, Raspberry PI and ESP32 to conn...Marco Parenzan

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Perchè un programmatore ama anche i database NoSQL

1. Marco Parenzan Microsoft Azure MVP @marco_parenzan marco [dot] parenzan [at] 1nn0va [dot] it

2. Speaker info/Marco Parenzan  www.slideshare.net/marco.parenzan  www.github.com/marcoparenzan  marco [dot] parenzan [at] 1nn0va [dot] it  www.innovazionefvg.net  www.facebook.com/1nn0va  @marco_parenzan Formazione ,Divulgazione e Consulenza con 1nn0va Microsoft MVP 2015 for Microsoft Azure Cloud Architect, NET developer Loves Html5 Game Programming and Internet of Things

4. Polyglot Programmer

5. MVC Browser MVVM View View View Backend Presentation

6. Backend Public Access Controller API

7. Business Logic Data Access Presentation Contract DA/BL Contract BL/P

10. «Monoglot» Data User

11. Set-theory-based Two-dimensional relations (or tables) Tables have columns Column data is strongly typed, types enforced by system Projections (SELECT…) Tables have tuples (or rows) Selections (WHERE…) Cartesian products (JOIN …)

12. Robust, stable, years of proving itself Homogeneous data conforms to a structured / rigid schema Flexible query-ability Very consistent & durable data ACID transactions

13. Impedance mismatch (ORM mapping) Requirement for flexible data (Typically) Scale up

14. Cloud Citizen

15. http://azure.microsoft.com/en-us/documentation/infographics/azure/

18. PaaS Azure SQL Database MySQL IaaS SQL Server on VMs Oracle Database MySQL PostgreSQL And so many more ….

20. http://azure.microsoft.com/en-us/documentation/infographics/cloud-design-patterns/

22. https://msdn.microsoft.com/en-us/library/dn568099.aspx

23. Some Facts

24. “all the information created, replicated, and consumed in a single year,” the number has grown from 130 billion gigabytes in 2010 to 2.8 trillion gigabytes in 2012.

25. SmartTVs, Personal Fitness Trackers, Fridges, Hot Tubs, Toasters, Industrial applications, Connected Cars, Home sensors & automation, Manufacturing, Agriculture, Medical etc. etc. etc.

27. What is NoSQL?

29. Issues of a CRUD model

30. MVC Data Session Controller View Business Logic Model

32. Contracts and View Models

35. For this kind of document - { “id": “13244_user", "firstName": "John", "lastName": "Smith", "age": 25, "address": { "streetAddress": "21 2nd Str", "city": "New York", "state": "NY", "postalCode": "10021" }, “mobileNumber": "212 555-1234" }

36. Part of the NoSQL family of databases Built for simplicity, scale and performance Non-relational, no schema enforced Flexible query options

37. Promotes code first development Resilient to iterative schema changes Richer query and indexing (compared to KV stores) Low impedance as object / JSON store; no ORM required It just works It’s fast (to get going, and in txs)

38. Mobile and Web apps – metadata for content oriented apps User data store – user profile and behavior data IoT scenarios – queryable device and event store Event workflow and logging Data ingestion and presentation – ingesting data with variable schema with rich views over the data Product catalogs

39. Complex relational data Complex map/reduce aggregations Distributed transactions OLAP

40. PaaS Azure DocumentDB MongoLab, MongoDB Inc, MongoHQ RavenHQ Iaas MongoDB CouchDB Couchbase ArangoDB And countless more …

43. Stale Data

46. Application Responsiveness

48. Competing Consumers pattern all consumers read from same stream (queue) Message consumed by a single consumer

49. Service Bus: A layer of abstraction between clients and services to relay inputs and outputs. Inputs and outputs are “Messages”

50. Messages are persisted quickly Messages are asynchronous by their nature Messages are still there even if processing goes offline Messages that can’t be processed can be replayed later

51. Queued access to DB Insert Update Insert Delete DB Customer Activities Data Persistence

60. Responsibility

63. CQRS Event Handler UI Event Command Handler Event Device Queue Topics/Subscription Event Hub Write Model Search Model Read Model

64. Publish/Subscribe pattern each consumer reads from its subscription (a copy of message on related topic) Message consumed by more subscribers (use filters) Subs

66. Partitioned Consumers pattern

67. Marco Parenzan Microsoft Azure MVP @marco_parenzan marco [dot] parenzan [at] 1nn0va [dot] it

Editor's Notes

#5: 4
#11: 10
#33: 32
#44: 43
#47: 46
#61: 60

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