This guide covers enterprise-level scaling strategies for the Telegram Audio Downloader, including horizontal scaling, vertical scaling, auto-scaling, and performance optimization for high-throughput environments.
Single Instance Limits:
Max Concurrent Downloads: 20
Memory Usage: ~500MB
CPU Usage: 2-4 cores
Throughput: ~100 files/hour
Database Connections: 20
Target Enterprise Scale:
Concurrent Downloads: 1000+
Instances: 10-50
Throughput: 10,000+ files/hour
Database Connections: 500+
Redis Connections: 200+
graph TB
LB[Load Balancer] --> A1[App Instance 1]
LB --> A2[App Instance 2]
LB --> A3[App Instance N]
A1 --> DB[(PostgreSQL Cluster)]
A2 --> DB
A3 --> DB
A1 --> RD[(Redis Cluster)]
A2 --> RD
A3 --> RD
A1 --> Q[Message Queue]
A2 --> Q
A3 --> Q
Q --> W1[Worker 1]
Q --> W2[Worker 2]
Q --> WN[Worker N]
# k8s/hpa-advanced.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: telegram-audio-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: telegram-audio-app
minReplicas: 5
maxReplicas: 50
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 60
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 70
- type: Pods
pods:
metric:
name: active_downloads
target:
type: AverageValue
averageValue: "10"
behavior:
scaleDown:
stabilizationWindowSeconds: 300
policies:
- type: Percent
value: 50
periodSeconds: 60
scaleUp:
stabilizationWindowSeconds: 60
policies:
- type: Percent
value: 100
periodSeconds: 30
- type: Pods
value: 5
periodSeconds: 30
selectPolicy: Max
# metrics/custom_metrics.py
from prometheus_client import Gauge, Counter, Histogram
import asyncio
# Custom metrics for scaling decisions
active_downloads_gauge = Gauge('telegram_audio_active_downloads', 'Number of active downloads')
queue_size_gauge = Gauge('telegram_audio_queue_size', 'Size of download queue')
download_duration = Histogram('telegram_audio_download_duration_seconds', 'Download duration')
download_rate = Counter('telegram_audio_downloads_total', 'Total downloads completed')
class ScalingMetrics:
"""Metrics collector for scaling decisions."""
def __init__(self):
self.active_downloads = 0
self.queue_size = 0
async def update_metrics(self):
"""Update metrics for scaling decisions."""
while True:
# Update active downloads
active_downloads_gauge.set(self.active_downloads)
# Update queue size
queue_size = await self.get_queue_size()
queue_size_gauge.set(queue_size)
# Calculate scaling recommendation
scaling_factor = self.calculate_scaling_factor()
await asyncio.sleep(10) # Update every 10 seconds
def calculate_scaling_factor(self) -> float:
"""Calculate recommended scaling factor."""
if self.queue_size > 100:
return 2.0 # Scale up aggressively
elif self.queue_size > 50:
return 1.5 # Scale up moderately
elif self.queue_size < 10 and self.active_downloads < 5:
return 0.5 # Scale down
return 1.0 # No scaling needed
# nginx-scaling.conf
upstream telegram_audio_backend {
least_conn;
server app1:8080 weight=1 max_fails=3 fail_timeout=30s;
server app2:8080 weight=1 max_fails=3 fail_timeout=30s;
server app3:8080 weight=1 max_fails=3 fail_timeout=30s;
# Auto-scaling targets
server app4:8080 weight=1 max_fails=3 fail_timeout=30s backup;
server app5:8080 weight=1 max_fails=3 fail_timeout=30s backup;
}
server {
listen 80;
# Connection pooling
upstream_keepalive_connections 32;
upstream_keepalive_requests 1000;
upstream_keepalive_timeout 60s;
location / {
proxy_pass http://telegram_audio_backend;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# Load balancing headers
proxy_set_header X-Instance-ID $upstream_addr;
# Timeouts for scaling
proxy_connect_timeout 5s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
}
# Health check endpoint
location /health {
access_log off;
proxy_pass http://telegram_audio_backend/health;
proxy_connect_timeout 2s;
proxy_send_timeout 2s;
proxy_read_timeout 2s;
}
}
# PostgreSQL Cluster Configuration
postgresql:
replication:
enabled: true
readReplicas: 3
synchronousCommit: "on"
maxConnections: 500
pooling:
enabled: true
pgbouncer:
poolSize: 100
maxClientConnections: 1000
defaultPoolSize: 50
sharding:
enabled: true
shardKey: "user_id"
shards:
- name: "shard1"
weight: 33
- name: "shard2"
weight: 33
- name: "shard3"
weight: 34
# k8s/deployment-optimized.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: telegram-audio-app-optimized
spec:
replicas: 3
template:
spec:
containers:
- name: app
image: telegram-audio-downloader:latest
resources:
requests:
memory: "1Gi"
cpu: "1000m"
ephemeral-storage: "2Gi"
limits:
memory: "4Gi"
cpu: "2000m"
ephemeral-storage: "10Gi"
env:
- name: WORKERS
value: "8"
- name: MAX_CONCURRENT_DOWNLOADS
value: "20"
- name: MEMORY_LIMIT
value: "3Gi"
# memory/optimization.py
import gc
import asyncio
from memory_profiler import profile
class MemoryOptimizer:
"""Memory optimization for vertical scaling."""
def __init__(self, max_memory_mb: int = 3000):
self.max_memory_mb = max_memory_mb
self.cleanup_threshold = max_memory_mb * 0.8
async def monitor_memory(self):
"""Monitor and optimize memory usage."""
while True:
current_memory = self.get_memory_usage()
if current_memory > self.cleanup_threshold:
await self.aggressive_cleanup()
elif current_memory > self.max_memory_mb * 0.6:
await self.moderate_cleanup()
await asyncio.sleep(30)
async def aggressive_cleanup(self):
"""Aggressive memory cleanup."""
# Force garbage collection
gc.collect()
# Clear caches
await self.clear_download_cache()
await self.clear_metadata_cache()
# Reduce concurrent operations
await self.reduce_concurrency()
@profile
async def memory_efficient_download(self, url: str, chunk_size: int = 1024*1024):
"""Memory-efficient download with streaming."""
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
with open(filename, 'wb') as f:
async for chunk in response.content.iter_chunked(chunk_size):
f.write(chunk)
# Yield control to prevent memory accumulation
await asyncio.sleep(0)
# cpu/optimization.py
import asyncio
import multiprocessing
from concurrent.futures import ProcessPoolExecutor
class CPUOptimizer:
"""CPU optimization for vertical scaling."""
def __init__(self):
self.cpu_count = multiprocessing.cpu_count()
self.process_pool = ProcessPoolExecutor(max_workers=self.cpu_count)
async def cpu_intensive_task(self, task_data):
"""Run CPU-intensive tasks in separate processes."""
loop = asyncio.get_event_loop()
# Offload to process pool
result = await loop.run_in_executor(
self.process_pool,
self.process_audio_file,
task_data
)
return result
def process_audio_file(self, file_path: str):
"""CPU-intensive audio processing."""
# Metadata extraction, format conversion, etc.
# This runs in a separate process
pass
# autoscaler/controller.py
import asyncio
import kubernetes
from typing import Dict, List
class AutoScaler:
"""Custom auto-scaling controller."""
def __init__(self, namespace: str = "telegram-audio"):
self.k8s_client = kubernetes.client.AppsV1Api()
self.namespace = namespace
self.target_cpu_utilization = 60
self.target_memory_utilization = 70
async def scale_controller(self):
"""Main scaling control loop."""
while True:
try:
metrics = await self.get_metrics()
scaling_decision = self.make_scaling_decision(metrics)
if scaling_decision['action'] != 'none':
await self.execute_scaling(scaling_decision)
except Exception as e:
print(f"Scaling error: {e}")
await asyncio.sleep(60) # Check every minute
async def get_metrics(self) -> Dict:
"""Get current metrics for scaling decision."""
return {
'cpu_utilization': await self.get_cpu_utilization(),
'memory_utilization': await self.get_memory_utilization(),
'active_downloads': await self.get_active_downloads(),
'queue_size': await self.get_queue_size(),
'current_replicas': await self.get_current_replicas()
}
def make_scaling_decision(self, metrics: Dict) -> Dict:
"""Make scaling decision based on metrics."""
current_replicas = metrics['current_replicas']
# Scale up conditions
if (metrics['cpu_utilization'] > 80 or
metrics['memory_utilization'] > 85 or
metrics['queue_size'] > 100):
return {
'action': 'scale_up',
'target_replicas': min(current_replicas * 2, 50)
}
# Scale down conditions
elif (metrics['cpu_utilization'] < 30 and
metrics['memory_utilization'] < 40 and
metrics['queue_size'] < 10):
return {
'action': 'scale_down',
'target_replicas': max(current_replicas // 2, 3)
}
return {'action': 'none'}
async def execute_scaling(self, decision: Dict):
"""Execute scaling decision."""
deployment_name = "telegram-audio-app"
# Update deployment
body = {
'spec': {
'replicas': decision['target_replicas']
}
}
self.k8s_client.patch_namespaced_deployment_scale(
name=deployment_name,
namespace=self.namespace,
body=body
)
print(f"Scaled to {decision['target_replicas']} replicas")
# autoscaler/predictive.py
import numpy as np
from sklearn.linear_model import LinearRegression
import asyncio
class PredictiveScaler:
"""Predictive auto-scaling based on historical patterns."""
def __init__(self):
self.model = LinearRegression()
self.history = []
async def collect_metrics(self):
"""Collect metrics for prediction."""
while True:
metrics = {
'timestamp': time.time(),
'cpu_usage': await get_cpu_usage(),
'memory_usage': await get_memory_usage(),
'download_rate': await get_download_rate(),
'queue_size': await get_queue_size()
}
self.history.append(metrics)
# Keep only last 24 hours
cutoff_time = time.time() - 86400
self.history = [m for m in self.history if m['timestamp'] > cutoff_time]
await asyncio.sleep(60)
def predict_load(self, minutes_ahead: int = 15) -> Dict:
"""Predict load for next N minutes."""
if len(self.history) < 10:
return {'cpu': 50, 'memory': 50, 'queue': 0}
# Prepare features (time-based patterns)
X = np.array([[h['timestamp'] % 86400] for h in self.history]) # Time of day
# Predict CPU usage
y_cpu = np.array([h['cpu_usage'] for h in self.history])
self.model.fit(X, y_cpu)
future_time = (time.time() + minutes_ahead * 60) % 86400
predicted_cpu = self.model.predict([[future_time]])[0]
# Predict memory usage
y_memory = np.array([h['memory_usage'] for h in self.history])
self.model.fit(X, y_memory)
predicted_memory = self.model.predict([[future_time]])[0]
return {
'cpu': max(0, min(100, predicted_cpu)),
'memory': max(0, min(100, predicted_memory)),
'confidence': self.calculate_confidence()
}
def calculate_recommended_replicas(self, prediction: Dict) -> int:
"""Calculate recommended replicas based on prediction."""
if prediction['confidence'] < 0.7:
return 0 # Don't scale if confidence is low
if prediction['cpu'] > 75 or prediction['memory'] > 80:
return +2 # Scale up
elif prediction['cpu'] < 25 and prediction['memory'] < 30:
return -1 # Scale down
return 0 # No change
# monitoring/scaling_dashboard.py
from flask import Flask, jsonify, render_template
import asyncio
app = Flask(__name__)
class ScalingDashboard:
"""Dashboard for monitoring scaling metrics."""
@app.route('/scaling/metrics')
async def get_scaling_metrics():
"""Get current scaling metrics."""
return jsonify({
'current_replicas': await get_current_replicas(),
'target_replicas': await get_target_replicas(),
'cpu_utilization': await get_avg_cpu_utilization(),
'memory_utilization': await get_avg_memory_utilization(),
'scaling_events': await get_recent_scaling_events(),
'performance_metrics': {
'downloads_per_second': await get_downloads_per_second(),
'avg_response_time': await get_avg_response_time(),
'error_rate': await get_error_rate()
}
})
@app.route('/scaling/recommendations')
async def get_scaling_recommendations():
"""Get scaling recommendations."""
predictor = PredictiveScaler()
prediction = predictor.predict_load(15)
return jsonify({
'prediction': prediction,
'recommended_action': predictor.calculate_recommended_replicas(prediction),
'reasoning': generate_scaling_reasoning(prediction)
})
# monitoring/scaling-alerts.yml
groups:
- name: scaling-alerts
rules:
- alert: ScalingEventHigh
expr: increase(kubernetes_deployment_spec_replicas[5m]) > 5
for: 1m
labels:
severity: warning
annotations:
summary: "Rapid scaling up detected"
description: "Deployment scaled up by replicas in 5 minutes"
- alert: ScalingThrashing
expr: changes(kubernetes_deployment_spec_replicas[30m]) > 10
for: 5m
labels:
severity: critical
annotations:
summary: "Scaling thrashing detected"
description: "Deployment scaled times in 30 minutes"
- alert: MaxReplicasReached
expr: kubernetes_deployment_spec_replicas >= 50
for: 1m
labels:
severity: critical
annotations:
summary: "Maximum replicas reached"
description: "Deployment has reached maximum replica count"
# Scaling Configuration
scaling_policies:
conservative:
scale_up:
cpu_threshold: 80
memory_threshold: 85
cooldown: 300s
max_scale_factor: 1.5
scale_down:
cpu_threshold: 30
memory_threshold: 40
cooldown: 600s
max_scale_factor: 0.7
aggressive:
scale_up:
cpu_threshold: 70
memory_threshold: 75
cooldown: 120s
max_scale_factor: 2.0
scale_down:
cpu_threshold: 40
memory_threshold: 50
cooldown: 300s
max_scale_factor: 0.8
# Resource planning calculator
class ResourcePlanner:
"""Calculate required resources for target throughput."""
def calculate_required_resources(self, target_throughput: int) -> Dict:
"""Calculate resources needed for target downloads per hour."""
# Base metrics per instance
base_throughput = 100 # downloads per hour per instance
base_cpu = 1000 # millicores
base_memory = 500 # MB
# Calculate required instances
required_instances = math.ceil(target_throughput / base_throughput)
# Add overhead and safety margin
safety_margin = 1.3
required_instances = int(required_instances * safety_margin)
return {
'instances': required_instances,
'total_cpu_cores': (required_instances * base_cpu) / 1000,
'total_memory_gb': (required_instances * base_memory) / 1024,
'estimated_cost_per_hour': required_instances * 0.1, # $0.1 per instance
'database_connections': required_instances * 20,
'redis_connections': required_instances * 10
}
Scale Targets:
Small (1-3 instances):
Throughput: 100-300 downloads/hour
Latency: <100ms
Memory: <500MB per instance
CPU: <2 cores total
Medium (3-10 instances):
Throughput: 300-1000 downloads/hour
Latency: <150ms
Memory: <5GB total
CPU: <10 cores total
Large (10-25 instances):
Throughput: 1000-2500 downloads/hour
Latency: <200ms
Memory: <25GB total
CPU: <50 cores total
Enterprise (25-50 instances):
Throughput: 2500-5000 downloads/hour
Latency: <300ms
Memory: <100GB total
CPU: <100 cores total
# Monitor scaling events
kubectl get events --field-selector reason=SuccessfulCreate
kubectl get events --field-selector reason=SuccessfulDelete
# Check HPA status
kubectl get hpa telegram-audio-hpa -o yaml
# Monitor resource usage
kubectl top pods -l app=telegram-audio
kubectl top nodes
# Check scaling metrics
curl http://prometheus:9090/api/v1/query?query=kubernetes_deployment_spec_replicas
This comprehensive scaling guide provides enterprise-level strategies for handling high-throughput scenarios with the Telegram Audio Downloader.