[System Design Tech Case Study Pulse #2] How Lyft Handles 2x Traffic Spikes during Peak Hours with Auto scaling Infrastructure..

Tech you must know....

Oct 01, 2024

Hi All,

Lyft's auto scaling infrastructure is a marvel of modern cloud engineering, capable of seamlessly handling 2x traffic spikes during peak hours. This robust system forms the backbone of Lyft's ability to maintain high performance and reliability, even under rapidly changing demand.

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In this post, I’ll dive deep into how this system works, exploring the key components, technologies, and processes that enable such dynamic scalability.

System Overview

Before I delve into the auto scaling architecture, let's look at some key metrics of Lyft's system:

Daily active users: 20+ million
Peak requests per second: Over 100,000
Normal to peak traffic ratio: 2:1
Supported cities: 600+
Microservices: 1000+
Kubernetes pods: 100,000+
Auto scaling response time: < 30 seconds
Infrastructure provisioning time: < 2 minutes
System availability during traffic spikes: 99.99%
Average request latency increase during spikes: < 10%
Cloud regions utilized: 3 main, 2 backup

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How Process Works —

User interacts with the Lyft App, generating traffic.
Global Load Balancer distributes requests across data centers.
API Gateway handles initial request processing.
Traffic Ingress Service analyzes incoming traffic in real-time.
Forecasting Engine predicts future traffic patterns.
Capacity Planning Service determines resource needs.
Kubernetes Cluster manages the application infrastructure:
- Cluster Autoscaler handles node-level scaling.
- Horizontal Pod Autoscaler manages service-level scaling.
Microservices handle specific functionalities (ride matching, pricing, etc.).
Custom Resource Autoscaler manages scaling for databases, caches, and queues.

Lyft's auto scaling infrastructure -

1. Real time Traffic Analysis

1. The Traffic Ingress Service continuously monitors incoming requests:
   Tracks request rates across all endpoints
   Analyzes traffic patterns and user behavior
   Identifies anomalies and sudden spikes
2. The Load Balancing Layer provides crucial metrics:
   Measures server response times and error rates
   Tracks connection pool utilization
   Reports on geographic distribution of traffic
Key metrics for this process:
Metric collection interval: Every 5 seconds
Anomaly detection time: < 10 seconds
Traffic pattern analysis latency: < 30 seconds

Predictive Analytics

1. The Forecasting Engine anticipates future traffic patterns:
   Utilizes historical data and machine learning models
   Considers factors like time of day, weather, and special events
   Generates short term (minutes) and long term (hours) predictions
2. The Capacity Planning Service uses these predictions to prepare:
   Estimates required resources for anticipated traffic
   Triggers proactive scaling actions
   Adjusts scaling thresholds dynamically
Prediction accuracy metrics:
Short term prediction accuracy: Within 10% for 95% of cases
Long term prediction accuracy: Within 20% for 90% of cases
Proactive scaling trigger time: 5 15 minutes before anticipated spike

2. Auto scaling Architecture
Lyft's auto scaling system is designed for rapid response and fine grained control:
1. The Kubernetes Cluster Autoscaler manages node level scaling:
   Monitors pod scheduling and resource utilization
   Adds or removes nodes based on demand
   Optimizes for cost efficiency and performance
2. The Horizontal Pod Autoscaler handles service level scaling:
   Adjusts the number of pods for each service
   Uses custom metrics beyond CPU/memory (e.g., request rate, queue length)
   Implements different scaling policies for various service types
3. The Custom Resource Autoscaler manages Lyft specific resources:
   Scales databases, caches, and message queues
   Implements gradual scaling to prevent thundering herd problems
   Ensures data consistency during scaling operations
Auto scaling performance metrics:
Node provisioning time: < 90 seconds
Pod scaling time: < 30 seconds
Custom resource scaling time: < 2 minutes
Scaling decision time: < 10 seconds

3. Intelligent Load Distribution

As the system scales, efficient load distribution becomes crucial:
1. The Global Traffic Manager directs users to optimal data centers:
   Uses DNS based routing and anycast IP addressing
   Considers geographical proximity and data center health
   Implements gradual traffic shifting during scaling events
2. The Service Mesh (based on Envoy) handles fine grained traffic routing:
   Implements advanced load balancing algorithms (least request, ring hash)
   Provides circuit breaking and retry mechanisms
   Enables canary deployments and gradual rollouts
3. The Rate Limiting Service protects against traffic overloads:
   Implements distributed rate limiting across services
   Adjusts limits dynamically based on system capacity
   Provides backpressure mechanisms to prevent cascading failures
Load distribution metrics:
Global routing decision time: < 50ms
Service to service request latency: < 10ms
Rate limiting decision time: < 5ms
Load balancing efficiency: < 5% variation in load across instances

4. Data Management and Consistency

Maintaining data consistency during rapid scaling is a significant challenge:
1. The Distributed Caching System ensures fast data access:
   Implements multi level caching (local, regional, global)
   Uses consistent hashing for cache key distribution
   Provides automatic cache population and invalidation
2. The Database Auto scaler manages database performance:
   Implements read replica auto scaling for high traffic periods
   Manages connection pools dynamically
   Provides query caching and optimization on the fly
3. The State Management Service handles stateful operations:
   Implements distributed locking for critical operations
   Manages session stickiness when required
   Provides a consistent view of system state across scaling events
Data management metrics:
Cache hit ratio: > 95% during traffic spikes
Database read replica spin up time: < 3 minutes
State convergence time after scaling: < 30 seconds
Data consistency guarantee: 99.99% during 2x traffic spikes

Behind the Scenes: Infrastructure and Optimization

To handle 2x traffic spikes efficiently, Lyft's infrastructure incorporates several advanced techniques:

1. Container Orchestration :

Utilizes Kubernetes for container management and orchestration
Implements custom schedulers for domain specific requirements
Achieves 95% resource utilization during peak times

2. Infrastructure as Code :

Uses tools like Terraform and Ansible for infrastructure provisioning
Implements GitOps practices for infrastructure changes
Enables rapid, version controlled infrastructure updates

3. Chaos Engineering :

Regularly simulates traffic spikes and component failures
Identifies bottlenecks and single points of failure
Improves system resilience through continuous testing

4. Performance Optimization :

Implements code level optimizations (e.g., asynchronous processing, caching)
Utilizes profiling tools to identify and resolve bottlenecks
Achieves 30% improvement in request throughput through ongoing optimizations

5. Cost Management :

Implements spot instance usage for non critical workloads
Uses automated cost allocation and tracking
Achieves 40% cost reduction compared to static provisioning

Infrastructure metrics:

Container startup time: < 5 seconds

Infrastructure provisioning accuracy: 99.99%

Chaos test frequency: Weekly for critical systems

Cost per request: Reduced by 35% during traffic spikes

Handling Scale and Efficiency -

To manage 2x traffic spikes with auto scaling infrastructure, Lyft employs -
1. Predictive Auto scaling :
   Uses machine learning to forecast traffic patterns
   Initiates scaling actions proactively
   Reduces reactive scaling needs by 60%
2. Multi dimensional Scaling :
   Scales not just horizontally, but also vertically and diagonally
   Optimizes instance types based on workload characteristics
   Achieves 25% better resource utilization compared to simple horizontal scaling
3. Granular Service Scaling :
   Implements per service scaling policies
   Uses custom metrics for scaling decisions (e.g., queue length, request complexity)
   Reduces over provisioning by 40% compared to uniform scaling
4. Stateless Service Architecture :
   Designs 95% of services to be stateless
   Utilizes distributed caching and session stores
   Enables near linear scalability for most system components
5. Adaptive Load Shedding :
   Implements intelligent request prioritization during peaks
   Gracefully degrades non critical features under extreme load
   Maintains core functionality even under 3x unexpected traffic spikes
6. Real time Performance Tuning :
   Dynamically adjusts system parameters (e.g., thread pools, connection limits)
   Implements automated database query optimization
   Achieves 20% latency reduction during traffic spikes

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