Architecture Patterns
Production-ready architecture patterns for deploying Alactic AGI at scale.
Single Instance Architecture
Basic Deployment
Suitable for small teams and development environments.
Architecture Diagram:
┌─────────────┐
│ Client │
└──────┬──────┘
│ HTTPS
▼
┌─────────────┐
│ Azure │
│ App │
│ Service │
└──────┬──────┘
│
├────────┐
│ │
▼ ▼
┌─────────┐ ┌──────────┐
│ Azure │ │ Azure │
│ Storage │ │ Search │
└─────────┘ └──────────┘
Characteristics:
- Single VM instance
- Shared storage
- No load balancing
- Manual scaling
- Cost-effective for low volume
When to Use:
- Development and testing
- Small teams (under 10 users)
- Low processing volume (under 100 documents/day)
- Non-critical applications
Configuration:
resources:
vm_size: Standard_B2s
instances: 1
storage: 100GB
region: eastus
scaling:
type: manual
min_instances: 1
max_instances: 1
High Availability Architecture
Multi-Instance Deployment
Production-ready setup with redundancy and failover.
Architecture Diagram:
┌─────────────┐
│ Azure │
│ Front │
│ Door │
└──────┬──────┘
│
┌────────────┴────────────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Region 1 │ │ Region 2 │
│ (Primary) │ │ (Secondary)│
└──────┬──────┘ └──────┬──────┘
│ │
┌────┴────┐ ┌────┴────┐
▼ ▼ ▼ ▼
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│App Svc │ │App Svc │ │App Svc │ │App Svc │
│Instance│ │Instance│ │Instance│ │Instance│
└────┬───┘ └───┬────┘ └────┬───┘ └───┬────┘
│ │ │ │
└────┬────┘ └────┬────┘
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Storage │◄────────►│ Storage │
│ (Primary) │ Sync │ (Replica) │
└─────────────┘ └─────────────┘
Characteristics:
- Multiple instances per region
- Cross-region redundancy
- Automatic failover
- Load balancing
- 99.95% SLA
When to Use:
- Production applications
- Business-critical workloads
- Geographic distribution required
- High availability needed
Configuration:
resources:
vm_size: Standard_D2s_v3
instances: 4
storage: 1TB
regions:
- eastus
- westus
load_balancer:
enabled: true
distribution: round_robin
health_check: /health
scaling:
type: auto
min_instances: 2
max_instances: 10
cpu_threshold: 70
Microservices Architecture
Service-Oriented Design
Decompose functionality into independent services.
Architecture Diagram:
┌─────────────┐
│ API │
│ Gateway │
└──────┬──────┘
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Document │ │ Web │ │ Search │
│ Processing │ │ Scraping │ │ Service │
│ Service │ │ Service │ │ │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
│ │ │
└────────┬────────┴────────┬────────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Azure │ │ Azure │
│ Storage │ │ Search │
└─────────────┘ └─────────────┘
Service Breakdown:
Document Processing Service:
- PDF, DOCX, TXT processing
- Text extraction
- Entity recognition
- Summarization
Web Scraping Service:
- URL processing
- Content extraction
- Link discovery
- Rate limiting
Search Service:
- Full-text search
- Vector search
- Filtering
- Ranking
API Gateway:
- Request routing
- Authentication
- Rate limiting
- Monitoring
Configuration:
services:
document_processor:
instances: 3
vm_size: Standard_D4s_v3
endpoints:
- /api/process
- /api/batch
web_scraper:
instances: 2
vm_size: Standard_D2s_v3
endpoints:
- /api/scrape
- /api/crawl
search:
instances: 2
vm_size: Standard_D2s_v3
endpoints:
- /api/search
- /api/search/vector
Event-Driven Architecture
Asynchronous Processing
Handle high-volume workloads with event-driven patterns.
Architecture Diagram:
┌─────────┐ ┌──────────────┐ ┌─────────────┐
│ Client │──────►│ API Service │──────►│ Azure │
└─────────┘ └──────────────┘ │ Event Grid │
└──────┬──────┘
│
┌────────────────────────────┼────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ │
│ Azure │ │ Azure │ │
│ Functions │ │ Functions │ │
│ (Processor) │ │ (Notifier) │ │
└──────┬───────┘ └──────────────┘ │
│ │
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Azure │ │ Logic │
│ Storage │ │ Apps │
└──────────────┘ └──────────────┘
Event Flow:
- Client uploads document via API
- API publishes event to Event Grid
- Function triggered for processing
- Results stored in Azure Storage
- Completion event published
- Notification sent to client
Implementation:
from azure.eventgrid import EventGridPublisherClient
from azure.core.credentials import AzureKeyCredential
# Publish event
def publish_processing_event(document_id, status):
client = EventGridPublisherClient(
endpoint=os.getenv('EVENT_GRID_ENDPOINT'),
credential=AzureKeyCredential(os.getenv('EVENT_GRID_KEY'))
)
event = {
'id': str(uuid.uuid4()),
'subject': f'documents/{document_id}',
'data': {
'document_id': document_id,
'status': status,
'timestamp': datetime.now().isoformat()
},
'event_type': 'document.processing.completed',
'data_version': '1.0'
}
client.send([event])
# Azure Function handler
def process_document_event(event):
document_id = event['data']['document_id']
# Process document
result = process_document(document_id)
# Publish completion event
publish_processing_event(document_id, 'completed')
Benefits:
- Decoupled components
- Scalable processing
- Fault tolerance
- Easy integration
Queue-Based Architecture
Reliable Message Processing
Use queues for guaranteed message delivery.
Architecture Diagram:
┌─────────┐ ┌──────────────┐ ┌──────────────┐
│ Client │──────►│ API Service │──────►│ Azure │
└─────────┘ └──────────────┘ │ Service │
│ Bus Queue │
└──────┬───────┘
│
│ Poll
▼
┌──────────────┐
│ Worker │
│ Processes │
└──────┬───────┘
│
▼
┌──────────────┐
│ Results │
│ Storage │
└──────────────┘
Implementation:
from azure.servicebus import ServiceBusClient, ServiceBusMessage
# Producer: Add to queue
def queue_document_processing(document_data):
client = ServiceBusClient.from_connection_string(
conn_str=os.getenv('SERVICE_BUS_CONN_STR')
)
with client:
sender = client.get_queue_sender(queue_name="document-processing")
message = ServiceBusMessage(json.dumps(document_data))
sender.send_messages(message)
# Consumer: Process from queue
def process_from_queue():
client = ServiceBusClient.from_connection_string(
conn_str=os.getenv('SERVICE_BUS_CONN_STR')
)
with client:
receiver = client.get_queue_receiver(queue_name="document-processing")
with receiver:
for msg in receiver:
try:
document_data = json.loads(str(msg))
# Process document
result = process_document(document_data)
# Complete message
receiver.complete_message(msg)
except Exception as e:
# Abandon message for retry
receiver.abandon_message(msg)
Benefits:
- Guaranteed delivery
- Load leveling
- Priority queuing
- Dead letter handling
Caching Strategy
Improve Performance
Implement caching at multiple levels.
Cache Layers:
┌─────────────┐
│ Client │
│ Cache │ ← Browser/App Cache
└──────┬──────┘
│
▼
┌─────────────┐
│ CDN │ ← Static Content
└──────┬──────┘
│
▼
┌─────────────┐
│ Redis │ ← Application Cache
│ Cache │
└──────┬──────┘
│
▼
┌─────────────┐
│ Database │ ← Persistent Storage
└─────────────┘
Redis Cache Implementation:
import redis
import json
import hashlib
class CachedProcessor:
def __init__(self, alactic_client, redis_client):
self.alactic = alactic_client
self.redis = redis_client
self.ttl = 3600 # 1 hour
def process_document(self, content, filename):
# Generate cache key
cache_key = hashlib.md5(content).hexdigest()
# Check cache
cached = self.redis.get(cache_key)
if cached:
return json.loads(cached)
# Process document
result = self.alactic.process_document(
content=content,
filename=filename
)
# Store in cache
self.redis.setex(
cache_key,
self.ttl,
json.dumps(result)
)
return result
Cache Configuration:
redis:
tier: Premium
capacity: P1
sku_family: P
enable_non_ssl_port: false
caching_rules:
- resource: processed_documents
ttl: 3600
- resource: search_results
ttl: 1800
- resource: user_sessions
ttl: 86400
Disaster Recovery
Business Continuity Planning
Ensure service availability during failures.
Recovery Strategy:
RPO (Recovery Point Objective): 1 hour RTO (Recovery Time Objective): 4 hours
Backup Configuration:
backup:
enabled: true
frequency: hourly
retention: 30_days
storage:
primary: eastus
secondary: westus
geo_redundant: true
recovery:
automated: true
test_frequency: quarterly
Failover Procedure:
def initiate_failover():
# 1. Verify primary region failure
if not check_region_health('eastus'):
# 2. Update DNS to secondary
update_traffic_manager(
primary='westus',
secondary='eastus'
)
# 3. Promote secondary database
promote_database_replica('westus')
# 4. Start services in secondary
start_services('westus')
# 5. Notify administrators
send_alert(
"Failover completed to westus region",
severity="high"
)
Monitoring Architecture
Observability Stack
Components:
┌─────────────────────────────────────────────┐
│ Application Insights │
│ (Logs, Metrics, Traces, Exceptions) │
└──────────────┬──────────────────────────────┘
│
┌───────┴───────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Azure │ │ Log │
│ Monitor │ │ Analytics │
└─────────────┘ └─────────────┘
│ │
└───────┬───────┘
▼
┌─────────────┐
│ Alerts │
│ & Actions │
└─────────────┘
Implementation:
from opencensus.ext.azure import metrics_exporter
from opencensus.ext.azure.log_exporter import AzureLogHandler
import logging
# Configure monitoring
logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(
connection_string='InstrumentationKey=...'
))
# Custom metrics
exporter = metrics_exporter.new_metrics_exporter(
connection_string='InstrumentationKey=...'
)
def track_processing_metrics(processing_time, success):
logger.info(
"Document processed",
extra={
'custom_dimensions': {
'processing_time': processing_time,
'success': success,
'timestamp': datetime.now().isoformat()
}
}
)
Cost Optimization
Resource Efficiency
Strategies:
- Auto-scaling: Scale based on demand
- Reserved Instances: Commit for discounts
- Spot Instances: Use for batch processing
- Storage Tiers: Move cold data to archive
- Resource Cleanup: Delete unused resources
Auto-scaling Configuration:
auto_scaling:
enabled: true
rules:
- metric: cpu_percentage
threshold: 70
scale_up: 2
scale_down: 1
- metric: request_count
threshold: 1000
scale_up: 3
scale_down: 1
schedule:
business_hours:
min_instances: 4
max_instances: 10
off_hours:
min_instances: 2
max_instances: 4