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Building Efficient, Scalable,
and Resilient Front-end
Logging Service with AWS
KOKILAVANI KATHIRESAN | 27/07/2019
Introduction
- Containers, Serverless, Microservice Architecture change the way the software is
built
- The systems are more distributed, and more ephemeral
- No Complex system is ever fully healthy
- Better Resilience and Fault Tolerance is the goal
- Ease of debugging is a cornerstone to maintain and evolve robust systems
Observability
- Internal states of the system should be inferred by
its external outputs
- Reduce MTTD and MTTR
- Verifying the health of the service proactively
- To know what’s broken, and why?
- Provides the all-important feedback that drives
future iterations
Our Business Case
- To Collect logs, traces and metrics from Mobile/Web Browser
- Get insights of the application
- Understanding the user behavior patterns
- Monitor application performance
Front-end Logging Service
- Exposed a REST Endpoint
- Spring boot application which accepts the
compressed log message
- Decompress and Validate the Payload
- Forward it to the application’s log
destination (Splunk)
Requirements:
- 20000 Transactions per second
- 1 second latency
Internet
Logging
Service
AWS Account
Compressed Batched Logs
Latency Improvement
We split the service into two microservices.
Producer:
- Receives request and Validate the sender
- Accepts the payload
- Puts the data to queue
Consumer:
- Polls the data from queue
- Extract the payload and Validate the data
- Sends it to log destination
Logging
Service -
Producer
Logging
Service -
Consumer
SQS
FE Architecture in AWS
SHAILJA AGARWALA
Well Architected Framework
Five pillars :
- Operational excellence
- Security
- Reliability
- Performance efficiency
- Cost optimization
EC2 Setup
Producer:
- Compute Intensive (c5.2xlarge)
- No of instances : 3 to 20
Consumer:
- Memory Intensive (m5.2xlarge)
- No of Instances : 3 to 20
Alarms:
- Based on JVM metrics sent to Cloud watch
Load Balancer
EC2
EC2
EC2
EC2
EC2
EC2
SQS ELB
ELB
Route 53
- Expose the producer ELB through Route 53
- Route 53 endpoint is hosted behind Intuit API
gateway
- Disaster recovery through multiple CName across
region
EC2 EC2 EC2
Route 53 config
Route 53 config
contd.
Auto Scaling Group
Log generated varies during tax peak across the year
Producer:
- Request Processing Time decides scaling
Consumer:
- SQS depth
Auto Scaling Policies
Target Groups
- With auto scaling and load balancers involved, target groups will route
requests to EC2s and microservices
- Requests are being sent to new targets as soon as the registration is
complete and initial health check is passed
Cloud formation – Infrastructure as Code
Deployment & AMI Restack
RAVIKUMAR KOTTA
AMI Restack
Background:
- Intuit compliance team applies security patches and new baseline images are
released every 2 weeks
- App teams must either use these AMIs or derive AMIs from those baseline images
- Automated this entire process by using CW Rule and Codebuild services
Config: CW Rule on rhel7.4
Code build logs - Baking Logging service AMI
- Launch the new EC2 instance from Baseline AMI
- Copy chef recipes required to install software like java etc.. and
configuration required for Splunk forwarder and log rotation
- Bake logging service AMI
- Publish cloud watch event with the AMI id
Code build logs - Baking Logging service AMI
CW rule on Baked AMI
- Cloud watch rule configured to trigger on baked logging service AMI
- We have 2 targets configured on this CW Rule
- Lambda function: Creates new launch config with new AMI and updates
ASG
- Code pipeline: CD service to automate the steps to release logging
service
CW rule on Baked AMI
Code Pipeline to automate Deployment process.
- Source Stage: Downloads app config files
- Code deployment stage: Reads app file from source stage and
triggers code deploy for all environments
- Blue-Green deployment
- Re-routing traffic to new instances
Deployment
Lifecycle hooks
- BeforeInstall: Setup application configuration for ex: install jre, collectd, splunk
forwarder and pulls the latest code from S3 and deploy it
- ApplicationStop: Stops application
- ApplicationStart: Starts application
- ValidateService: Invokes automation tests against deployed code(Green)
App Spec Config
Installing Application and dependencies
Performance Test Report
Title + Content
Golden Signal
Metrics
Enhancements
- Extension for Metrics and Traces
- Dockerize the service code
- Deploy in Kubernetes
Thank you!

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Building Efficient, Scalable and Resilient Front-end logging service with AWS

  • 1. Building Efficient, Scalable, and Resilient Front-end Logging Service with AWS KOKILAVANI KATHIRESAN | 27/07/2019
  • 2. Introduction - Containers, Serverless, Microservice Architecture change the way the software is built - The systems are more distributed, and more ephemeral - No Complex system is ever fully healthy - Better Resilience and Fault Tolerance is the goal - Ease of debugging is a cornerstone to maintain and evolve robust systems
  • 3. Observability - Internal states of the system should be inferred by its external outputs - Reduce MTTD and MTTR - Verifying the health of the service proactively - To know what’s broken, and why? - Provides the all-important feedback that drives future iterations
  • 4.
  • 5. Our Business Case - To Collect logs, traces and metrics from Mobile/Web Browser - Get insights of the application - Understanding the user behavior patterns - Monitor application performance
  • 6. Front-end Logging Service - Exposed a REST Endpoint - Spring boot application which accepts the compressed log message - Decompress and Validate the Payload - Forward it to the application’s log destination (Splunk) Requirements: - 20000 Transactions per second - 1 second latency Internet Logging Service AWS Account Compressed Batched Logs
  • 7. Latency Improvement We split the service into two microservices. Producer: - Receives request and Validate the sender - Accepts the payload - Puts the data to queue Consumer: - Polls the data from queue - Extract the payload and Validate the data - Sends it to log destination Logging Service - Producer Logging Service - Consumer SQS
  • 8. FE Architecture in AWS SHAILJA AGARWALA
  • 9. Well Architected Framework Five pillars : - Operational excellence - Security - Reliability - Performance efficiency - Cost optimization
  • 10. EC2 Setup Producer: - Compute Intensive (c5.2xlarge) - No of instances : 3 to 20 Consumer: - Memory Intensive (m5.2xlarge) - No of Instances : 3 to 20 Alarms: - Based on JVM metrics sent to Cloud watch
  • 12. Route 53 - Expose the producer ELB through Route 53 - Route 53 endpoint is hosted behind Intuit API gateway - Disaster recovery through multiple CName across region EC2 EC2 EC2
  • 15. Auto Scaling Group Log generated varies during tax peak across the year Producer: - Request Processing Time decides scaling Consumer: - SQS depth
  • 17. Target Groups - With auto scaling and load balancers involved, target groups will route requests to EC2s and microservices - Requests are being sent to new targets as soon as the registration is complete and initial health check is passed
  • 18. Cloud formation – Infrastructure as Code
  • 19. Deployment & AMI Restack RAVIKUMAR KOTTA
  • 20. AMI Restack Background: - Intuit compliance team applies security patches and new baseline images are released every 2 weeks - App teams must either use these AMIs or derive AMIs from those baseline images - Automated this entire process by using CW Rule and Codebuild services
  • 21. Config: CW Rule on rhel7.4
  • 22. Code build logs - Baking Logging service AMI - Launch the new EC2 instance from Baseline AMI - Copy chef recipes required to install software like java etc.. and configuration required for Splunk forwarder and log rotation - Bake logging service AMI - Publish cloud watch event with the AMI id
  • 23. Code build logs - Baking Logging service AMI
  • 24. CW rule on Baked AMI - Cloud watch rule configured to trigger on baked logging service AMI - We have 2 targets configured on this CW Rule - Lambda function: Creates new launch config with new AMI and updates ASG - Code pipeline: CD service to automate the steps to release logging service
  • 25. CW rule on Baked AMI
  • 26. Code Pipeline to automate Deployment process. - Source Stage: Downloads app config files - Code deployment stage: Reads app file from source stage and triggers code deploy for all environments - Blue-Green deployment - Re-routing traffic to new instances
  • 28. Lifecycle hooks - BeforeInstall: Setup application configuration for ex: install jre, collectd, splunk forwarder and pulls the latest code from S3 and deploy it - ApplicationStop: Stops application - ApplicationStart: Starts application - ValidateService: Invokes automation tests against deployed code(Green) App Spec Config
  • 31. Title + Content Golden Signal Metrics
  • 32. Enhancements - Extension for Metrics and Traces - Dockerize the service code - Deploy in Kubernetes