Normal Operations:
- Concurrent viewers: 1M - 10M
- Active commenters: 100K - 1M (10% participation rate)
- Comments per second: 1K - 10K
- Read operations: 100K - 1M per second

Peak Events (99th percentile):
- Concurrent viewers: 100M+
- Active commenters: 10M+
- Comments per second: 100K+
- Read operations: 10M+ per second

Extreme Events (99.9th percentile):
- Concurrent viewers: 500M+
- Active commenters: 50M+
- Comments per second: 500K+
- Read operations: 50M+ per second

Connection Distribution:
- Viewers (read-only): 90% of connections
- Commenters (read-write): 10% of connections
- Connection lifetime: 30-120 minutes average
- Reconnection rate: 5-10% per minute

Server Capacity:
- Connections per server: 10K - 50K (depending on hardware)
- Memory per connection: 4KB - 8KB
- CPU overhead per connection: 0.1% - 0.5%
- Network bandwidth per connection: 1KB/s - 10KB/s

Write Operations:
- Comment inserts: 100K+ per second
- User activity updates: 500K+ per second
- Moderation actions: 1K+ per second
- Analytics events: 1M+ per second

Read Operations:
- Comment fetches: 10M+ per second
- User profile lookups: 5M+ per second
- Moderation queries: 100K+ per second
- Analytics queries: 1M+ per second

Storage Growth:
- Comments per day: 100M - 1B
- Storage per comment: 500 bytes - 2KB
- Daily storage growth: 50GB - 2TB
- Media attachments: 10TB+ per day

Target Latencies:
- Comment submission to database: <50ms (P95)
- Comment propagation to viewers: <200ms (P95)
- Comment moderation decision: <100ms (P95)
- User interface updates: <16ms (60 FPS)

Acceptable Degradation:
- During peak traffic: <500ms (P95)
- During system stress: <1s (P95)
- Fallback to polling: <5s updates

API Response Times:
- Comment POST: <100ms (P95)
- Comment GET (recent): <50ms (P95)
- User authentication: <200ms (P95)
- Moderation actions: <300ms (P95)

Database Query Times:
- Comment insertion: <10ms (P95)
- Recent comments fetch: <20ms (P95)
- User lookup: <5ms (P95)
- Aggregation queries: <100ms (P95)

# Peak throughput calculations
class ThroughputRequirements:
    def __init__(self):
        self.peak_comments_per_second = 500_000
        self.peak_viewers = 500_000_000
        self.comment_fanout_ratio = 1000  # Average viewers per comment
        
    def calculate_message_throughput(self):
        # Inbound comment processing
        inbound_throughput = self.peak_comments_per_second
        
        # Outbound message delivery
        outbound_throughput = (
            self.peak_comments_per_second * 
            self.comment_fanout_ratio
        )
        
        return {
            'inbound_messages_per_second': inbound_throughput,
            'outbound_messages_per_second': outbound_throughput,
            'total_network_messages': inbound_throughput + outbound_throughput
        }
    
    def calculate_bandwidth_requirements(self):
        avg_comment_size = 500  # bytes
        avg_delivery_size = 600  # bytes (with metadata)
        
        throughput = self.calculate_message_throughput()
        
        inbound_bandwidth = (
            throughput['inbound_messages_per_second'] * 
            avg_comment_size
        )
        
        outbound_bandwidth = (
            throughput['outbound_messages_per_second'] * 
            avg_delivery_size
        )
        
        return {
            'inbound_gbps': inbound_bandwidth / (1024**3) * 8,
            'outbound_gbps': outbound_bandwidth / (1024**3) * 8,
            'total_gbps': (inbound_bandwidth + outbound_bandwidth) / (1024**3) * 8
        }

# Example calculation
requirements = ThroughputRequirements()
throughput = requirements.calculate_message_throughput()
bandwidth = requirements.calculate_bandwidth_requirements()

print(f"Peak throughput: {throughput}")
print(f"Bandwidth requirements: {bandwidth}")

Per-Connection Memory Usage:
- Connection state: 2KB
- User session data: 1KB
- Message buffers: 4KB
- Protocol overhead: 1KB
Total per connection: ~8KB

Server Memory Calculation:
- 50K connections per server
- 8KB per connection
- Base memory usage: 400MB
- OS and application overhead: 2GB
- Buffer pools: 1GB
- Total memory per server: 4GB minimum

class CacheMemoryCalculation:
    def __init__(self):
        self.active_events = 1000
        self.comments_per_event = 10000  # Recent comments cached
        self.avg_comment_size = 500
        self.user_cache_entries = 1_000_000
        self.avg_user_data_size = 200
        
    def calculate_cache_memory(self):
        # Comment cache
        comment_cache_size = (
            self.active_events * 
            self.comments_per_event * 
            self.avg_comment_size
        )
        
        # User cache
        user_cache_size = (
            self.user_cache_entries * 
            self.avg_user_data_size
        )
        
        # Metadata and indexes
        metadata_size = comment_cache_size * 0.2
        
        total_cache_memory = (
            comment_cache_size + 
            user_cache_size + 
            metadata_size
        )
        
        return {
            'comment_cache_gb': comment_cache_size / (1024**3),
            'user_cache_gb': user_cache_size / (1024**3),
            'metadata_gb': metadata_size / (1024**3),
            'total_cache_gb': total_cache_memory / (1024**3)
        }

cache_calc = CacheMemoryCalculation()
cache_memory = cache_calc.calculate_cache_memory()
print(f"Cache memory requirements: {cache_memory}")

CPU-Intensive Operations:
- Comment validation and sanitization: 1ms per comment
- Spam detection algorithms: 5ms per comment
- Content moderation (ML): 10ms per comment
- Encryption/decryption: 0.5ms per comment
- JSON serialization/deserialization: 0.2ms per comment

Processing Pipeline:
- Total CPU time per comment: ~16.7ms
- At 500K comments/second: 8,350 CPU-seconds needed
- With 32-core servers: 261 servers minimum
- With safety margin (3x): 783 servers for processing

class DatabaseProcessingLoad:
    def __init__(self):
        self.peak_writes_per_second = 500_000
        self.peak_reads_per_second = 10_000_000
        self.avg_write_cpu_ms = 2
        self.avg_read_cpu_ms = 0.5
        
    def calculate_db_cpu_requirements(self):
        write_cpu_seconds = (
            self.peak_writes_per_second * 
            self.avg_write_cpu_ms / 1000
        )
        
        read_cpu_seconds = (
            self.peak_reads_per_second * 
            self.avg_read_cpu_ms / 1000
        )
        
        total_cpu_seconds = write_cpu_seconds + read_cpu_seconds
        
        return {
            'write_cpu_seconds_per_second': write_cpu_seconds,
            'read_cpu_seconds_per_second': read_cpu_seconds,
            'total_cpu_seconds_per_second': total_cpu_seconds,
            'equivalent_cores_needed': total_cpu_seconds
        }

db_load = DatabaseProcessingLoad()
cpu_requirements = db_load.calculate_db_cpu_requirements()
print(f"Database CPU requirements: {cpu_requirements}")

Regional Traffic Patterns:
- North America: 30% of global traffic
- Europe: 25% of global traffic
- Asia-Pacific: 35% of global traffic
- Rest of World: 10% of global traffic

Cross-Region Communication:
- User-to-user latency: <200ms globally
- Content delivery latency: <100ms from nearest CDN
- Database replication lag: <1s between regions
- Failover time: <30s for regional outages

class CDNRequirements:
    def __init__(self):
        self.global_viewers = 500_000_000
        self.avg_data_per_viewer_per_hour = 50  # MB
        self.peak_multiplier = 3
        
    def calculate_cdn_bandwidth(self):
        base_bandwidth_mbps = (
            self.global_viewers * 
            self.avg_data_per_viewer_per_hour / 3600 * 8
        )
        
        peak_bandwidth_mbps = base_bandwidth_mbps * self.peak_multiplier
        
        return {
            'base_bandwidth_gbps': base_bandwidth_mbps / 1000,
            'peak_bandwidth_gbps': peak_bandwidth_mbps / 1000,
            'cdn_nodes_needed': peak_bandwidth_mbps / 10000,  # 10 Gbps per node
            'storage_per_node_tb': 100  # Cache recent comments
        }

cdn_req = CDNRequirements()
cdn_bandwidth = cdn_req.calculate_cdn_bandwidth()
print(f"CDN requirements: {cdn_bandwidth}")

Daily Comment Volume:
- Normal days: 100M comments
- Peak event days: 1B+ comments
- Average comment size: 500 bytes
- Daily storage growth: 50GB - 500GB

Annual Storage Projections:
- Year 1: 20TB (base growth)
- Year 2: 50TB (user growth)
- Year 3: 100TB (feature expansion)
- Year 5: 500TB (global scale)

Storage Distribution:
- Hot data (last 24 hours): 5% of total
- Warm data (last 30 days): 15% of total
- Cold data (older): 80% of total

class StoragePartitioning:
    def __init__(self):
        self.comments_per_day = 1_000_000_000  # Peak day
        self.avg_comment_size = 500
        self.retention_days = 365 * 5  # 5 years
        
    def calculate_partition_strategy(self):
        daily_storage_gb = (
            self.comments_per_day * 
            self.avg_comment_size / (1024**3)
        )
        
        # Time-based partitioning
        partition_size_days = 7  # Weekly partitions
        partition_size_gb = daily_storage_gb * partition_size_days
        
        total_partitions = self.retention_days / partition_size_days
        
        return {
            'daily_storage_gb': daily_storage_gb,
            'partition_size_gb': partition_size_gb,
            'total_partitions': int(total_partitions),
            'total_storage_tb': daily_storage_gb * self.retention_days / 1024
        }
    
    def calculate_sharding_strategy(self):
        # Shard by event_id for even distribution
        target_shard_size_gb = 100  # 100GB per shard
        
        daily_storage_gb = (
            self.comments_per_day * 
            self.avg_comment_size / (1024**3)
        )
        
        shards_needed_daily = daily_storage_gb / target_shard_size_gb
        
        return {
            'shards_per_day': int(shards_needed_daily) + 1,
            'shard_size_gb': target_shard_size_gb,
            'total_shards_5_years': int(shards_needed_daily * 365 * 5)
        }

storage_calc = StoragePartitioning()
partition_strategy = storage_calc.calculate_partition_strategy()
sharding_strategy = storage_calc.calculate_sharding_strategy()

print(f"Partition strategy: {partition_strategy}")
print(f"Sharding strategy: {sharding_strategy}")

Availability Targets:
- Overall system availability: 99.95% (4.38 hours downtime/year)
- Comment submission success rate: 99.9%
- Comment delivery success rate: 99.5%
- Real-time latency SLO: 95% of comments <200ms

Failure Tolerance:
- Single server failure: No service impact
- Single datacenter failure: <30s failover
- Regional failure: <5 minutes failover
- Database failure: <1 minute failover with replica

class DisasterRecoveryPlanning:
    def __init__(self):
        self.rto_minutes = 5  # Recovery Time Objective
        self.rpo_minutes = 1  # Recovery Point Objective
        self.backup_frequency_minutes = 15
        
    def calculate_backup_requirements(self):
        daily_data_gb = 500  # Peak day comment data
        
        # Incremental backups every 15 minutes
        incremental_backup_gb = daily_data_gb / (24 * 60 / 15)
        
        # Full backup daily
        full_backup_gb = daily_data_gb
        
        # Cross-region replication bandwidth
        replication_bandwidth_mbps = (
            incremental_backup_gb * 1024 * 8 / (15 * 60)
        )
        
        return {
            'incremental_backup_gb': incremental_backup_gb,
            'full_backup_gb': full_backup_gb,
            'replication_bandwidth_mbps': replication_bandwidth_mbps,
            'backup_storage_tb_monthly': full_backup_gb * 30 / 1024
        }

dr_planning = DisasterRecoveryPlanning()
backup_req = dr_planning.calculate_backup_requirements()
print(f"Backup requirements: {backup_req}")

WebSocket Servers:
- 1000 servers @ $500/month = $500K/month
- Load balancers: $50K/month
- Monitoring and logging: $25K/month

Database Infrastructure:
- Primary databases: $200K/month
- Read replicas: $300K/month
- Backup storage: $50K/month

CDN and Bandwidth:
- CDN costs: $100K/month
- Data transfer: $200K/month
- Edge computing: $75K/month

Total Monthly Infrastructure: ~$1.5M
Annual Infrastructure Cost: ~$18M

class CostOptimization:
    def __init__(self):
        self.base_monthly_cost = 1_500_000  # $1.5M
        
    def calculate_optimization_savings(self):
        optimizations = {
            'auto_scaling': 0.20,  # 20% savings
            'reserved_instances': 0.30,  # 30% savings
            'data_compression': 0.15,  # 15% bandwidth savings
            'cache_optimization': 0.25,  # 25% database load reduction
            'regional_optimization': 0.10  # 10% latency improvement
        }
        
        total_savings = 0
        for optimization, savings_percent in optimizations.items():
            savings_amount = self.base_monthly_cost * savings_percent
            total_savings += savings_amount
            
        optimized_cost = self.base_monthly_cost - total_savings
        
        return {
            'base_monthly_cost': self.base_monthly_cost,
            'total_monthly_savings': total_savings,
            'optimized_monthly_cost': optimized_cost,
            'annual_savings': total_savings * 12
        }

cost_opt = CostOptimization()
savings = cost_opt.calculate_optimization_savings()
print(f"Cost optimization analysis: {savings}")

System Metrics (1-second granularity):
- Comments per second
- WebSocket connections count
- Database query latency
- Memory and CPU utilization
- Network bandwidth usage

Business Metrics (1-minute granularity):
- Active users per event
- Comment engagement rates
- Moderation action rates
- Revenue-impacting events

Application Metrics (5-second granularity):
- API response times
- Error rates by endpoint
- Cache hit/miss ratios
- Queue depths and processing times

class AlertingConfiguration:
    def __init__(self):
        self.thresholds = {
            'comment_latency_p95_ms': 200,
            'error_rate_percent': 1.0,
            'cpu_utilization_percent': 80,
            'memory_utilization_percent': 85,
            'disk_utilization_percent': 90,
            'connection_count_per_server': 45000,
            'queue_depth_messages': 10000
        }
        
    def generate_alert_rules(self):
        alert_rules = []
        
        for metric, threshold in self.thresholds.items():
            rule = {
                'metric': metric,
                'threshold': threshold,
                'duration': '2m',  # Alert after 2 minutes
                'severity': self._determine_severity(metric),
                'action': self._determine_action(metric)
            }
            alert_rules.append(rule)
            
        return alert_rules
    
    def _determine_severity(self, metric):
        critical_metrics = ['error_rate_percent', 'comment_latency_p95_ms']
        return 'critical' if metric in critical_metrics else 'warning'
    
    def _determine_action(self, metric):
        auto_scale_metrics = ['cpu_utilization_percent', 'connection_count_per_server']
        return 'auto_scale' if metric in auto_scale_metrics else 'notify'

alerting = AlertingConfiguration()
alert_rules = alerting.generate_alert_rules()
print(f"Alert rules configured: {len(alert_rules)}")