MS Azure
DocumentDB
MSDEVMTL
GROUPE AZURE
OCTOBER 26TH, 2015

Who am I?
Vincent-Philippe Lauzon
Cloud Solution Architect
Microsoft Canada
Blog: http://vincentlauzon.com
Twitter: 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
101
010
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 millionunique users
store 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
101
010
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 Stream
Analytics
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