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Resiliency and Availability Design Patterns

Resiliency and Availability Design Patterns

We have traditionally built robust software systems by trying to avoid mistakes and by dodging failures when they occur in production or by testing parts of the system in isolation from one another. Modern methods and techniques take a very different approach based on resiliency, which promotes embracing failure instead of trying to avoid it. Resilient architectures enhance observability, leverage well-known patterns such as graceful degradation, timeouts and circuit breakers but also new patterns like cell-based architecture and shuffle sharding. In this session, will review the most useful patterns for building resilient software systems and especially show the audience how they can benefit from the patterns.

Adrian Hornsby

April 03, 2019
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  1. © 2019, Amazon Web Services, Inc. or its affiliates. All

    rights reserved. Resiliency and Availability Design Patterns for the Cloud Adrian Hornsby Sr. Technical Evangelist Amazon Web Services
  2. © 2019, Amazon Web Services, Inc. or its affiliates. All

    rights reserved. Distributed Systems are hard
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    rights reserved. Complex systems Amazon Twitter Netflix
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    rights reserved. Partial failure mode
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    rights reserved. Resiliency: Ability for a system to handle and eventually recover from unexpected conditions
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    rights reserved. How do we build resilient software systems?
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    rights reserved. “Quality is not an act, it is a habit” Aristotle, some time around 350BC
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    rights reserved. https://aws.amazon.com/wellarchitected
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    rights reserved. Let’s talk about Multi-AZ
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    rights reserved. Multi-AZ architecture Region Availability zone a Availability zone b Availability zone c Instances Instances Instances DB Instance DB instance standby
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    rights reserved. Multi-AZ architecture Region Availability zone a Availability zone b Availability zone c Instances Instances Instances DB Instance DB instance standby X
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    rights reserved. Multi-AZ architecture Region Availability zone a Availability zone b Availability zone c Instances Instances Instances DB Instance DB instance standby X
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    rights reserved. Multi-AZ architecture Region Availability zone a Availability zone b Availability zone c Instances Instances Instances DB Instance X
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    rights reserved. Multi-AZ architecture • Enables fault-tolerant applications • AWS regional services designed to withstand AZ failures • Leveraged by most AWS regional services (S3, DynamoDB, ELBs, …) Region Availability zone a Availability zone b Availability zone c Instances Instances Instances DB Instance DB instance standby
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    rights reserved. Region Availability zone a Availability zone b Availability zone c Application
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    rights reserved. Region Availability zone a Availability zone b Availability zone c Application
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    rights reserved. Cascading Failures
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    rights reserved. Better to react without reacting
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    rights reserved. Region Availability zone a Availability zone b Availability zone c Application
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    rights reserved. 3 AZ’s is better than 2
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    rights reserved. Region Availability zone a Availability zone b Availability zone c Application Requires 8 Instances
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    rights reserved. Region Availability zone a Availability zone b Availability zone c Application Requires 6 Instances
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    rights reserved. Let’s talk about auto scaling
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    rights reserved. Auto-Scaling Fixed Variable
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    rights reserved. Availability zone 1 Auto Scaling group AWS Region Availability zone 2 Auto-scaling for self-healing X
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    rights reserved. Let’s talk about decoupling and async
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    rights reserved. Process A Process B Process A Process B Synchronous Asynchronous Waiting Working Continues get or fetch result Get result Decoupling with async pattern
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    rights reserved. API: {DO foo} PUT JOB: {JobID: 0001, Task: DO foo} API: {JobID: 0001} GET JOB: {JobID: 0001, Task: DO foo} {JobID: 0001, Result: bar} Cache node Worker Instance Worker Instance Queue/Streaming API Instance API Instance API Instance
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    rights reserved. Push Notification User Worker Instance Worker Instance API Instance API Instance Cache node Fetch results API Instance Queue/Streaming
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    rights reserved. Degrade & prioritize traffic with queues Worker Instance Worker Instance API Instance API Instance API Instance High Priority Queue Low Priority Queue
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    rights reserved. Let’s talk about timeouts, backoff & retries!
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    rights reserved. Users App DB Conn Pool INSERT INSERT INSERT INSERT What happens if the DB “slows down”? Timeout client side Timeout backend side ? ?
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    rights reserved. User 1 App DB Conn Pool INSERT Timeout client side = 10s Timeout backend side = default = Infinite Retry INSERT Retry INSERT ERROR: Failed to get connection from pool Retry
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    rights reserved. How else could we have prevented the error? User 1 DB Conn Pool INSERT Retry INSERT Retry INSERT Retry ERROR: Failed to get connection from pool
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    rights reserved. User 1 DB Conn Pool INSERT Timeout client side = 10s Timeout backend side = 10s Wait 2s before Retry INSERT INSERT Wait 4s before Retry Wait 8s before Retry Wait 16s before Retry Backing off between retries Releasing connections Backoff
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    rights reserved. No jitter With jitter https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ Simple Exponential Backoff is not enough: Add Jitter
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    rights reserved. Example: add jitter 0-1000ms def get_item(self, url, n=1): MAX_TRIES = 12 try: res = requests.get(url) except: if n > MAX_TRIES: return None n += 1 time.sleep((2 ** n) + (random.randint(0, 1000) / 1000.0)) return self.get_item(url, n) else: return res
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    rights reserved. Idempotent operation No additional effect if it is called more than once with the same input parameters.
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    rights reserved. Circuit Breaker • Wrap a protected function call in a circuit breaker object, which monitors for failures. • If failures reach a certain threshold, the circuit breaker trips. Producer Circuit Breaker Consumer Connection Monitoring Timeouts Breaking Circuit
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    rights reserved. https://spring.io/guides/gs/circuit-breaker/
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    rights reserved. Let’s talk about health checking!
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    rights reserved. Auto Scaling group Service A Availability zone 1 Auto Scaling group AWS Region Service A Availability zone 2 Service B Service B database Email Probing for health Cluster
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    rights reserved. Shallow health check Instance Cache node Email database Cluster Are you healthy? yes
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    rights reserved. Shallow health check Instance Cache node Email database Cluster Are you healthy? yes
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    rights reserved. Deep health check Instance Cache node Email database Cluster Are you healthy? yes Are you healthy? yes yes yes yes
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    rights reserved. Deep health check Instance Cache node Email database Cluster Are you healthy? no Are you healthy? no yes yes yes
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    rights reserved. Prioritize shallow health checks during hard times. Cache.
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    rights reserved. Let’s talk about load shedding.
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    rights reserved. Cheaply reject excess work
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    rights reserved. Be careful when selecting the right metric
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    rights reserved. Don’t be overly optimistic and take on more than you can. Find an operational metric to reject what you cannot take in. Favor cached and static content Prioritize ELB health check (shallow) pings In an overload situation you have precious resources, do not let any of it go to waste. Load Shedding
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    rights reserved. Service Degradation & Fallbacks
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    rights reserved. Let’s talk about databases!
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    rights reserved. Database Federation Users DB Products DB Instance Instance Instance DB Instance DB instance read replica DB Instance DB instance read replica
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    rights reserved. Database Sharding User ShardID 002345 A 002346 B 002347 C 002348 B 002349 A C B A Instance Instance Instance DB Instance DB instance read replica DB Instance DB instance read replica DB Instance DB instance read replica
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    rights reserved. Read / Write separation DB Instance DB instance read replica DB instance read replica DB instance read replica Instance Instance Instance Supports degradation through Read-Only mode
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    rights reserved. Transient state does not belong in the database.
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    rights reserved. Let’s talk about shuffle sharding.
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    rights reserved. X X X X X X X X ♤ ♡ ♢ ⚀ ⚁ ⚂ ⚃ ♧ ♢
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    rights reserved. Cascading Failures
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    rights reserved. Measure for this: blast radius
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    rights reserved. Cell-based architecture X X ♤ ♡ ♢ ⚀ ⚁ ⚂ ⚃ ♧ ♢
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    rights reserved. Shuffle sharding X X ♤ ♡ ♢ ⚀ ⚁⚂ ⚃ ♡ ♤ ♧ ♢ ⚀⚂ ♧ ⚁⚃ ♢ ♢ ♡ ♧ ♢
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    rights reserved. Shuffle sharding Nodes = 8 Shard size = 2 Combinations = 28 Overlap % customers 0 53.6% 1 42.8% 2 3.6%
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    rights reserved. Shuffle sharding Nodes = 100 Shard size = 5 Combinations = 75 million! Overlap % customers 0 77% 1 21% 2 1.8% 3 0.06% 4 0.0006% 5 0.0000013%
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    rights reserved. Let’s talk about chaos!
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    rights reserved. GameDay at Amazon Creating Resiliency Through Destruction https://www.youtube.com/watch?v=zoz0ZjfrQ9s
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    rights reserved. Chaos engineering https://github.com/Netflix/SimianArmy
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    rights reserved. “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” http://principlesofchaos.org
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    rights reserved. Failure injection • Start small & build confidence • Application level • Host failure • Resource attacks (CPU, memory, …) • Network attacks (dependencies, latency, …) • Region attacks • “Paul” attack
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    rights reserved. Plan for the worst, prepare for the unexpected.
  72. Thank you! © 2019, Amazon Web Services, Inc. or its

    affiliates. All rights reserved. Adrian Hornsby @adhorn https://medium.com/@adhorn https://speakerdeck.com/adhorn/patterns-for-building-resilient-software-systems-2019