Azure DocumentDb Training - Resource Model

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Azure DocumentDb Training – Resource Model

How do we model Data inside DocumentDb?


Recap: Why DocumentDb

Resource Model

Document and Resource Units

Database Account, Accessibility and Consistency

Database and Namespace

Containers and Partitioning

Conclusions


Recap: why DocumentDb


Set of principles that can be satisfied by a Distributed System

Consistency:All nodes should see the same data at the same time

"Result is always up to date" (no out-of-date data / stale data)

Availability:Every request receives a response about whether it succeeded or failed

"There is always a result" (accepting out-of-date data / stale data)

Partition-tolerance:The system continues to operate despite arbitrary partitioning due to network failures

"Data can be given one of multiple nodes" (some nodes can be out-of-date/ stale)

A distributed system can satisfy any two of these principles at the same time, but not all three


A DBMS is a Distributed SystemIt lives under CAP Theorem

A DBMS needs to choose loosing one of the three principles:A Relational DBMS chooses loosing partitioning to guarantee strict consistency and

availability

A No-Sql DBMS typically chooses moving to eventual consistency (relaxing, not

loosing) to guarantee partitioning and availability (it complains Stale Data)


With Eventual Consistency, a DBMS cannot run some typical

relational featuresreferential integrity

check constraints

schema


DocumentDb is a schemaless Db

DocumentDb is a Document-Oriented DbDocument is JSON Document

Promote code first development (mapping objects to json)

Resilient to iterative schema changes

No ORM required

It’s great for Catalog Data, Preference and State, Event Store,

User Generated Content, Data Exchange


Resource Model


DocumentDb is Platform as a ServiceNo OnPremise

RESTful APIAll DocDb elements public and accessible as Resource Uri

ResourceJson Resources



Documents


JS

JS

JS

101010

{

"id" : "123"

"name" : "joe"

"age" : 30

"address" : {

"street" : "some st"

}

}


Come as you are

Data normalization


embed reference


Representing one-to-many relationships.

Representing many-to-many relationships.

Related data changes frequently.

Referenced data could be unbounded

Provides more flexibility than embeddingMore round trips to read data

Normalizing typically provides better write performance


There are contains relationships between entities.

There are one-to-few relationships between entities.

There is embedded data that changes infrequently.

There is embedded data won't grow without bound.

There is embedded data that is integral to data in a document.


Resource Unit

DocumentDb is Platform as a Service: no perception of physical resource allocation

A throughput currency

1RU: ability of reading a 1Kb Json document

Many factors impacting RU usage: Document size. Document property count. Data consistency. Indexed properties. Document indexing. Query patterns. Script usage.

Reservation ModelYou are billed for the amount of throughput reserved for the collection, regardless of how much of that throughput is actively used.

There is a pricing calculator available to help calculating costs

https://www.documentdb.com/capacityplanner

https://www.documentdb.com/capacityplanner


demo

Estimating cost for a

docucument


Database Account


Unit of Autorization

Unit of Consistency

JS

JS

JS

101010


Master keysUpon creation of a DocumentDB account, two master keys (primary and secondary) are created. These keys enable full administrative access to all resources within the DocumentDB account.

Read-only keysUpon creation of a DocumentDB account, two read-only keys (primary and secondary) are created. These keys enable read-only access to all resources within the DocumentDBaccount.

Resource tokensA resource token is associated with a DocumentDB permission resource and captures the relationship between the user of a database and the permission that user has for a specific DocumentDB application resource (e.g. collection, document).


Query / transaction throughput (and reliability – i.e., hardware

failure) depend on replication!All writes to the primary are replicated across two secondary replicas

All reads are distributed across three copies

“Scalability of throughput” – allowing different clients to read from different replicas

helps prevent bottlenecks

BUT replication takes time!Potential scenario: some clients are reading while another is writing

Now, the data is stale (out-of-date), inconsistent!


Trade-off: speed (performance & availability) or consistency (data correctness)?“Does every read need the MOST current data?”

“Or do I need every request to be handled and handled quickly?”

4 options …Strong, Session, Bounded Staleness, Eventual

Default consistency for the entire Db…

At collection basis in a future release

On query basis (optional parameter on CreateDocumentQuery method)


demo

Creating a DocumentDb

Database Account


Database


Unit of Namespace

JS

JS

JS

101010


demo

Creating a DocumenDb

Database


Collections


A unit of scale for transactionfor stored procedures and triggers

A unit of query throughputcapacity units allocated uniformly across all collections)

A unit of replicationA collection is replicated three times

A container of JSON documentsJSON docs inside of a collection can vary dramatically

JS

JS

JS

101010


Collection-based RU ReservationCapacity units allocated uniformly across all collections)

Standard pricing tier with hourly billing$0.042/hour for mimimum 400RU/s

Performance levels can be adjusted

Each collection = 10GB of SSDLimit of 100 collections (1 TB)

Soft limit, can be lifted as needed per account (with Support)


Partitioning

Data SizeA single collection (currently*) holds 10GB

Throughput3 Performance tiers with a max of 2,500 RU/sec


demo

Creating a DocumenDb

Collection


Conclusions


DocumentDb is a Restful service

Documents defines Unit of Costs with Resource Units

Database Account defines Accessibility and Consistency

Database is a Namespace placeholder

Containers is the unit of Scale

Software

Azure DocumentDb Training - Resource Model