Patterns for Scalability in

Microsoft Azure Applications

(starting at 17.00)Alex Mang

alexmang.ro | @mangalexandru

25th of October 2014

•Alex Mang

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• They promised crazy scaling capacity

•… & they fulfilled their promise

•… but my apps are still under-performing

Context of cloud apps

Common Architecture

Queue-Based Load Pattern

Queue Based Load Leveling Pattern

Source: Microsoft Patterns & Practices - Cloud Design Patterns

Possible bottlenecks

Competing-Consumers Pattern

Source: Microsoft Patterns & Practices - Cloud Design Patterns

What about important messages?

Priority Queue Pattern

Competing Consumers Pattern

Source: Microsoft Patterns & Practices - Cloud Design Patterns

Recap

Queue-based Load:

- Removes app failure caused by 3rd party

services

Competing-Consumers:

- Ensures messages (= tasks) are processed

faster, even under high load

Priority Queue:

- Ensures important tasks run first

DEMO

PRIORITY QUEUE PATTERN

•Most developers think about how data is

stored in OOP manner

• In NoSQL, commonly store everything in a

single entity

• In SQL, we have size constraints

• End-up in:

• Performance impact

• High prices

What about data repo. limit.?

•Can generate views in advance, containing

data on a per-requirement basis

•Only contain data required by query

• Include current values of calculated columns

or data items

•May be optimized for a single queries

•Updated a.s.a.p. (schedule / triggered)

What about data repo. limit.? (cont’)

Materialized-View Pattern

Source: Microsoft Patterns & Practices - Cloud Design Patterns

Materialized-View Pattern

Materialized View Pattern ExampleSource: Microsoft Patterns & Practices - Cloud Design Patterns

Materialized-View Pattern

When to use

Queries are complex

Data difficult to query directly

Temporary views dramatically improve perf.

Temporary views act as DTOs for UI,

reporting etc.

Data store not always available (SQL Azure)

Security or privacy reasons

Recap

So far, I’ve talked about:

• Taking pieces of code out of the ‘front facing’ apps

• Messaging methods between apps and consumers

• Simple queue

• Priority queue

• Response queues

• Partitioning data into views

• Materialized views!

Hip-hip, Hurray!

WE CAN SCALE OUR COMPUTE ROLES!

But what about data?

• Thought:

If we scale-out our compute roles…

… why not scale out our databases too?

Sharding Pattern

• Scale-up vs. scale-out applies to dbs. too!

• Shard = horizontal partition of overall data

•Must scale out data because:

• Storage limitations

• Concurrent requests

• Customer isolation

• Geography

Sharding Pattern (cont’ed)

•Horizontal partitions of data

• (a.k.a. shards)

• Same schema, different data

•Runs on its own server

•Benefits:

• Use commodity hardware

• Better performance

• Closely located geographically

Sharding Pattern (cont’ed)

Source: Microsoft Patterns & Practices - Cloud Design Patterns

Strategy Advantages Considerations

Lookup • More control

• Easy shard rebalance

• Shard lookup may

create additional

overhead

Range • Easy to implement

• Works well on range queries

• Easy management

• Suboptimal balance

• Shard rebalance is

difficult

Hash • Best balance

• Request routing directly via

hashing alg.

• Calculating hash may

create additional

overhead

• Rebalance is difficult

Sharding Pattern (cont’ed)

DEMO

SHARDING PATTERN

•Microsoft Patterns & Practices:

• Cloud Design Patterns

http://msdn.microsoft.com/en-

us/library/dn568099.aspx

• Developing Multi-tenant Apps. for the Cloud:

http://msdn.microsoft.com/en-

us/library/ff966499.aspx

• Moving Applications to the Cloud:

http://msdn.microsoft.com/en-

us/library/ff728592.aspx

References

References (cont’ed)

•Get Started with Azure SQL Database Elastic

Scale Preview

• http://azure.microsoft.com/en-

us/documentation/articles/sql-database-elastic-

scale-get-started/

• Elastic Scale Documentation Map

• http://azure.microsoft.com/en-

us/documentation/articles/sql-database-elastic-

scale-documentation-map/

• Elastic Scale Sample App

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Q & A

Thank you!

Patterns for Scalability in Microsoft Azure Applications

Alex Mang

alexmang.ro | @mangalexandru

25th of October 2014

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