MS Azure DocumentDBMSDEVMTLGROUPE AZUREOCTOBER 26TH, 2015

Who am I?

Vincent-Philippe LauzonCloud Solution ArchitectMicrosoft Canada

Blog: http://vincentlauzon.comTwitter: http://twitter.com/@vplauzon

Audience

I’ve read about Azure

I’ve tried Azure

Working with Azure

Azure is what I do

No SQL

NO SQL → Not Only SQL Other than Tabular / Relational model Less / No up-front (schema) design Easier to scale horizontally (cluster) Make them a better match for big data Each Product makes different tradeoffs Younger & less complete feature set

No SQL on Azure

Fully Managed Table Storage (Key Value) Redis (Key Value) Hadoop HBase (Wide Column) Hadoop Hive (ad hoc tables) DocumentDB (Document)

Through Marketplace MongoDB (Document) CouchBase (Document) Cassandra (Wide Column) Neo4J (Graph)

Azure DocumentDB

NoSQL document database as-a-service Query JSON docs: whole docs are indexed Familiar languages: SQL & JavaScript Fast / Predictable Performance (SSD) Tunable consistency Flexible document schema without sacrificing

queryability

Will be available on Azure Stack (on premise)

Conceptual Domain

101010

DocumentDB account

Databases

Users

Permissions

Collections Documents Attachments

Stored procedures

Triggers

User-defined functions

your Documents here

{ }{ }

JS

JS

JS

Demo: Account Creation & Adding Documents

Collections

Collections are not tables Unit of partitioning / Scaling Unit Transaction boundary No enforced schema, flexible Queries or updates stay within one collection Size of 10 Gb

For more, you need to shard through multiple collections

e.g. Spill-over, Range

Demo: Simple Querying

More on querying

Visit the Querying Playground: https://www.documentdb.com/sql/demo

Use the cheat sheet http://aka.ms/docdbcheatsheet

Data Migration Tool: https

://azure.microsoft.com/en-us/documentation/articles/documentdb-import-data/

Querying limitation

Within a collection No inter-document joins (yet?) Beside filtering, only ORDER BY is supported No aggregation yet

No COUNT No GROUP BY No SUM, AVG, etc.

SQL for queries only No batch UPDATE or DELETE or CREATE

Indexing

Every property is indexed! Unless you opt out

You can opt-out selectively Leave out paths Per collection (policy) or per document

Indexing mode: consistent vs lazy Kind: hash, range & spatial Automatic vs manual You might want to fiddle with it: indexes take space You can now change them online!

Demo: Looking at indexing policy

Consistency

Set at the account level Can be overridden at the query level 4 Levels

Strong Bounded staleness Session Eventual

Demo: Looking at consistency level

DocumentDB at Microsoft

over 425 million unique usersstore 20TB of JSON document data

under 15ms writes and single digit ms reads

store for 40+ app / device combinations

available globally to serve all markets

user data store

Other objects

101010

DocumentDB account

Databases

Users

Permissions

Collections Documents Attachments

Stored procedures

Triggers

User-defined functions

your Documents here

{ }{ }

JS

JS

JS

Limitations / Quotas

https://azure.microsoft.com/en-us/documentation/articles/documentdb-limits/

Entity Quota

Accounts 5 (soft)

Dbs / Account 100

Permissions / Account 2M

Sprocs, Triggers & UDFs / collection 25

Max Execution Time / Sproc, Triggeer 5 seconds

Collections / DB 100 (soft)

ID Length 255 chars

AND, OR / query 20

Demo: .NET SDK

Data Modeling – Polymorphism

Put every document type in a collection

Discriminate on document type somehow documentType property or other mechanism

Use Collection as scaling units not as categorization unit

Data Modeling – Denormalization

Optimize for read (no inter-doc joins) Embed relationships in document

One-to-few relationships Data changing infrequently Data that is integral to documents When embed provides better reading perf

Make sure the pattern fit: you read much more than you write

Gone wrong: blog posts & comments Leverage x-doc transaction (stored procs)

Data Modeling – Normalization

Normalize One-to-many (unbound) Many-to-many Frequent changes

If nothing in here fits: stick with relational

Integration within Azure

Indexer for Azure Search https://azure.microsoft.com/en-us/documentation/

articles/search-howto-connecting-azure-sql-database-to-azure-search-using-indexers-2015-02-28/

Power BI: https

://azure.microsoft.com/en-us/blog/unleashing-insights-from-data-in-documentdb-with-power-bi/

Data Factory: both source & sink Sink in Stream Analytics:

https://azure.microsoft.com/en-us/blog/azure-stream-analytics-and-documentdb-for-your-iot-application/

IMDB import

Batch Event Hub StreamAnalytics

DocumentDB

Log Analysis Example

DocumentDBWeb SiteApp Logs

{ eventName = “busRead“, duration = “0:0:21.423“, subscription = “XYZ”, messageCount=“3”}

http://bit.ly/1JQbkTH

{ eventName = “requestFailed“, duration = “0:0:0.231“, error = “…”, endPoint=“http://oursite.com/...”}

Thank you!

All demo material is available here: http://bit.ly/1SrhVcA