Patterns for Scalability in Windows Azure Applications (Alex Mang)
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Transcript of Patterns for Scalability in Windows Azure Applications (Alex Mang)
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Patterns for Scalability in Microsoft Azure Applications
Alex Mang
http://alexmang.ro
@mangalexandru
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Huge thanks to our sponsors & partners!
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• Alex Mang
• CEO @ KeyTicket Solutions
–Microsoft BizSpark Plus
• Azure Advisor
• MS, MCP, MCSD
Speaker.Bio.ToString()
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• Common requirements for cloud apps:–Availability
–Data management
–Design and implementation
–Messaging
–Management and monitoring
–Performance and scalability
–Resiliency
– Security
What are patterns?
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• Performance
– ‘indication of responsiveness of a system to execute any action within a given time interval’
• Scalability
– ‘ability of a system to handle increases in load without impact on performance’
Performance and Scalability Patterns
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Performance and Scalability Patterns
Cache-aside
Competing consumersCQRS
Event sourcing
Index table
Materialized view
Priority Queue
Queue based load leveling
Sharding
Static content
Throttling
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QUEUE-BASED LOAD LEVELING PATTERN
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Queue-Based Load Leveling Pattern (Context)
• Cloud app require external services
• High load on cloud app means high load on services
• External services may be less scalable
• High load on cloud app could result in failing external services
• Possible self-throttling
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Queue-Based Load Leveling Pattern (Solution)
• Force the processing of request inside a queue
• Thus, load-leveled service requests
• Additional advantage: queue also works as a buffer
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Queue-Based Load Leveling Pattern (Solution)
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Queue-Based Load Leveling Pattern (Consid.)
• Make sure services are scaled correctly
• Task senders may wait service replies
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COMPETING CONSUMERS PATTERN
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• Asynchronously process requests
• The number of concurrent requests over time varies
• The time required for processing varies
Competing Consumers Pattern (Context)
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Competing Consumers Pattern (Solution)
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Competing Consumers Pattern (Considerations)
• Ordering
• Poisoned messages
• Result handling
• Message queue scaling
• Reliability
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PRIORITY QUEUE PATTERN
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• Asynchronous processing via queues
–Queues can’t sort messages (most of the times)
• Push notification (15K) vs. e-mail (15K)
Priority Queue Pattern (Context)
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• Queues with different priorities
• Consumers based on queue priority
Priority Queue Pattern (Solution)
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Priority Queue Pattern (Solution)
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Priority Queue Pattern (Considerations)
• What is ‘high priority’ vs ‘low priority’
• (Single pool consumers) high first, low after
• (Single pool consumers) elevate old messages
• Multiple queues work best for less priority definitions
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Priority Queue Pattern (When To Use)
Push first, send after example
Multi-tenant applications
Different SLAs / customers
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Priority Queue Pattern (When NOT To Use)
Messages have similar priority
No burst of messages in the queue ever exists
Costs must be kept down
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DEMO
PRIORITY QUEUE PATTERN
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THROTTLING PATTERN
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Throttling Pattern (Context)
• Cloud application load varies
–# active users (mostly during work hours)
– Type of activities (analysis at end of month)
• Sudden unanticipated bursts
–Poor performance
– Eventual failures
• SLA requirements
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Throttling Pattern (Solution)
• Auto-scaling, for starters…
• Define resource soft limits
• Monitor resource usage
• Throttle users –Based on business impact (tiers / plans)
–Based on users’ concurrent requests
• Degrade functionality
• Load-leveling pattern / priority-queue pattern
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Throttling Pattern (Solution)
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Throttling Pattern (Solution)
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Throttling Pattern (Considerations)
• Architectural decision: consider it while designing
• Quick monitoring technique
• Notify accordingly
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Throttling Pattern (When To Use)
• Meet SLA
• Prevent single user monopolize everything
• Gracefully handle activity bursts
• Control costs by limiting max. resource usage
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DEMO
THROTTLING PATTERN
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Throttling Pattern (Demo)
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MATERIALIZED VIEW PATTERN
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• Most developers think about how data is stored
• In NoSQL, we usually store everything in a single entity
• In SQL, we have size constraints
• End-up in:
–Performance impact
–High prices
Materialized View Pattern (Context)
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• 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 query
• Updated a.s.a.p. (schedule / triggered)
Materialized View Pattern (Solution)
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Materialized View Pattern (Solution)
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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
Security or privacy reasons
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Materialized View Pattern (When NOT To Use)
Data source is simple to query
Data changes quickly
Consistency is most important
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DEMO
MATERIALIZED VIEW PATTERN
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Materialized View Pattern (Demo)
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COMMAND AND QUERY RESPONSIBILITY SEGREGATION PATTERN
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CQRS Pattern (Context)
• Traditional CRUD system do everything over the same data store
• Typically, same entity for DB <--> UI <--> DB
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CQRS Pattern (Context)
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CQRS Pattern (Context)
• Many concurrent connections FAIL
• Complex business logic FAIL
• Too much data passed around
• Performance impact @ high load, due to complex querying
• Security issues may arise
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CQRS Pattern (Solution)
• Segregate read (queries) from write (commands)
• Models for querying and for updating are different
• Possible to access same store, better not to
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CQRS Pattern (Solution)
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CQRS Pattern (Considerations)
• Additional complexity
• Consistency considerations
• CQRS for parts of the application
• Use in conjuction with Event Source pattern
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CQRS Pattern (When To Use)
Multiple concurrent operations
Already familiar with Domain-Driven-Design techniques
Read performance ≠ write performance
Different teams (read vs. write)
App. lifecycle: model update, business logic update
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CQRS Pattern (When NOT To Use)
Simple business rules
Simple CRUD-style UI are enough
Across the whole system
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SHARDING PATTERN
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Sharding Pattern (Context)
• Why scale out compute, and not scale out data?
• Must scale out data because:
– Storage limitations
–Concurrent requests
–Network bandwidth
–Geography
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Sharding Pattern (Solution)
• Horizontal partitions of data – (a.k.a. shards)
• Same schema, different data
• Runs on its own server
• Benefits:– Scale out data service
–Use commodity hardware
–Better performance
–Closely located geographically
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Sharding Pattern (Solution)
• Lookup strategy• Range strategy• Hash strategy
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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 (Solution)
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THANK YOU!
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Q & A