Audit Module Production Deployment Guide

This guide covers the complete production deployment process for the PDaaS Audit Module, including canary deployment, gradual rollout, and post-deployment monitoring.

Pre-Deployment Checklist
Infrastructure Prerequisites
Deployment Phases
Canary Deployment
Full Rollout
Rollback Procedures
Post-Deployment Monitoring
Troubleshooting

Pre-Deployment Checklist

Testing and Quality Assurance

Security Review

Security review completed and approved
No critical or high-severity vulnerabilities
Sensitive data sanitization verified
Multi-tenant isolation tested
TLS/SSL configuration validated
Authentication credentials secured

Infrastructure Readiness

Configuration and Documentation

Production environment variables configured
Excluded paths configuration reviewed
Index lifecycle policies configured
Operational runbook reviewed
Team trained on runbook procedures
Rollback plan documented and reviewed

Approvals

Infrastructure Prerequisites

OpenSearch Cluster Requirements

Cluster Sizing

Based on load testing results and expected traffic:

Production Cluster (Recommended):

Nodes: 3 data nodes (minimum)
Instance Type: r6g.2xlarge (8 vCPU, 64 GB RAM)
Storage: 1 TB SSD per node (3 TB total)
Replication: 1 replica (2 copies of data)
Shards: 5 primary shards per index

Traffic Capacity:

Events/second: 10,000-20,000 sustained
Daily events: 500M-1B
Daily storage: 50-100 GB
Retention: 90 days

Cluster Configuration

# opensearch.yml
cluster.name: pdaas-audit-prod
node.name: ${HOSTNAME}
network.host: 0.0.0.0

# Security
plugins.security.ssl.http.enabled: true
plugins.security.ssl.transport.enabled: true

# Performance
indices.memory.index_buffer_size: 30%
thread_pool.write.queue_size: 1000

# Snapshots
path.repo: ["/mnt/snapshots"]

Index Templates

Create index templates before deployment:

# Create audit index template
curl -X PUT "https://opensearch.prod.internal:9200/_index_template/audit-template" \
  -H 'Content-Type: application/json' \
  -u "admin:password" \
  -d '{
    "index_patterns": ["audit-*"],
    "template": {
      "settings": {
        "number_of_shards": 5,
        "number_of_replicas": 1,
        "refresh_interval": "30s",
        "index.codec": "best_compression"
      },
      "mappings": {
        "properties": {
          "occurred_at": {"type": "date"},
          "actor_id": {"type": "keyword"},
          "actor_type": {"type": "keyword"},
          "organization_id": {"type": "keyword"},
          "account_id": {"type": "keyword"},
          "action": {"type": "keyword"},
          "target": {"type": "keyword"},
          "service": {"type": "keyword"},
          "request_method": {"type": "keyword"},
          "request_path": {"type": "keyword"},
          "response_status_code": {"type": "short"},
          "response_duration_ms": {"type": "float"},
          "trace_id": {"type": "keyword"}
        }
      }
    }
  }'

Index Lifecycle Policy

Configure automated retention:

# Create ILM policy for 90-day retention
curl -X PUT "https://opensearch.prod.internal:9200/_plugins/_ism/policies/audit-lifecycle" \
  -H 'Content-Type: application/json' \
  -u "admin:password" \
  -d '{
    "policy": {
      "description": "Audit log lifecycle: hot(30d) -> warm(30d) -> cold(30d) -> delete",
      "default_state": "hot",
      "states": [
        {
          "name": "hot",
          "actions": [],
          "transitions": [{
            "state_name": "warm",
            "conditions": {"min_index_age": "30d"}
          }]
        },
        {
          "name": "warm",
          "actions": [
            {"replica_count": {"number_of_replicas": 1}}
          ],
          "transitions": [{
            "state_name": "cold",
            "conditions": {"min_index_age": "60d"}
          }]
        },
        {
          "name": "cold",
          "actions": [
            {"replica_count": {"number_of_replicas": 0}},
            {"read_only": {}}
          ],
          "transitions": [{
            "state_name": "delete",
            "conditions": {"min_index_age": "90d"}
          }]
        },
        {
          "name": "delete",
          "actions": [{"delete": {}}],
          "transitions": []
        }
      ]
    }
  }'

Application Configuration

Production Environment Variables

Create production .env file:

# Environment
ENV=production

# Audit Module - Production Configuration
AUDIT_ENABLED=true
AUDIT_SERVICE_NAME=api
AUDIT_ENVIRONMENT=production
AUDIT_SERVICE_VERSION=1.0.0

# OpenSearch Connection
AUDIT_OPENSEARCH_HOST=opensearch.prod.internal
AUDIT_OPENSEARCH_PORT=9200
AUDIT_OPENSEARCH_USERNAME=audit_writer
AUDIT_OPENSEARCH_PASSWORD=${OPENSEARCH_AUDIT_PASSWORD}  # From secrets manager
AUDIT_OPENSEARCH_USE_SSL=true
AUDIT_OPENSEARCH_VERIFY_CERTS=true
AUDIT_OPENSEARCH_CA_CERTS=/etc/ssl/certs/opensearch-ca.crt
AUDIT_OPENSEARCH_TIMEOUT=30

# Performance Tuning (Production)
AUDIT_BATCH_SIZE=500
AUDIT_FLUSH_INTERVAL_SECONDS=10.0
AUDIT_MAX_BODY_SIZE=20480
AUDIT_MAX_QUEUE_SIZE=50000

# Path Exclusion
AUDIT_EXCLUDED_PATHS=/health,/healthz,/metrics,/docs,/redoc,/openapi.json

# Index Configuration
AUDIT_INDEX_PREFIX=audit
AUDIT_INDEX_ROTATION=daily

# Retry Configuration
AUDIT_RETRY_ATTEMPTS=3
AUDIT_RETRY_BACKOFF_SECONDS=2.0
AUDIT_RETRY_BACKOFF_MULTIPLIER=2.0

OpenSearch User Setup

Create dedicated audit writer user with minimal permissions:

# Create audit_writer role
curl -X PUT "https://opensearch.prod.internal:9200/_plugins/_security/api/roles/audit_writer" \
  -H 'Content-Type: application/json' \
  -u "admin:password" \
  -d '{
    "cluster_permissions": ["cluster:monitor/health"],
    "index_permissions": [{
      "index_patterns": ["audit-*"],
      "allowed_actions": [
        "indices:data/write/index",
        "indices:data/write/bulk",
        "indices:admin/create"
      ]
    }]
  }'

# Create audit_writer user
curl -X PUT "https://opensearch.prod.internal:9200/_plugins/_security/api/internalusers/audit_writer" \
  -H 'Content-Type: application/json' \
  -u "admin:password" \
  -d '{
    "password": "STRONG_PASSWORD_HERE",
    "opendistro_security_roles": ["audit_writer"]
  }'

Deployment Phases

Phase 1: Infrastructure Setup (Day 0)

Duration: 2-4 hours

Provision OpenSearch cluster

# Using AWS CloudFormation, Terraform, or manual setup
terraform apply -var-file=production.tfvars

Verify cluster health

curl -u "admin:password" "https://opensearch.prod.internal:9200/_cluster/health?pretty"

Create index templates and policies

./scripts/setup-opensearch-production.sh

Configure monitoring

# Apply Prometheus scrape config
kubectl apply -f k8s/prometheus-config.yaml

# Import OpenSearch Dashboards
python backend/audit/dashboards/import_dashboard.py \
  --url https://opensearch.prod.internal:9200 \
  --username admin \
  --all

Configure alerting

# Apply AlertManager rules
kubectl apply -f k8s/alertmanager-rules.yaml

Phase 2: Canary Deployment (Day 1-2)

Duration: 24-48 hours Traffic: 10% of production

See detailed Canary Deployment section below.

Phase 3: Gradual Rollout (Day 3-5)

Duration: 48-72 hours Traffic: 10% → 25% → 50% → 100%

See detailed Full Rollout section below.

Phase 4: Post-Deployment Monitoring (Day 6-36)

Duration: 30 days Traffic: 100%

See detailed Post-Deployment Monitoring section below.

Canary Deployment

Overview

Canary deployment enables risk-free production deployment by:

Deploying to a small subset of traffic (10%)
Monitoring for issues without impacting all users
Quick rollback if problems detected
Confidence building before full rollout

Canary Strategy

Approach: Route-based canary using excluded paths

Initial State (Before Canary):

# backend/api/app.py - init_audit()
config = get_audit_config()

# Audit disabled or fully excluded
config.enabled = False  # OR
config.excluded_paths = ["/*"]  # Exclude all paths

Canary State (10% traffic):

# backend/api/app.py - init_audit()
config = get_audit_config()
config.enabled = True

# Only audit 10% of endpoints (exclude 90%)
config.excluded_paths = [
    "/health",
    "/metrics",
    "/docs",
    "/redoc",
    "/openapi.json",
    # Exclude most endpoints (keep only critical for canary)
    "/accounts/*",
    "/memberships/*",
    "/groups/*",
    "/service-accounts/*",
    # Keep: /authentication/*, /policies/*, /organizations/*
]

Canary Deployment Steps

Step 1: Pre-Deployment Validation (30 min)

# 1. Verify all tests pass
python -m pytest backend/audit/tests/ -v

# Expected: 488+ tests passing

# 2. Verify OpenSearch cluster health
curl -u "audit_writer:password" \
  "https://opensearch.prod.internal:9200/_cluster/health?pretty"

# Expected: {"status": "green"}

# 3. Verify application can connect
python -c "
from backend.audit.config import get_audit_config
from backend.audit.client import OpenSearchClientFactory
import asyncio

async def test():
    config = get_audit_config()
    client = await OpenSearchClientFactory.get_client(config)
    healthy = await client.health_check()
    print(f'OpenSearch healthy: {healthy}')

asyncio.run(test())
"

# Expected: OpenSearch healthy: True

Step 2: Deploy Canary (15 min)

# 1. Update environment variables
export AUDIT_ENABLED=true
export AUDIT_ENVIRONMENT=production
# ... other production vars

# 2. Deploy application with canary configuration
# Using Kubernetes rolling update
kubectl set env deployment/pdaas-api AUDIT_ENABLED=true
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/metrics,/accounts/*,/memberships/*,/groups/*,/service-accounts/*"

# Or using Docker
docker-compose -f docker-compose.prod.yml up -d --no-deps pdaas-api

# 3. Verify deployment
kubectl rollout status deployment/pdaas-api

# 4. Verify audit middleware is active
curl https://api.prod.internal/health

# Check logs for audit initialization
kubectl logs -l app=pdaas-api | grep "Audit system initialized"

Step 3: Monitor Canary (24 hours)

Monitoring Checklist:

Check Prometheus metrics (every 15 minutes for first hour, then hourly)

# Event ingestion rate
rate(audit_events_total[5m])

# Event processing latency (should be < 5ms p95)
histogram_quantile(0.95, rate(audit_events_latency_seconds_bucket[5m]))

# Error rate (should be < 0.1%)
rate(audit_errors_total[5m]) / rate(audit_events_total[5m])

# Queue size (should be < 1000)
audit_queue_size

# Circuit breaker state (should be 0 = CLOSED)
circuit_breaker_state

Check OpenSearch Dashboards (every hour)
- Navigate to "Audit System Health" dashboard
- Verify events are being written
- Check for errors or anomalies
- Review latency distribution

Check application logs (every 30 minutes)

kubectl logs -l app=pdaas-api --tail=100 | grep -i "audit\|error"

Check alerts (continuous)
- No critical alerts should fire
- Warning alerts acceptable if transient

Verify data in OpenSearch (every 2 hours)

# Count events written
curl -u "audit_writer:password" \
  "https://opensearch.prod.internal:9200/audit-*/_count?pretty"

# Sample recent events
curl -u "audit_writer:password" \
  "https://opensearch.prod.internal:9200/audit-*/_search?pretty&size=10&sort=occurred_at:desc"

Success Criteria (24 hours):

No critical alerts triggered
Error rate < 0.1%
Request latency impact < 5ms (p95)
OpenSearch write latency < 100ms (p95)
Queue size < 1,000 events
Circuit breaker remained CLOSED
No application errors related to audit
Events successfully written to OpenSearch
Multi-tenant isolation verified (spot checks)

Step 4: Canary Decision (15 min)

If success criteria met:

✅ Proceed to gradual rollout (Phase 3)
Document any minor issues for follow-up
Communicate success to stakeholders

If success criteria NOT met:

❌ Initiate rollback (see Rollback Procedures)
Conduct incident review
Fix issues before retry

Canary Rollback

If issues detected during canary:

# Quick rollback: Disable audit via environment variable
kubectl set env deployment/pdaas-api AUDIT_ENABLED=false

# Or: Exclude all paths (safer, keeps code path active)
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/*"

# Verify rollback
kubectl rollout status deployment/pdaas-api

# Verify audit disabled
kubectl logs -l app=pdaas-api | grep "Audit system disabled"

Full Rollout

Overview

After successful canary (24-48 hours), gradually increase traffic:

Day 3: 25% (remove more exclusions)
Day 4: 50% (remove more exclusions)
Day 5: 100% (audit all endpoints except health checks)

Rollout Steps

Day 3: 25% Traffic

# Update excluded paths to audit 25% of endpoints
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/metrics,/accounts/*,/memberships/*"

# Monitor for 24 hours using same checklist as canary

Success Criteria:

Same as canary, but with 2.5x traffic

Day 4: 50% Traffic

# Update excluded paths to audit 50% of endpoints
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/metrics,/accounts/*"

# Monitor for 24 hours

Success Criteria:

Same as canary, but with 5x traffic
OpenSearch cluster still healthy
Storage usage within projections

Day 5: 100% Traffic (Full Production)

# Update excluded paths to audit all except health/metrics
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/healthz,/metrics"

# Monitor for 24 hours, then 7 days, then 30 days

Success Criteria:

Same as canary, but with 10x traffic
Full compliance coverage achieved
No performance degradation

Rollout Decision Gates

Before proceeding to next phase, verify:

No critical alerts in previous phase
Error rate < 0.1% consistently
Performance targets met (< 5ms p95 latency)
OpenSearch cluster healthy (GREEN status)
Storage usage trending as expected
Team confidence high (no major issues)

If any gate fails:

Pause rollout
Investigate and fix issues
Re-run previous phase
Only proceed when gates pass

Rollback Procedures

Scenario 1: High Error Rate

Trigger: Error rate > 5% for 5+ minutes

Action:

# Immediate: Disable audit
kubectl set env deployment/pdaas-api AUDIT_ENABLED=false

# Or: Exclude all paths
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/*"

# Investigate errors
kubectl logs -l app=pdaas-api | grep -i "error" > error-log.txt

# Fix issues
# Re-deploy with fixes
# Retry from canary phase

Scenario 2: OpenSearch Unavailable

Trigger: Circuit breaker OPEN for 10+ minutes

Action:

# Verify circuit breaker is working (events going to logs)
kubectl logs -l app=pdaas-api | grep "Circuit breaker open"

# This is expected behavior - no rollback needed
# Events are being logged instead of lost

# Fix OpenSearch connectivity issue
# Circuit breaker will auto-close when OpenSearch recovers

Scenario 3: Performance Degradation

Trigger: Request latency increase > 10ms (p95)

Action:

# Investigate cause
kubectl top pods -l app=pdaas-api
kubectl logs -l app=pdaas-api | grep "latency\|slow"

# If caused by audit:
# Option 1: Tune batching parameters
kubectl set env deployment/pdaas-api AUDIT_BATCH_SIZE=1000
kubectl set env deployment/pdaas-api AUDIT_FLUSH_INTERVAL_SECONDS=15.0

# Option 2: Exclude more paths
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/metrics,/high-traffic-endpoint/*"

# Option 3: Full rollback (last resort)
kubectl set env deployment/pdaas-api AUDIT_ENABLED=false

Scenario 4: Storage Overflow

Trigger: OpenSearch disk usage > 80%

Action:

# Check storage usage
curl -u "admin:password" \
  "https://opensearch.prod.internal:9200/_cat/indices/audit-*?v&h=index,store.size&s=store.size:desc"

# Option 1: Reduce body size to save space
kubectl set env deployment/pdaas-api AUDIT_MAX_BODY_SIZE=5120  # 5KB instead of 20KB

# Option 2: Delete old indices manually
curl -X DELETE -u "admin:password" \
  "https://opensearch.prod.internal:9200/audit-*-2025-09-*"

# Option 3: Increase retention policy speed
# (Update ILM policy to delete after 30 days instead of 90)

# Option 4: Add storage capacity
# (Resize OpenSearch cluster)

Emergency Full Rollback

When: Critical production incident caused by audit module

Steps:

# 1. Disable audit immediately
kubectl set env deployment/pdaas-api AUDIT_ENABLED=false

# 2. Verify application recovery
curl https://api.prod.internal/health

# 3. Verify no audit activity
kubectl logs -l app=pdaas-api | grep "Audit system disabled"

# 4. Create incident report
# 5. Conduct post-mortem
# 6. Fix root cause
# 7. Re-test in staging
# 8. Retry production deployment from Phase 1

Post-Deployment Monitoring

First 24 Hours (Critical Period)

Monitoring Frequency: Every 15 minutes for first hour, then hourly

Key Metrics:

Event ingestion rate
Event processing latency
Error rate
Queue size
Circuit breaker state
OpenSearch cluster health
Storage growth rate

Actions:

Have on-call engineer monitoring
Keep rollback procedure ready
Log all anomalies for review

First 7 Days (Stabilization Period)

Monitoring Frequency: 3x daily (morning, afternoon, evening)

Key Activities:

Review dashboards for trends
Analyze error logs
Check storage usage growth
Verify multi-tenant isolation
Collect feedback from team
Tune configuration if needed

First 30 Days (Validation Period)

Monitoring Frequency: Daily

Success Metrics:

Availability: 99.9% uptime
Performance: < 5ms latency impact (p95)
Reliability: < 0.1% error rate
Compliance: 100% coverage of security operations
Storage: Usage within 10% of projections
Incidents: Zero critical incidents

End of 30 Days:

Conduct deployment retrospective
Document lessons learned
Update runbook with production insights
Mark feature as "Generally Available" (GA)

Troubleshooting

Issue: Events Not Appearing in OpenSearch

Symptoms:

Prometheus shows events emitted (audit_events_total increasing)
OpenSearch shows no events (audit-* indices empty or missing)

Diagnosis:

# 1. Check circuit breaker state
curl http://api.prod.internal/metrics | grep circuit_breaker_state

# 2. Check OpenSearch connectivity
kubectl exec -it pdaas-api-pod -- python -c "
from backend.audit.client import OpenSearchClientFactory
from backend.audit.config import get_audit_config
import asyncio

async def test():
    config = get_audit_config()
    client = await OpenSearchClientFactory.get_client(config)
    healthy = await client.health_check()
    print(f'Healthy: {healthy}')

asyncio.run(test())
"

# 3. Check application logs
kubectl logs -l app=pdaas-api | grep -i "opensearch\|circuit"

Resolution:

If circuit breaker OPEN: Fix OpenSearch connectivity
If authentication failed: Verify credentials
If network issue: Check firewall/security groups

Issue: High Memory Usage

Symptoms:

Application pod memory usage increasing
OOMKilled errors in Kubernetes

Diagnosis:

# Check queue size
curl http://api.prod.internal/metrics | grep audit_queue_size

Resolution:

# Reduce queue size
kubectl set env deployment/pdaas-api AUDIT_MAX_QUEUE_SIZE=5000

# Increase flush frequency
kubectl set env deployment/pdaas-api AUDIT_FLUSH_INTERVAL_SECONDS=2.0

# Increase batch size (fewer flushes)
kubectl set env deployment/pdaas-api AUDIT_BATCH_SIZE=1000

Issue: OpenSearch Indices Growing Too Fast

Symptoms:

Disk usage exceeding projections
Cluster disk watermark warnings

Diagnosis:

# Check index sizes
curl -u "admin:password" \
  "https://opensearch.prod.internal:9200/_cat/indices/audit-*?v&h=index,docs.count,store.size&s=store.size:desc"

# Check average event size
curl -u "admin:password" \
  "https://opensearch.prod.internal:9200/audit-*/_stats?pretty" | jq '.indices | to_entries | map({index: .key, docs: .value.total.docs.count, size: .value.total.store.size_in_bytes}) | map(.avg_size = (.size / .docs))'

Resolution:

# Reduce body size
kubectl set env deployment/pdaas-api AUDIT_MAX_BODY_SIZE=5120  # 5KB

# Exclude high-volume, low-value endpoints
kubectl set env deployment/pdaas-api AUDIT_EXCLUDED_PATHS="/health,/metrics,/high-volume-endpoint/*"

# Accelerate ILM policy (delete sooner)
# Update ILM policy to delete after 30 days instead of 90

Best Practices

Configuration Management

Use secrets manager for sensitive values (passwords, keys)
Version control environment configuration
Document changes to production configuration
Test configuration in staging before production

Monitoring and Alerting

Set up alerts before deployment
Test alerts in staging
Document alert runbooks for on-call engineers
Review alerts weekly for tuning

Team Readiness

Train team on runbook procedures
Conduct drills for rollback scenarios
Assign clear roles (deployer, monitor, approver)
Establish communication channels (Slack, email)

Documentation

Keep runbook updated with production learnings
Document all incidents for pattern analysis
Share knowledge across team
Maintain deployment log with timestamps and decisions

Success Criteria

Deployment is considered successful when:

✅ All 488+ tests passing
✅ Zero critical incidents in 30 days
✅ Performance targets met (< 5ms latency, < 0.1% errors)
✅ 99.9% availability achieved
✅ Storage usage within projections
✅ Team confident in operating system
✅ All documentation complete and accurate
✅ Stakeholders satisfied with compliance coverage

Next Steps

After successful production deployment:

Monitor for 30 days - Validate stability
Collect feedback - From security, compliance, and operations teams
Optimize configuration - Based on production data
Plan enhancements - Epic 06-08 features (retention, workers, compliance)
Share success - Document case study and lessons learned

Support

For deployment assistance:

Operational Runbook: Audit Operations Runbook
Configuration Guide: Audit Configuration Guide
Architecture Overview: Audit Trails Overview
Team Contact: #audit-module-support on Slack

Table of Contents​

Pre-Deployment Checklist​

Testing and Quality Assurance​

Security Review​

Infrastructure Readiness​

Configuration and Documentation​

Approvals​

Infrastructure Prerequisites​

OpenSearch Cluster Requirements​

Cluster Sizing​

Cluster Configuration​

Index Templates​

Index Lifecycle Policy​

Application Configuration​

Production Environment Variables​

OpenSearch User Setup​

Deployment Phases​

Phase 1: Infrastructure Setup (Day 0)​

Phase 2: Canary Deployment (Day 1-2)​

Phase 3: Gradual Rollout (Day 3-5)​

Phase 4: Post-Deployment Monitoring (Day 6-36)​

Canary Deployment​

Overview​

Canary Strategy​

Canary Deployment Steps​

Step 1: Pre-Deployment Validation (30 min)​

Step 2: Deploy Canary (15 min)​

Step 3: Monitor Canary (24 hours)​

Step 4: Canary Decision (15 min)​

Canary Rollback​

Full Rollout​

Overview​

Rollout Steps​

Day 3: 25% Traffic​

Day 4: 50% Traffic​

Day 5: 100% Traffic (Full Production)​

Rollout Decision Gates​

Rollback Procedures​

Scenario 1: High Error Rate​

Scenario 2: OpenSearch Unavailable​

Scenario 3: Performance Degradation​

Scenario 4: Storage Overflow​

Emergency Full Rollback​

Post-Deployment Monitoring​

First 24 Hours (Critical Period)​

First 7 Days (Stabilization Period)​

First 30 Days (Validation Period)​

Troubleshooting​

Issue: Events Not Appearing in OpenSearch​

Issue: High Memory Usage​

Issue: OpenSearch Indices Growing Too Fast​

Best Practices​

Configuration Management​

Monitoring and Alerting​

Team Readiness​

Documentation​

Success Criteria​

Next Steps​

Support​

Table of Contents

Pre-Deployment Checklist

Testing and Quality Assurance

Security Review

Infrastructure Readiness

Configuration and Documentation

Approvals

Infrastructure Prerequisites

OpenSearch Cluster Requirements

Cluster Sizing

Cluster Configuration

Index Templates

Index Lifecycle Policy

Application Configuration

Production Environment Variables

OpenSearch User Setup

Deployment Phases

Phase 1: Infrastructure Setup (Day 0)

Phase 2: Canary Deployment (Day 1-2)

Phase 3: Gradual Rollout (Day 3-5)

Phase 4: Post-Deployment Monitoring (Day 6-36)

Canary Deployment

Overview

Canary Strategy

Canary Deployment Steps

Step 1: Pre-Deployment Validation (30 min)

Step 2: Deploy Canary (15 min)

Step 3: Monitor Canary (24 hours)

Step 4: Canary Decision (15 min)

Canary Rollback

Full Rollout

Overview

Rollout Steps

Day 3: 25% Traffic

Day 4: 50% Traffic

Day 5: 100% Traffic (Full Production)

Rollout Decision Gates

Rollback Procedures

Scenario 1: High Error Rate

Scenario 2: OpenSearch Unavailable

Scenario 3: Performance Degradation

Scenario 4: Storage Overflow

Emergency Full Rollback

Post-Deployment Monitoring

First 24 Hours (Critical Period)

First 7 Days (Stabilization Period)

First 30 Days (Validation Period)

Troubleshooting

Issue: Events Not Appearing in OpenSearch

Issue: High Memory Usage

Issue: OpenSearch Indices Growing Too Fast

Best Practices

Configuration Management

Monitoring and Alerting

Team Readiness

Documentation

Success Criteria

Next Steps

Support