Scalability Strategies

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What is Scalability?

Scalability is the ability of a system to handle an increased workload without losing performance. More precisely, it’s the system’s ability to handle increased workload by repeatedly applying a cost-effective strategy.

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Understanding Scalability

What do Amazon, Netflix, and Uber have in common? They are extremely good at scaling their systems whenever needed.

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Three Main Bottlenecks to Scalability

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Core Principles for Scalable Systems

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8 Must-Know Scalability Strategies

Here are 8 essential strategies to scale your system effectively:

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1. Stateless Services

Why Stateless Services Matter

Design stateless services because they don’t rely on server-specific data and are easier to scale.Benefits:

• Any server can handle any request
• Easy to add/remove servers
• Simpler load balancing
• Better fault tolerance

Implementation:

• Store session data in distributed cache (Redis)
• Use JWT tokens for authentication
• Externalize configuration

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2. Horizontal Scaling

Add more servers so that the workload can be shared across multiple instances. Horizontal Scaling Benefits:

• No upper limit on capacity
• Better fault tolerance
• Cost-effective with commodity hardware
• Easier rollback and updates

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3. Load Balancing

Load Balancing Algorithms:

• Round Robin
• Least Connections
• IP Hash
• Weighted Round Robin
• Least Response Time

Benefits:

• Even distribution of traffic
• High availability
• Health checking
• SSL termination

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4. Auto Scaling

Implement auto-scaling policies to adjust resources based on real-time traffic.

Auto Scaling Strategies

Scaling Triggers:

• CPU utilization
• Memory usage
• Request count
• Custom metrics

Scaling Policies:

• Target Tracking: Maintain a specific metric target
• Step Scaling: Scale by different amounts based on thresholds
• Scheduled Scaling: Scale based on predictable patterns

Cloud Provider Solutions:

• AWS Auto Scaling
• Azure Virtual Machine Scale Sets
• Google Cloud Autoscaler
• Kubernetes Horizontal Pod Autoscaler

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5. Caching

Caching Layers:

• Client-side caching - Browser, mobile app
• CDN caching - Edge locations
• Application caching - Redis, Memcached
• Database caching - Query result caching

Popular Caching Solutions:

• Redis
• Memcached
• Varnish
• CloudFront

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6. Database Replication

Replicate data across multiple nodes to scale read operations while improving redundancy. Benefits:

• Improved read performance
• High availability
• Disaster recovery
• Geographic distribution

Replication Patterns:

• Master-Slave replication
• Master-Master replication
• Multi-region replication

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7. Database Sharding

Sharding Strategies:

Common Sharding Approaches

1. Range-based Sharding

• Partition by ID ranges
• Simple but can create hotspots

2. Hash-based Sharding

• Use hash function on key
• Even distribution
• Difficult to add/remove shards

3. Geographic Sharding

• Partition by location
• Reduces latency
• Supports data compliance

4. Directory-based Sharding

• Lookup table for routing
• Flexible but adds complexity

Challenges:

• Complex queries across shards
• Distributed transactions
• Rebalancing shards
• Maintaining referential integrity

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8. Async Processing

Use Cases:

• Email sending
• Image/video processing
• Report generation
• Data analytics
• Batch jobs

Implementation Patterns:

• Message queues (RabbitMQ, SQS)
• Event streams (Kafka)
• Task queues (Celery, Bull)
• Job schedulers (Airflow, Cron)

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Database Scaling Strategies

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7 Must-Know Database Scaling Techniques

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1. Indexing

Indexing Best Practices

Check the query patterns of your application and create the right indexes.Types of Indexes:

• B-tree indexes (default)
• Hash indexes
• Full-text indexes
• Geospatial indexes

Best Practices:

• Index columns used in WHERE clauses
• Index foreign keys
• Avoid over-indexing (writes slow down)
• Monitor index usage

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2. Materialized Views

Pre-compute complex query results and store them for faster access. Benefits:

• Faster query performance
• Reduced computation load
• Better for complex aggregations

Trade-offs:

• Additional storage required
• Need refresh strategy
• Potential staleness

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3. Denormalization

When to Denormalize:

• Read-heavy workloads
• Complex joins impacting performance
• Data that changes infrequently

Considerations:

• Data consistency challenges
• Increased storage
• Update complexity

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4. Vertical Scaling

Boost your database server by adding more CPU, RAM, or storage. Limitations:

• Hardware limits
• Expensive at scale
• Downtime during upgrades
• Single point of failure

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5. Caching

Store frequently accessed data in a faster storage layer to reduce database load. Caching Strategies:

• Cache-aside
• Write-through
• Write-behind
• Refresh-ahead

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6. Replication

Create replicas of your primary database on different servers for scaling reads.

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7. Sharding

Split your database tables into smaller pieces and spread them across servers. Used for scaling both writes and reads.

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Scaling from One to Millions of Users

The diagram illustrates the evolution of a simplified eCommerce website from a monolithic design on a single server to a service-oriented/microservice architecture.

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Step 1: Separate Application and Database

Initial Separation

With the growth of the user base, one single application server cannot handle the traffic anymore.Solution: Put the application server and database server on separate servers.Benefits:

• Better resource allocation
• Independent scaling
• Improved security

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Step 2: Application Server Cluster

The business continues to grow, and a single application server is no longer enough. Solution: Deploy a cluster of application servers.

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Step 3: Load Balancer

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Step 4: Database Read Replicas

With business continuing to grow, the database might become the bottleneck. Solution: Separate reads and writes so that frequent read queries go to read replicas. Benefits:

• Greatly increased throughput for database writes
• Reduced load on primary database
• Better performance

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Step 5: Horizontal Partition and Caching

One single database cannot handle the load on both the inventory table and user table.

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Step 6: Microservices Architecture

Benefits:

• Independent deployment
• Technology flexibility
• Team autonomy
• Better fault isolation
• Easier scaling

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Common Scalability Techniques Summary

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Performance Optimization

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Reduce Latency Strategies

Top 5 Strategies to Reduce Latency

1. Use CDN - Serve static content from edge locations
2. Optimize Database Queries - Add indexes, optimize joins
3. Implement Caching - Multiple layers of caching
4. Async Processing - Move work to background
5. Connection Pooling - Reuse database connections

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System Design Trade-offs

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Monitoring and Observability

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Key Metrics to Track

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Logging and Tracing

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Best Practices

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Common Pitfalls to Avoid

What NOT to Do

Premature Optimization:

• Don’t scale before you need to
• Measure first, optimize later
• Start with vertical scaling if appropriate

Over-Engineering:

• Keep it simple initially
• Add complexity only when necessary
• Consider operational overhead

Ignoring Bottlenecks:

• Profile and identify real bottlenecks
• Don’t assume - measure
• Fix the biggest bottleneck first

Not Planning for Failure:

• Everything fails eventually
• Design for graceful degradation
• Implement proper monitoring

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Next Steps