# Setup
cd ~/dev/foundation
iex -S mix

# Test Script
defmodule FoundationF1Test do
  def run_scenario do
    IO.puts("🟢 Foundation Scenario F1: Local Development Workflow")
    start_time = System.monotonic_time(:millisecond)
    
    # Step 1: Basic Service Verification
    assert Foundation.available?()
    assert Foundation.Config.available?()
    assert Foundation.Events.available?()
    assert Foundation.Telemetry.available?()
    
    # Step 2: Configuration Operations
    {:ok, config} = Foundation.Config.get()
    {:ok, debug_mode} = Foundation.Config.get([:dev, :debug_mode])
    
    # Step 3: Event System Workflow
    correlation_id = Foundation.Utils.generate_correlation_id()
    {:ok, event} = Foundation.Events.new_event(:dev_workflow, %{
      action: :code_change,
      file: "lib/my_app/user.ex",
      lines_changed: 25
    }, correlation_id: correlation_id)
    
    {:ok, event_id} = Foundation.Events.store(event)
    {:ok, retrieved} = Foundation.Events.get(event_id)
    
    # Step 4: Telemetry Workflow
    Foundation.Telemetry.emit_counter([:dev, :compilation], %{success: true})
    Foundation.Telemetry.measure([:dev, :test_suite], %{}, fn ->
      :timer.sleep(100)  # Simulate test run
      {:ok, %{tests: 45, failures: 0}}
    end)
    
    # Step 5: Development Load Test
    # Simulate rapid development cycle (file saves, test runs)
    for i <- 1..50 do
      {:ok, change_event} = Foundation.Events.new_event(:file_change, %{
        file: "lib/my_app/module_#{i}.ex",
        timestamp: System.os_time(:microsecond)
      })
      Foundation.Events.store(change_event)
      Foundation.Telemetry.emit_counter([:dev, :file_saves], %{file_type: :elixir})
    end
    
    end_time = System.monotonic_time(:millisecond)
    duration = end_time - start_time
    
    IO.puts("✅ F1 Completed in #{duration}ms")
    IO.puts("📊 Metrics: 50 events stored, configuration read, telemetry working")
    
    # Verify state
    {:ok, events} = Foundation.Events.query(%{event_type: :file_change, limit: 10})
    assert length(events) >= 10
    
    {:ok, metrics} = Foundation.Telemetry.get_metrics()
    assert is_map(metrics)
    
    :ok
  end
end

# Run it
FoundationF1Test.run_scenario()

# Test Script - Run in IEx
defmodule FoundationF2Test do
  def run_scenario do
    IO.puts("🟢 Foundation Scenario F2: Configuration Management Under Load")
    
    # Simulate SaaS application with feature flags
    feature_flags = [
      [:features, :new_dashboard],
      [:features, :beta_analytics], 
      [:features, :advanced_search],
      [:features, :dark_mode],
      [:features, :real_time_chat]
    ]
    
    # Step 1: Set initial feature flags
    for flag_path <- feature_flags do
      if flag_path in Foundation.Config.updatable_paths() do
        Foundation.Config.update(flag_path, false)
      end
    end
    
    # Step 2: Simulate A/B testing - rapid flag changes
    tasks = for i <- 1..20 do
      Task.async(fn ->
        results = []
        
        # Each task simulates a different user segment
        for _change <- 1..25 do
          flag = Enum.random(feature_flags)
          
          if flag in Foundation.Config.updatable_paths() do
            # Random enable/disable
            new_value = :rand.uniform() > 0.5
            result = Foundation.Config.update(flag, new_value)
            
            # Simulate application reading config after change
            :timer.sleep(:rand.uniform(50))
            {:ok, current_value} = Foundation.Config.get(flag)
            
            [%{flag: flag, set_value: new_value, read_value: current_value, result: result} | results]
          else
            results
          end
        end
        
        {i, results}
      end)
    end
    
    # Step 3: Concurrent configuration reads (simulating high-traffic app)
    read_tasks = for i <- 1..10 do
      Task.async(fn ->
        reads = for _read <- 1..100 do
          flag = Enum.random(feature_flags)
          start_time = System.monotonic_time(:microsecond)
          {:ok, value} = Foundation.Config.get(flag)
          end_time = System.monotonic_time(:microsecond)
          
          %{flag: flag, value: value, duration_us: end_time - start_time}
        end
        
        {i, reads}
      end)
    end
    
    # Wait for all tasks
    update_results = Task.await_many(tasks, 30_000)
    read_results = Task.await_many(read_tasks, 30_000)
    
    # Analysis
    total_updates = update_results |> Enum.map(fn {_i, results} -> length(results) end) |> Enum.sum()
    total_reads = read_results |> Enum.map(fn {_i, reads} -> length(reads) end) |> Enum.sum()
    
    avg_read_time = read_results
                    |> Enum.flat_map(fn {_i, reads} -> reads end)
                    |> Enum.map(& &1.duration_us)
                    |> Enum.sum()
                    |> div(total_reads)
    
    IO.puts("✅ F2 Completed")
    IO.puts("📊 Updates: #{total_updates}, Reads: #{total_reads}")
    IO.puts("📊 Average read time: #{avg_read_time}μs")
    
    # Assertions
    assert total_updates > 0
    assert total_reads > 0
    assert avg_read_time < 10_000  # Should be under 10ms
    
    :ok
  end
end

FoundationF2Test.run_scenario()

defmodule FoundationF3Test do
  def run_scenario do
    IO.puts("🟢 Foundation Scenario F3: Event System Performance Baseline")
    
    # Simulate e-commerce application events
    event_types = [
      :user_login, :user_logout, :page_view, :product_view,
      :add_to_cart, :remove_from_cart, :purchase_started,
      :payment_processed, :order_confirmed, :shipment_created
    ]
    
    # Step 1: Single-threaded baseline
    IO.puts("📊 Phase 1: Single-threaded event creation and storage")
    start_time = System.monotonic_time(:millisecond)
    
    single_thread_events = for i <- 1..1000 do
      event_type = Enum.random(event_types)
      {:ok, event} = Foundation.Events.new_event(event_type, %{
        user_id: "user_#{:rand.uniform(100)}",
        session_id: Foundation.Utils.generate_correlation_id(),
        timestamp: System.os_time(:microsecond),
        sequence: i
      })
      
      {:ok, event_id} = Foundation.Events.store(event)
      event_id
    end
    
    single_thread_time = System.monotonic_time(:millisecond) - start_time
    
    IO.puts("📊 Single-thread: 1000 events in #{single_thread_time}ms")
    IO.puts("📊 Rate: #{1000 * 1000 / single_thread_time |> Float.round(1)} events/sec")
    
    # Step 2: Multi-threaded event creation
    IO.puts("📊 Phase 2: Multi-threaded event creation (10 workers)")
    start_time = System.monotonic_time(:millisecond)
    
    multi_tasks = for worker_id <- 1..10 do
      Task.async(fn ->
        events_per_worker = 200
        
        worker_events = for i <- 1..events_per_worker do
          event_type = Enum.random(event_types)
          {:ok, event} = Foundation.Events.new_event(event_type, %{
            user_id: "user_#{:rand.uniform(1000)}",
            session_id: Foundation.Utils.generate_correlation_id(),
            worker_id: worker_id,
            sequence: i,
            timestamp: System.os_time(:microsecond)
          })
          
          {:ok, event_id} = Foundation.Events.store(event)
          event_id
        end
        
        {worker_id, worker_events}
      end)
    end
    
    multi_results = Task.await_many(multi_tasks, 30_000)
    multi_thread_time = System.monotonic_time(:millisecond) - start_time
    total_multi_events = multi_results |> Enum.map(fn {_id, events} -> length(events) end) |> Enum.sum()
    
    IO.puts("📊 Multi-thread: #{total_multi_events} events in #{multi_thread_time}ms")
    IO.puts("📊 Rate: #{total_multi_events * 1000 / multi_thread_time |> Float.round(1)} events/sec")
    
    # Step 3: Batch storage test
    IO.puts("📊 Phase 3: Batch storage performance")
    start_time = System.monotonic_time(:millisecond)
    
    batch_events = for i <- 1..500 do
      event_type = Enum.random(event_types)
      {:ok, event} = Foundation.Events.new_event(event_type, %{
        batch_id: "batch_test",
        sequence: i,
        timestamp: System.os_time(:microsecond)
      })
      event
    end
    
    {:ok, batch_event_ids} = Foundation.Events.store_batch(batch_events)
    batch_time = System.monotonic_time(:millisecond) - start_time
    
    IO.puts("📊 Batch: #{length(batch_event_ids)} events in #{batch_time}ms")
    IO.puts("📊 Rate: #{length(batch_event_ids) * 1000 / batch_time |> Float.round(1)} events/sec")
    
    # Step 4: Query performance
    IO.puts("📊 Phase 4: Query performance analysis")
    
    query_start = System.monotonic_time(:millisecond)
    {:ok, login_events} = Foundation.Events.query(%{event_type: :user_login, limit: 100})
    query_time_1 = System.monotonic_time(:millisecond) - query_start
    
    query_start = System.monotonic_time(:millisecond)
    {:ok, recent_events} = Foundation.Events.query(%{
      created_after: System.os_time(:second) - 300,  # Last 5 minutes
      limit: 200
    })
    query_time_2 = System.monotonic_time(:millisecond) - query_start
    
    IO.puts("📊 Query 1 (by type): #{length(login_events)} events in #{query_time_1}ms")
    IO.puts("📊 Query 2 (by time): #{length(recent_events)} events in #{query_time_2}ms")
    
    # Results summary
    IO.puts("✅ F3 Completed - Event System Performance Baseline Established")
    
    performance_report = %{
      single_thread_rate: 1000 * 1000 / single_thread_time,
      multi_thread_rate: total_multi_events * 1000 / multi_thread_time,
      batch_rate: length(batch_event_ids) * 1000 / batch_time,
      query_1_time: query_time_1,
      query_2_time: query_time_2
    }
    
    IO.puts("📊 Performance Report: #{inspect(performance_report, pretty: true)}")
    
    # Baseline assertions (adjust based on your hardware)
    assert performance_report.single_thread_rate > 100  # At least 100 events/sec single-threaded
    assert performance_report.multi_thread_rate > performance_report.single_thread_rate  # Multi-threading helps
    assert performance_report.batch_rate > performance_report.single_thread_rate  # Batching helps
    assert query_time_1 < 1000  # Queries under 1 second
    assert query_time_2 < 1000
    
    :ok
  end
end

FoundationF3Test.run_scenario()

# Start 3-node cluster
./test_cluster.sh

# Node 1: API Gateway Service
# Node 2: User Service  
# Node 3: Order Service

# Run on Node 1 (API Gateway)
defmodule IntegrationI1Test do
  def run_scenario do
    IO.puts("🟡 Integration Scenario I1: Microservices Communication")
    
    # Verify cluster connectivity
    nodes = Node.list()
    IO.puts("📡 Connected nodes: #{inspect(nodes)}")
    assert length(nodes) >= 2, "Need at least 2 other nodes for this test"
    
    [user_service_node, order_service_node | _] = nodes
    
    # Step 1: Register services across nodes
    :rpc.call(user_service_node, Foundation.ServiceRegistry, :register, [
      :production, :user_service, self()
    ])
    
    :rpc.call(order_service_node, Foundation.ServiceRegistry, :register, [
      :production, :order_service, self()
    ])
    
    # Step 2: Simulate API requests flowing through microservices
    correlation_id = Foundation.Utils.generate_correlation_id()
    
    # API Gateway receives request
    Foundation.Telemetry.emit_counter([:api_gateway, :requests], %{
      endpoint: "/api/users/123/orders",
      method: "GET"
    })
    
    {:ok, api_event} = Foundation.Events.new_event(:api_request, %{
      user_id: "user_123",
      endpoint: "/api/users/123/orders",
      source_ip: "192.168.1.100"
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(api_event)
    
    # Step 3: Cross-service communication with context propagation
    user_lookup_task = Task.async(fn ->
      # Simulate call to user service
      :rpc.call(user_service_node, __MODULE__, :handle_user_lookup, ["user_123", correlation_id])
    end)
    
    order_lookup_task = Task.async(fn ->
      # Simulate call to order service  
      :rpc.call(order_service_node, __MODULE__, :handle_order_lookup, ["user_123", correlation_id])
    end)
    
    # Wait for services to respond
    user_result = Task.await(user_lookup_task, 5000)
    order_result = Task.await(order_lookup_task, 5000)
    
    # Step 4: Aggregate response
    {:ok, response_event} = Foundation.Events.new_event(:api_response, %{
      user_id: "user_123",
      user_data: user_result,
      orders: order_result,
      response_time_ms: 150
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(response_event)
    
    Foundation.Telemetry.emit_counter([:api_gateway, :responses], %{
      status: 200,
      response_time_bucket: "100-200ms"
    })
    
    # Step 5: Verify cross-node event correlation
    :timer.sleep(1000)  # Allow events to propagate
    
    # Query events by correlation ID across all nodes
    {:ok, correlated_events} = Foundation.Events.query(%{
      correlation_id: correlation_id
    })
    
    IO.puts("✅ I1 Completed")
    IO.puts("📊 Correlated events across nodes: #{length(correlated_events)}")
    
    # Verify events were created on multiple nodes
    assert length(correlated_events) >= 3  # At least API request, user lookup, order lookup
    
    :ok
  end
  
  # Mock service handlers (would run on respective nodes)
  def handle_user_lookup(user_id, correlation_id) do
    Foundation.Telemetry.emit_counter([:user_service, :lookups], %{user_id: user_id})
    
    {:ok, user_event} = Foundation.Events.new_event(:user_lookup, %{
      user_id: user_id,
      node: Node.self(),
      result: "user_found"
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(user_event)
    
    # Simulate database lookup
    :timer.sleep(50)
    
    %{
      user_id: user_id,
      name: "John Doe", 
      email: "[email protected]",
      node: Node.self()
    }
  end
  
  def handle_order_lookup(user_id, correlation_id) do
    Foundation.Telemetry.emit_counter([:order_service, :lookups], %{user_id: user_id})
    
    {:ok, order_event} = Foundation.Events.new_event(:order_lookup, %{
      user_id: user_id,
      node: Node.self(),
      orders_found: 3
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(order_event)
    
    # Simulate database lookup
    :timer.sleep(75)
    
    [
      %{order_id: "order_1", total: 29.99, status: "shipped"},
      %{order_id: "order_2", total: 15.50, status: "processing"},
      %{order_id: "order_3", total: 89.99, status: "delivered"}
    ]
  end
end

IntegrationI1Test.run_scenario()

defmodule IntegrationI2Test do
  def run_scenario do
    IO.puts("🟡 Integration Scenario I2: Event-Driven Architecture")
    
    nodes = Node.list()
    assert length(nodes) >= 2
    
    [write_node, read_node | _] = nodes
    
    # Step 1: Setup event sourcing pattern
    # Write side: Command handlers and event store
    # Read side: Event processors and read models
    
    aggregate_id = Foundation.Utils.generate_correlation_id()
    
    # Step 2: Process business commands (e-commerce order flow)
    commands = [
      {:create_order, %{user_id: "user_456", items: [%{sku: "LAPTOP_001", qty: 1, price: 999.99}]}},
      {:add_item, %{sku: "MOUSE_001", qty: 1, price: 29.99}},
      {:apply_discount, %{code: "SAVE10", amount: 102.99}},
      {:process_payment, %{payment_method: "credit_card", amount: 926.99}},
      {:confirm_order, %{estimated_delivery: "2024-12-15"}}
    ]
    
    events_created = []
    
    # Execute commands and generate events
    {events_created, _} = Enum.map_reduce(commands, 1, fn {command, data}, sequence ->
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      # Write side: Store command event
      {:ok, command_event} = Foundation.Events.new_event(:command_received, %{
        aggregate_id: aggregate_id,
        command_type: command,
        command_data: data,
        sequence: sequence
      }, correlation_id: correlation_id)
      
      :rpc.call(write_node, Foundation.Events, :store, [command_event])
      
      # Generate corresponding domain event
      domain_event_type = case command do
        :create_order -> :order_created
        :add_item -> :item_added
        :apply_discount -> :discount_applied
        :process_payment -> :payment_processed
        :confirm_order -> :order_confirmed
      end
      
      {:ok, domain_event} = Foundation.Events.new_event(domain_event_type, Map.merge(data, %{
        aggregate_id: aggregate_id,
        sequence: sequence,
        timestamp: System.os_time(:microsecond)
      }), correlation_id: correlation_id)
      
      :rpc.call(write_node, Foundation.Events, :store, [domain_event])
      
      # Emit telemetry for command processing
      Foundation.Telemetry.emit_counter([:cqrs, :commands_processed], %{
        command_type: command,
        aggregate_type: :order
      })
      
      :timer.sleep(100)  # Simulate processing time
      
      {[command_event, domain_event], sequence + 1}
    end)
    
    # Step 3: Read side processing (projections)
    # Simulate read model building from events
    read_task = Task.async(fn ->
      :rpc.call(read_node, __MODULE__, :build_read_models, [aggregate_id])
    end)
    
    # Step 4: Query read models
    :timer.sleep(2000)  # Allow read models to build
    
    read_models = Task.await(read_task, 10_000)
    
    # Step 5: Event replay simulation (rebuilding read models)
    IO.puts("📊 Starting event replay simulation")
    
    replay_start = System.monotonic_time(:millisecond)
    
    {:ok, aggregate_events} = Foundation.Events.query(%{
      metadata: %{aggregate_id: aggregate_id},
      order_by: :sequence
    })
    
    # Rebuild state from events
    final_state = Enum.reduce(aggregate_events, %{}, fn event, state ->
      case event.event_type do
        :order_created -> 
          Map.merge(state, %{
            status: :created,
            user_id: event.data["user_id"],
            items: event.data["items"],
            total: calculate_total(event.data["items"])
          })
        
        :item_added ->
          items = Map.get(state, :items, [])
          new_item = %{
            sku: event.data["sku"],
            qty: event.data["qty"], 
            price: event.data["price"]
          }
          Map.put(state, :items, [new_item | items])
        
        :discount_applied ->
          Map.put(state, :discount, event.data["amount"])
        
        :payment_processed ->
          Map.merge(state, %{
            status: :paid,
            payment_method: event.data["payment_method"]
          })
        
        :order_confirmed ->
          Map.merge(state, %{
            status: :confirmed,
            estimated_delivery: event.data["estimated_delivery"]
          })
        
        _ -> state
      end
    end)
    
    replay_time = System.monotonic_time(:millisecond) - replay_start
    
    IO.puts("✅ I2 Completed")
    IO.puts("📊 Events in aggregate: #{length(aggregate_events)}")
    IO.puts("📊 Read models built: #{map_size(read_models)}")
    IO.puts("📊 Event replay time: #{replay_time}ms")
    IO.puts("📊 Final aggregate state: #{inspect(final_state, pretty: true)}")
    
    # Assertions
    assert length(aggregate_events) >= 10  # Command + domain events
    assert final_state[:status] == :confirmed
    assert is_list(final_state[:items])
    assert length(final_state[:items]) >= 2  # Original + added item
    
    :ok
  end
  
  def build_read_models(aggregate_id) do
    # Simulate read model projections
    :timer.sleep(1000)
    
    {:ok, events} = Foundation.Events.query(%{
      metadata: %{aggregate_id: aggregate_id}
    })
    
    # Build different read models
    read_models = %{
      order_summary: build_order_summary(events),
      payment_details: build_payment_details(events),
      audit_trail: build_audit_trail(events)
    }
    
    Foundation.Telemetry.emit_counter([:cqrs, :read_models_built], %{
      aggregate_id: aggregate_id,
      models_count: map_size(read_models)
    })
    
    read_models
  end
  
  defp build_order_summary(events) do
    # Mock read model building
    %{
      total_events: length(events),
      event_types: events |> Enum.map(& &1.event_type) |> Enum.uniq(),
      built_at: System.os_time(:second)
    }
  end
  
  defp build_payment_details(events) do
    payment_events = Enum.filter(events, fn event ->
      event.event_type in [:payment_processed, :discount_applied]
    end)
    
    %{
      payment_events_count: length(payment_events),
      built_at: System.os_time(:second)
    }
  end
  
  defp build_audit_trail(events) do
    %{
      event_sequence: Enum.map(events, fn event ->
        %{
          type: event.event_type,
          timestamp: event.created_at,
          correlation_id: event.correlation_id
        }
      end),
      built_at: System.os_time(:second)
    }
  end
  
  defp calculate_total(items) when is_list(items) do
    Enum.reduce(items, 0, fn item, acc ->
      price = item["price"] || 0
      qty = item["qty"] || 1
      acc + (price * qty)
    end)
  end
  
  defp calculate_total(_), do: 0
end

IntegrationI2Test.run_scenario()

defmodule ProductionP1Test do
  def run_scenario do
    IO.puts("🔴 Production Scenario P1: High-Traffic SaaS Application")
    
    nodes = Node.list()
    assert length(nodes) >= 4, "Need at least 4 nodes for production simulation"
    
    # Node allocation:
    # Node 1: Load balancer + API Gateway
    # Node 2-3: Application servers
    # Node 4: Background job processing
    # Node 5: Analytics/reporting
    
    [api_node1, api_node2, worker_node, analytics_node | _] = nodes
    
    # Step 1: Simulate realistic user traffic pattern
    # Peak hours: 1000 concurrent users
    # Off-peak: 200 concurrent users
    # Burst traffic: 2000 concurrent users for short periods
    
    IO.puts("📊 Phase 1: Steady state traffic (200 concurrent users)")
    
    steady_state_task = Task.async(fn ->
      simulate_user_traffic(200, 300_000, [api_node1, api_node2])  # 5 minutes
    end)
    
    # Step 2: Background job processing
    background_jobs_task = Task.async(fn ->
      :rpc.call(worker_node, __MODULE__, :process_background_jobs, [300_000])
    end)
    
    # Step 3: Real-time analytics processing
    analytics_task = Task.async(fn ->
      :rpc.call(analytics_node, __MODULE__, :process_analytics, [300_000])
    end)
    
    # Let steady state run
    :timer.sleep(30_000)  # 30 seconds
    
    # Step 4: Traffic burst simulation
    IO.puts("📊 Phase 2: Traffic burst (2000 concurrent users)")
    
    burst_task = Task.async(fn ->
      simulate_user_traffic(2000, 60_000, [api_node1, api_node2])  # 1 minute burst
    end)
    
    # Step 5: Gradual scale down
    IO.puts("📊 Phase 3: Scale down to peak hours (1000 users)")
    
    :timer.sleep(60_000)  # Wait for burst to complete
    
    peak_task = Task.async(fn ->
      simulate_user_traffic(1000, 180_000, [api_node1, api_node2])  # 3 minutes
    end)
    
    # Wait for all phases to complete
    steady_results = Task.await(steady_state_task, 350_000)
    burst_results = Task.await(burst_task, 70_000)
    peak_results = Task.await(peak_task, 190_000)
    background_results = Task.await(background_jobs_task, 350_000)
    analytics_results = Task.await(analytics_task, 350_000)
    
    # Step 6: Performance analysis
    total_requests = steady_results.requests + burst_results.requests + peak_results.requests
    total_errors = steady_results.errors + burst_results.errors + peak_results.errors
    error_rate = total_errors / total_requests * 100
    
    IO.puts("✅ P1 Completed - High-Traffic SaaS Simulation")
    IO.puts("📊 Total requests: #{total_requests}")
    IO.puts("📊 Total errors: #{total_errors} (#{Float.round(error_rate, 2)}%)")
    IO.puts("📊 Background jobs processed: #{background_results.jobs_processed}")
    IO.puts("📊 Analytics events processed: #{analytics_results.events_processed}")
    
    # Production-grade assertions
    assert error_rate < 0.1, "Error rate should be under 0.1% in production"
    assert total_requests > 50_000, "Should handle significant request volume"
    assert background_results.jobs_processed > 100, "Background processing should be active"
    
    :ok
  end
  
  defp simulate_user_traffic(concurrent_users, duration_ms, api_nodes) do
    IO.puts("🚀 Starting #{concurrent_users} concurrent users for #{duration_ms}ms")
    
    end_time = System.monotonic_time(:millisecond) + duration_ms
    
    # Spawn user simulation processes
    user_tasks = for user_id <- 1..concurrent_users do
      Task.async(fn ->
        simulate_user_session(user_id, end_time, api_nodes)
      end)
    end
    
    # Collect results
    results = Task.await_many(user_tasks, duration_ms + 10_000)
    
    total_requests = Enum.sum(Enum.map(results, & &1.requests))
    total_errors = Enum.sum(Enum.map(results, & &1.errors))
    
    %{requests: total_requests, errors: total_errors}
  end
  
  defp simulate_user_session(user_id, end_time, api_nodes) do
    requests = 0
    errors = 0
    
    simulate_user_loop(user_id, end_time, api_nodes, requests, errors)
  end
  
  defp simulate_user_loop(user_id, end_time, api_nodes, requests, errors) do
    if System.monotonic_time(:millisecond) < end_time do
      # Pick random API node (load balancing)
      api_node = Enum.random(api_nodes)
      
      # Simulate different user actions
      action = Enum.random([
        :login, :dashboard_view, :profile_update, :data_query,
        :report_generation, :settings_change, :logout
      ])
      
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      result = try do
        :rpc.call(api_node, __MODULE__, :handle_user_action, [action, user_id, correlation_id])
      rescue
        _error -> {:error, :api_error}
      catch
        :exit, _reason -> {:error, :node_down}
      end
      
      {new_requests, new_errors} = case result do
        {:ok, _} -> {requests + 1, errors}
        {:error, _} -> {requests + 1, errors + 1}
      end
      
      # Realistic user think time
      :timer.sleep(:rand.uniform(100) + 50)  # 50-150ms between actions
      
      simulate_user_loop(user_id, end_time, api_nodes, new_requests, new_errors)
    else
      %{requests: requests, errors: errors}
    end
  end
  
  def handle_user_action(action, user_id, correlation_id) do
    # Simulate API processing time
    processing_time = case action do
      :login -> :rand.uniform(100) + 50
      :dashboard_view -> :rand.uniform(200) + 100
      :profile_update -> :rand.uniform(300) + 200
      :data_query -> :rand.uniform(500) + 300
      :report_generation -> :rand.uniform(1000) + 500
      :settings_change -> :rand.uniform(150) + 100
      :logout -> :rand.uniform(50) + 25
    end
    
    :timer.sleep(processing_time)
    
    # Log the action
    {:ok, event} = Foundation.Events.new_event(:user_action, %{
      user_id: "user_#{user_id}",
      action: action,
      processing_time_ms: processing_time,
      node: Node.self()
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(event)
    
    # Emit telemetry
    Foundation.Telemetry.emit_counter([:api, :requests], %{
      action: action,
      response_time_bucket: response_time_bucket(processing_time)
    })
    
    # Simulate occasional errors (99.9% success rate)
    if :rand.uniform(1000) == 1 do
      {:error, :random_failure}
    else
      {:ok, %{action: action, user_id: user_id, processing_time: processing_time}}
    end
  end
  
  def process_background_jobs(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    jobs_processed = 0
    
    process_jobs_loop(end_time, jobs_processed)
  end
  
  defp process_jobs_loop(end_time, jobs_processed) do
    if System.monotonic_time(:millisecond) < end_time do
      # Simulate different background jobs
      job_type = Enum.random([
        :email_delivery, :report_generation, :data_cleanup,
        :analytics_aggregation, :backup_creation
      ])
      
      processing_time = case job_type do
        :email_delivery -> :rand.uniform(1000) + 500
        :report_generation -> :rand.uniform(5000) + 2000
        :data_cleanup -> :rand.uniform(3000) + 1000
        :analytics_aggregation -> :rand.uniform(2000) + 1000
        :backup_creation -> :rand.uniform(10000) + 5000
      end
      
      :timer.sleep(processing_time)
      
      # Log job completion
      {:ok, event} = Foundation.Events.new_event(:background_job_completed, %{
        job_type: job_type,
        processing_time_ms: processing_time,
        node: Node.self()
      })
      
      Foundation.Events.store(event)
      
      Foundation.Telemetry.emit_counter([:background_jobs, :completed], %{
        job_type: job_type
      })
      
      process_jobs_loop(end_time, jobs_processed + 1)
    else
      %{jobs_processed: jobs_processed}
    end
  end
  
  def process_analytics(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    events_processed = 0
    
    process_analytics_loop(end_time, events_processed)
  end
  
  defp process_analytics_loop(end_time, events_processed) do
    if System.monotonic_time(:millisecond) < end_time do
      # Query recent events for analytics
      {:ok, recent_events} = Foundation.Events.query(%{
        created_after: System.os_time(:second) - 60,  # Last minute
        limit: 100
      })
      
      # Process events in batches
      batch_size = min(length(recent_events), 50)
      batch_events = Enum.take(recent_events, batch_size)
      
      # Simulate analytics processing
      :timer.sleep(100 * batch_size)  # 100ms per event
      
      if batch_size > 0 do
        Foundation.Telemetry.emit_counter([:analytics, :events_processed], %{
          batch_size: batch_size
        })
      end
      
      :timer.sleep(5000)  # Process every 5 seconds
      
      process_analytics_loop(end_time, events_processed + batch_size)
    else
      %{events_processed: events_processed}
    end
  end
  
  defp response_time_bucket(time_ms) when time_ms < 100, do: "fast"
  defp response_time_bucket(time_ms) when time_ms < 500, do: "medium"
  defp response_time_bucket(time_ms) when time_ms < 1000, do: "slow"
  defp response_time_bucket(_), do: "very_slow"
end

ProductionP1Test.run_scenario()

defmodule ProductionP2Test do
  def run_scenario do
    IO.puts("🔴 Production Scenario P2: Financial Services Real-Time Processing")
    
    nodes = Node.list()
    assert length(nodes) >= 6, "Need at least 6 nodes for financial services simulation"
    
    # Node allocation:
    # Node 1-2: Trading engines (ultra-low latency)
    # Node 3-4: Risk management systems
    # Node 5: Settlement processing
    # Node 6: Compliance and audit
    # Node 7: Market data processing
    
    [trading_node1, trading_node2, risk_node1, risk_node2, settlement_node, compliance_node | _] = nodes
    
    IO.puts("📊 Starting financial services simulation")
    IO.puts("📊 Trading nodes: #{inspect([trading_node1, trading_node2])}")
    IO.puts("📊 Risk nodes: #{inspect([risk_node1, risk_node2])}")
    
    # Step 1: Market data simulation (high-frequency updates)
    market_data_task = Task.async(fn ->
      simulate_market_data_feed(600_000)  # 10 minutes of market data
    end)
    
    # Step 2: High-frequency trading simulation
    trading_tasks = [
      Task.async(fn -> 
        :rpc.call(trading_node1, __MODULE__, :run_trading_engine, [:algorithmic_trading, 600_000])
      end),
      Task.async(fn ->
        :rpc.call(trading_node2, __MODULE__, :run_trading_engine, [:market_making, 600_000])
      end)
    ]
    
    # Step 3: Risk management (real-time monitoring)
    risk_tasks = [
      Task.async(fn ->
        :rpc.call(risk_node1, __MODULE__, :run_risk_monitoring, [:portfolio_risk, 600_000])
      end),
      Task.async(fn ->
        :rpc.call(risk_node2, __MODULE__, :run_risk_monitoring, [:counterparty_risk, 600_000])
      end)
    ]
    
    # Step 4: Settlement processing
    settlement_task = Task.async(fn ->
      :rpc.call(settlement_node, __MODULE__, :run_settlement_processing, [600_000])
    end)
    
    # Step 5: Compliance monitoring
    compliance_task = Task.async(fn ->
      :rpc.call(compliance_node, __MODULE__, :run_compliance_monitoring, [600_000])
    end)
    
    # Let simulation run for a while
    :timer.sleep(120_000)  # 2 minutes
    
    # Step 6: Stress test - market volatility spike
    IO.puts("📊 Injecting market volatility spike")
    
    volatility_task = Task.async(fn ->
      simulate_volatility_spike(60_000)  # 1 minute of high volatility
    end)
    
    # Continue running
    :timer.sleep(180_000)  # 3 more minutes
    
    # Step 7: Regulatory reporting simulation
    IO.puts("📊 Starting end-of-day regulatory reporting")
    
    reporting_task = Task.async(fn ->
      generate_regulatory_reports()
    end)
    
    # Wait for all components to complete
    market_results = Task.await(market_data_task, 650_000)
    trading_results = Task.await_many(trading_tasks, 650_000)
    risk_results = Task.await_many(risk_tasks, 650_000)
    settlement_results = Task.await(settlement_task, 650_000)
    compliance_results = Task.await(compliance_task, 650_000)
    volatility_results = Task.await(volatility_task, 70_000)
    reporting_results = Task.await(reporting_task, 120_000)
    
    # Step 8: Performance analysis
    total_trades = Enum.sum(Enum.map(trading_results, & &1.trades_executed))
    total_risk_checks = Enum.sum(Enum.map(risk_results, & &1.risk_checks))
    
    avg_trade_latency = trading_results
                       |> Enum.flat_map(& &1.latencies)
                       |> Enum.sum()
                       |> div(length(Enum.flat_map(trading_results, & &1.latencies)))
    
    IO.puts("✅ P2 Completed - Financial Services Simulation")
    IO.puts("📊 Market data points processed: #{market_results.data_points}")
    IO.puts("📊 Total trades executed: #{total_trades}")
    IO.puts("📊 Risk checks performed: #{total_risk_checks}")
    IO.puts("📊 Average trade latency: #{avg_trade_latency}μs")
    IO.puts("📊 Settlements processed: #{settlement_results.settlements}")
    IO.puts("📊 Compliance alerts: #{compliance_results.alerts}")
    IO.puts("📊 Volatility events: #{volatility_results.events}")
    IO.puts("📊 Regulatory reports: #{reporting_results.reports}")
    
    # Financial services assertions (strict requirements)
    assert avg_trade_latency < 1000, "Trade latency must be under 1ms (#{avg_trade_latency}μs)"
    assert total_trades > 1000, "Should execute significant number of trades"
    assert compliance_results.alerts >= 0, "Compliance monitoring should be active"
    assert reporting_results.reports > 0, "Should generate regulatory reports"
    
    :ok
  end
  
  defp simulate_market_data_feed(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    data_points = 0
    
    # Simulate high-frequency market data (1000 updates per second)
    simulate_market_loop(end_time, data_points)
  end
  
  defp simulate_market_loop(end_time, data_points) do
    if System.monotonic_time(:millisecond) < end_time do
      # Generate market data update
      instruments = [:AAPL, :GOOGL, :MSFT, :TSLA, :AMZN, :META, :NVDA]
      instrument = Enum.random(instruments)
      
      price_change = (:rand.uniform() - 0.5) * 2.0  # -1.0 to +1.0
      volume = :rand.uniform(10000) + 1000
      
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, market_event} = Foundation.Events.new_event(:market_data_update, %{
        instrument: instrument,
        price_change: price_change,
        volume: volume,
        timestamp: System.os_time(:microsecond)
      }, correlation_id: correlation_id)
      
      Foundation.Events.store(market_event)
      
      Foundation.Telemetry.emit_counter([:market_data, :updates], %{
        instrument: instrument
      })
      
      # High frequency - 1ms between updates
      :timer.sleep(1)
      
      simulate_market_loop(end_time, data_points + 1)
    else
      %{data_points: data_points}
    end
  end
  
  def run_trading_engine(strategy, duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    trades_executed = 0
    latencies = []
    
    trading_loop(strategy, end_time, trades_executed, latencies)
  end
  
  defp trading_loop(strategy, end_time, trades, latencies) do
    if System.monotonic_time(:millisecond) < end_time do
      # Query recent market data
      start_time = System.monotonic_time(:microsecond)
      
      {:ok, market_events} = Foundation.Events.query(%{
        event_type: :market_data_update,
        created_after: System.os_time(:second) - 1,  # Last second
        limit: 10
      })
      
      # Execute trading decision (ultra-low latency)
      if length(market_events) > 0 and should_trade?(strategy, market_events) do
        trade_decision_time = System.monotonic_time(:microsecond)
        
        # Execute trade
        instrument = Enum.random([:AAPL, :GOOGL, :MSFT])
        quantity = :rand.uniform(1000) + 100
        side = if :rand.uniform() > 0.5, do: :buy, else: :sell
        
        correlation_id = Foundation.Utils.generate_correlation_id()
        
        {:ok, trade_event} = Foundation.Events.new_event(:trade_executed, %{
          strategy: strategy,
          instrument: instrument,
          quantity: quantity,
          side: side,
          execution_time: System.os_time(:microsecond),
          node: Node.self()
        }, correlation_id: correlation_id)
        
        Foundation.Events.store(trade_event)
        
        execution_latency = System.monotonic_time(:microsecond) - start_time
        
        Foundation.Telemetry.emit_counter([:trading, :executions], %{
          strategy: strategy,
          instrument: instrument,
          side: side
        })
        
        Foundation.Telemetry.emit_gauge([:trading, :latency_microseconds], execution_latency, %{
          strategy: strategy
        })
        
        trading_loop(strategy, end_time, trades + 1, [execution_latency | latencies])
      else
        # No trade, but still monitor
        :timer.sleep(5)  # 5ms monitoring interval
        trading_loop(strategy, end_time, trades, latencies)
      end
    else
      %{trades_executed: trades, latencies: latencies}
    end
  end
  
  defp should_trade?(strategy, market_events) do
    case strategy do
      :algorithmic_trading ->
        # Simple momentum strategy
        price_changes = Enum.map(market_events, fn event -> event.data["price_change"] end)
        avg_change = Enum.sum(price_changes) / length(price_changes)
        abs(avg_change) > 0.5
      
      :market_making ->
        # Market making strategy (provide liquidity)
        :rand.uniform() > 0.7  # 30% trade probability
    end
  end
  
  def run_risk_monitoring(risk_type, duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    risk_checks = 0
    
    risk_monitoring_loop(risk_type, end_time, risk_checks)
  end
  
  defp risk_monitoring_loop(risk_type, end_time, checks) do
    if System.monotonic_time(:millisecond) < end_time do
      # Query recent trades for risk analysis
      {:ok, trade_events} = Foundation.Events.query(%{
        event_type: :trade_executed,
        created_after: System.os_time(:second) - 10,  # Last 10 seconds
        limit: 50
      })
      
      # Perform risk calculation
      risk_score = calculate_risk_score(risk_type, trade_events)
      
      if risk_score > 0.8 do  # High risk threshold
        correlation_id = Foundation.Utils.generate_correlation_id()
        
        {:ok, risk_event} = Foundation.Events.new_event(:risk_alert, %{
          risk_type: risk_type,
          risk_score: risk_score,
          trades_analyzed: length(trade_events),
          alert_level: :high
        }, correlation_id: correlation_id)
        
        Foundation.Events.store(risk_event)
        
        Foundation.Telemetry.emit_counter([:risk, :alerts], %{
          risk_type: risk_type,
          alert_level: :high
        })
      end
      
      Foundation.Telemetry.emit_gauge([:risk, :score], risk_score, %{
        risk_type: risk_type
      })
      
      # Risk monitoring every 100ms
      :timer.sleep(100)
      
      risk_monitoring_loop(risk_type, end_time, checks + 1)
    else
      %{risk_checks: checks}
    end
  end
  
  defp calculate_risk_score(risk_type, trade_events) do
    case risk_type do
      :portfolio_risk ->
        # Calculate portfolio concentration risk
        if length(trade_events) == 0 do
          0.0
        else
          instruments = Enum.map(trade_events, fn event -> event.data["instrument"] end)
          unique_instruments = Enum.uniq(instruments)
          concentration = length(instruments) / length(unique_instruments)
          min(concentration / 10.0, 1.0)  # Normalize to 0-1
        end
      
      :counterparty_risk ->
        # Simple counterparty risk (random for simulation)
        :rand.uniform()
    end
  end
  
  def run_settlement_processing(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    settlements = 0
    
    settlement_loop(end_time, settlements)
  end
  
  defp settlement_loop(end_time, settlements) do
    if System.monotonic_time(:millisecond) < end_time do
      # Query trades ready for settlement
      {:ok, trade_events} = Foundation.Events.query(%{
        event_type: :trade_executed,
        created_after: System.os_time(:second) - 30,  # Last 30 seconds
        limit: 20
      })
      
      # Process settlements in batches
      if length(trade_events) > 0 do
        correlation_id = Foundation.Utils.generate_correlation_id()
        
        {:ok, settlement_event} = Foundation.Events.new_event(:settlement_batch, %{
          trades_count: length(trade_events),
          settlement_time: System.os_time(:microsecond),
          batch_id: correlation_id
        }, correlation_id: correlation_id)
        
        Foundation.Events.store(settlement_event)
        
        Foundation.Telemetry.emit_counter([:settlement, :batches], %{
          trades_count: length(trade_events)
        })
        
        settlement_loop(end_time, settlements + 1)
      else
        :timer.sleep(1000)  # Wait for more trades
        settlement_loop(end_time, settlements)
      end
    else
      %{settlements: settlements}
    end
  end
  
  def run_compliance_monitoring(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    alerts = 0
    
    compliance_loop(end_time, alerts)
  end
  
  defp compliance_loop(end_time, alerts) do
    if System.monotonic_time(:millisecond) < end_time do
      # Monitor for compliance violations
      {:ok, trade_events} = Foundation.Events.query(%{
        event_type: :trade_executed,
        created_after: System.os_time(:second) - 60,  # Last minute
        limit: 100
      })
      
      # Check for suspicious patterns
      violations = detect_compliance_violations(trade_events)
      
      if length(violations) > 0 do
        for violation <- violations do
          correlation_id = Foundation.Utils.generate_correlation_id()
          
          {:ok, compliance_event} = Foundation.Events.new_event(:compliance_violation, violation, 
            correlation_id: correlation_id)
          
          Foundation.Events.store(compliance_event)
          
          Foundation.Telemetry.emit_counter([:compliance, :violations], %{
            violation_type: violation.violation_type
          })
        end
      end
      
      :timer.sleep(5000)  # Check every 5 seconds
      
      compliance_loop(end_time, alerts + length(violations))
    else
      %{alerts: alerts}
    end
  end
  
  defp detect_compliance_violations(trade_events) do
    # Simple compliance checks
    violations = []
    
    # Check for unusually large trades
    large_trades = Enum.filter(trade_events, fn event ->
      event.data["quantity"] > 5000
    end)
    
    if length(large_trades) > 0 do
      [%{
        violation_type: :large_trade,
        trade_count: length(large_trades),
        detected_at: System.os_time(:second)
      } | violations]
    else
      violations
    end
  end
  
  defp simulate_volatility_spike(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    events = 0
    
    volatility_loop(end_time, events)
  end
  
  defp volatility_loop(end_time, events) do
    if System.monotonic_time(:millisecond) < end_time do
      # Generate high volatility event
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, volatility_event} = Foundation.Events.new_event(:market_volatility, %{
        volatility_level: :extreme,
        vix_equivalent: 50.0 + :rand.uniform() * 30.0,  # 50-80 VIX
        trigger: :news_event
      }, correlation_id: correlation_id)
      
      Foundation.Events.store(volatility_event)
      
      Foundation.Telemetry.emit_counter([:market, :volatility_events], %{
        level: :extreme
      })
      
      # High frequency volatility events
      :timer.sleep(100)
      
      volatility_loop(end_time, events + 1)
    else
      %{events: events}
    end
  end
  
  defp generate_regulatory_reports do
    # Generate end-of-day regulatory reports
    {:ok, all_trades} = Foundation.Events.query(%{
      event_type: :trade_executed,
      created_after: System.os_time(:second) - 86400,  # Last 24 hours
      limit: 10000
    })
    
    {:ok, risk_alerts} = Foundation.Events.query(%{
      event_type: :risk_alert,
      created_after: System.os_time(:second) - 86400,
      limit: 1000
    })
    
    {:ok, compliance_violations} = Foundation.Events.query(%{
      event_type: :compliance_violation,
      created_after: System.os_time(:second) - 86400,
      limit: 1000
    })
    
    reports = [
      %{report_type: :trade_summary, trade_count: length(all_trades)},
      %{report_type: :risk_summary, alert_count: length(risk_alerts)},
      %{report_type: :compliance_summary, violation_count: length(compliance_violations)}
    ]
    
    for report <- reports do
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, report_event} = Foundation.Events.new_event(:regulatory_report, report,
        correlation_id: correlation_id)
      
      Foundation.Events.store(report_event)
    end
    
    Foundation.Telemetry.emit_counter([:regulatory, :reports_generated], %{
      report_count: length(reports)
    })
    
    %{reports: length(reports)}
  end
end

ProductionP2Test.run_scenario()

defmodule ExtremeE1Test do
  def run_scenario do
    IO.puts("⚫ Extreme Scenario E1: Global Multi-Cloud Edge Computing")
    
    # This scenario simulates a global CDN/edge computing platform
    # with Foundation coordinating across multiple cloud providers
    # and edge locations worldwide
    
    nodes = Node.list()
    assert length(nodes) >= 9, "Need at least 9 nodes for global edge simulation"
    
    # Simulate global deployment:
    # AWS us-east-1, us-west-2, eu-west-1
    # GCP us-central1, europe-west1, asia-southeast1  
    # Azure eastus, westeurope, southeastasia
    # Edge locations in major cities
    
    [aws_us_east, aws_us_west, aws_eu_west,
     gcp_us_central, gcp_eu_west, gcp_asia_se,
     azure_us_east, azure_eu_west, azure_asia_se] = nodes
    
    cloud_regions = %{
      aws: [aws_us_east, aws_us_west, aws_eu_west],
      gcp: [gcp_us_central, gcp_eu_west, gcp_asia_se],
      azure: [azure_us_east, azure_eu_west, azure_asia_se]
    }
    
    IO.puts("📊 Global edge deployment across #{length(nodes)} regions")
    
    # Step 1: Global service mesh initialization
    mesh_task = Task.async(fn ->
      initialize_global_service_mesh(cloud_regions)
    end)
    
    # Step 2: Simulate global user traffic with edge routing
    global_traffic_tasks = for {cloud, regions} <- cloud_regions do
      Task.async(fn ->
        simulate_edge_traffic(cloud, regions, 1_800_000)  # 30 minutes
      end)
    end
    
    # Step 3: Cross-cloud data replication
    replication_task = Task.async(fn ->
      run_global_data_replication(cloud_regions, 1_800_000)
    end)
    
    # Step 4: Edge computing workloads
    edge_computing_tasks = for region <- List.flatten(Map.values(cloud_regions)) do
      Task.async(fn ->
        :rpc.call(region, __MODULE__, :run_edge_computing, [1_800_000])
      end)
    end
    
    # Let the system run for 10 minutes
    :timer.sleep(600_000)
    
    # Step 5: Simulate cloud provider outage (chaos engineering)
    IO.puts("📊 Simulating cloud provider outage - AWS regions going down")
    
    outage_task = Task.async(fn ->
      simulate_cloud_outage(:aws, cloud_regions[:aws], 300_000)  # 5 minute outage
    end)
    
    # Step 6: Global failover and recovery
    failover_task = Task.async(fn ->
      coordinate_global_failover(:aws, cloud_regions, 300_000)
    end)
    
    # Continue running during outage
    :timer.sleep(300_000)  # 5 minutes
    
    # Step 7: Recovery phase
    IO.puts("📊 Starting recovery phase - AWS regions coming back online")
    
    recovery_task = Task.async(fn ->
      coordinate_global_recovery(:aws, cloud_regions[:aws], 300_000)
    end)
    
    # Run for another 15 minutes
    :timer.sleep(900_000)
    
    # Collect all results
    mesh_result = Task.await(mesh_task, 60_000)
    traffic_results = Task.await_many(global_traffic_tasks, 1_900_000)
    replication_result = Task.await(replication_task, 1_900_000)
    edge_results = Task.await_many(edge_computing_tasks, 1_900_000)
    outage_result = Task.await(outage_task, 350_000)
    failover_result = Task.await(failover_task, 350_000)
    recovery_result = Task.await(recovery_task, 350_000)
    
    # Step 8: Global performance analysis
    total_requests = Enum.sum(Enum.map(traffic_results, & &1.requests))
    total_edge_computations = Enum.sum(Enum.map(edge_results, & &1.computations))
    
    global_latencies = traffic_results
                      |> Enum.flat_map(& &1.latencies)
                      |> Enum.sort()
    
    p50_latency = Enum.at(global_latencies, div(length(global_latencies), 2))
    p95_latency = Enum.at(global_latencies, div(length(global_latencies) * 95, 100))
    p99_latency = Enum.at(global_latencies, div(length(global_latencies) * 99, 100))
    
    IO.puts("✅ E1 Completed - Global Multi-Cloud Edge Computing")
    IO.puts("📊 Global requests processed: #{total_requests}")
    IO.puts("📊 Edge computations: #{total_edge_computations}")
    IO.puts("📊 Data replications: #{replication_result.replications}")
    IO.puts("📊 Global latency P50: #{p50_latency}ms")
    IO.puts("📊 Global latency P95: #{p95_latency}ms")
    IO.puts("📊 Global latency P99: #{p99_latency}ms")
    IO.puts("📊 Outage duration: #{outage_result.outage_duration_ms}ms")
    IO.puts("📊 Failover time: #{failover_result.failover_time_ms}ms")
    IO.puts("📊 Recovery time: #{recovery_result.recovery_time_ms}ms")
    
    # Extreme scenario assertions (planetary scale requirements)
    assert total_requests > 100_000, "Should handle massive global traffic"
    assert p99_latency < 2000, "P99 latency should be under 2 seconds globally"
    assert failover_result.failover_time_ms < 30_000, "Failover should complete in under 30 seconds"
    assert recovery_result.recovery_time_ms < 60_000, "Recovery should complete in under 1 minute"
    
    :ok
  end
  
  defp initialize_global_service_mesh(cloud_regions) do
    IO.puts("🌐 Initializing global service mesh")
    
    # Register all regions in the global service mesh
    for {cloud, regions} <- cloud_regions do
      for region <- regions do
        :rpc.call(region, Foundation.ServiceRegistry, :register, [
          :global, :"#{cloud}_edge_service", self()
        ])
      end
    end
    
    # Set up cross-cloud connectivity matrix
    all_regions = List.flatten(Map.values(cloud_regions))
    
    connectivity_matrix = for region1 <- all_regions, region2 <- all_regions, region1 != region2 do
      latency = simulate_inter_region_latency(region1, region2)
      
      %{
        from: region1,
        to: region2,
        latency_ms: latency,
        cloud1: get_cloud_from_region(region1),
        cloud2: get_cloud_from_region(region2)
      }
    end
    
    %{
      regions_registered: length(all_regions),
      connectivity_matrix: connectivity_matrix
    }
  end
  
  defp simulate_edge_traffic(cloud, regions, duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    requests = 0
    latencies = []
    
    edge_traffic_loop(cloud, regions, end_time, requests, latencies)
  end
  
  defp edge_traffic_loop(cloud, regions, end_time, requests, latencies) do
    if System.monotonic_time(:millisecond) < end_time do
      # Pick nearest edge region for request
      edge_region = Enum.random(regions)
      
      # Simulate different types of edge requests
      request_type = Enum.random([
        :cdn_content, :api_call, :real_time_data,
        :iot_telemetry, :video_streaming, :gaming_session
      ])
      
      request_start = System.monotonic_time(:millisecond)
      
      # Route request to appropriate edge region
      result = :rpc.call(edge_region, __MODULE__, :handle_edge_request, [
        request_type, cloud, System.os_time(:microsecond)
      ])
      
      request_latency = System.monotonic_time(:millisecond) - request_start
      
      case result do
        {:ok, _response} ->
          edge_traffic_loop(cloud, regions, end_time, requests + 1, [request_latency | latencies])
        
        {:error, _reason} ->
          # Log failed request but continue
          edge_traffic_loop(cloud, regions, end_time, requests + 1, [request_latency | latencies])
      end
    else
      %{requests: requests, latencies: latencies}
    end
  end
  
  def handle_edge_request(request_type, cloud, timestamp) do
    # Simulate edge processing
    processing_time = case request_type do
      :cdn_content -> :rand.uniform(50) + 10       # 10-60ms
      :api_call -> :rand.uniform(100) + 50         # 50-150ms
      :real_time_data -> :rand.uniform(20) + 5     # 5-25ms
      :iot_telemetry -> :rand.uniform(30) + 10     # 10-40ms
      :video_streaming -> :rand.uniform(200) + 100 # 100-300ms
      :gaming_session -> :rand.uniform(50) + 20    # 20-70ms
    end
    
    :timer.sleep(processing_time)
    
    correlation_id = Foundation.Utils.generate_correlation_id()
    
    {:ok, edge_event} = Foundation.Events.new_event(:edge_request_processed, %{
      request_type: request_type,
      cloud: cloud,
      edge_region: Node.self(),
      processing_time_ms: processing_time,
      timestamp: timestamp
    }, correlation_id: correlation_id)
    
    Foundation.Events.store(edge_event)
    
    Foundation.Telemetry.emit_counter([:edge, :requests], %{
      request_type: request_type,
      cloud: cloud
    })
    
    Foundation.Telemetry.emit_gauge([:edge, :processing_time], processing_time, %{
      request_type: request_type
    })
    
    {:ok, %{
      processed_at: Node.self(),
      processing_time: processing_time,
      response_data: "edge_response_#{:rand.uniform(1000)}"
    }}
  end
  
  defp run_global_data_replication(cloud_regions, duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    replications = 0
    
    replication_loop(cloud_regions, end_time, replications)
  end
  
  defp replication_loop(cloud_regions, end_time, replications) do
    if System.monotonic_time(:millisecond) < end_time do
      # Pick source and target clouds for replication
      clouds = Map.keys(cloud_regions)
      source_cloud = Enum.random(clouds)
      target_clouds = clouds -- [source_cloud]
      
      if length(target_clouds) > 0 do
        target_cloud = Enum.random(target_clouds)
        
        source_region = Enum.random(cloud_regions[source_cloud])
        target_region = Enum.random(cloud_regions[target_cloud])
        
        # Simulate data replication
        replication_start = System.monotonic_time(:millisecond)
        
        # Fetch data from source
        {:ok, recent_events} = :rpc.call(source_region, Foundation.Events, :query, [%{
          created_after: System.os_time(:second) - 300,  # Last 5 minutes
          limit: 100
        }])
        
        # Replicate to target (simplified)
        replication_data = %{
          source_cloud: source_cloud,
          target_cloud: target_cloud,
          events_count: length(recent_events),
          replication_timestamp: System.os_time(:microsecond)
        }
        
        correlation_id = Foundation.Utils.generate_correlation_id()
        
        {:ok, replication_event} = Foundation.Events.new_event(:data_replication, replication_data,
          correlation_id: correlation_id)
        
        :rpc.call(target_region, Foundation.Events, :store, [replication_event])
        
        replication_time = System.monotonic_time(:millisecond) - replication_start
        
        Foundation.Telemetry.emit_counter([:replication, :cross_cloud], %{
          source_cloud: source_cloud,
          target_cloud: target_cloud
        })
        
        Foundation.Telemetry.emit_gauge([:replication, :duration_ms], replication_time, %{
          source_cloud: source_cloud,
          target_cloud: target_cloud
        })
        
        # Replication every 30 seconds
        :timer.sleep(30_000)
        
        replication_loop(cloud_regions, end_time, replications + 1)
      else
        :timer.sleep(30_000)
        replication_loop(cloud_regions, end_time, replications)
      end
    else
      %{replications: replications}
    end
  end
  
  def run_edge_computing(duration_ms) do
    end_time = System.monotonic_time(:millisecond) + duration_ms
    computations = 0
    
    edge_computing_loop(end_time, computations)
  end
  
  defp edge_computing_loop(end_time, computations) do
    if System.monotonic_time(:millisecond) < end_time do
      # Simulate different edge computing workloads
      workload_type = Enum.random([
        :image_processing, :ml_inference, :data_aggregation,
        :real_time_analytics, :iot_processing, :video_transcoding
      ])
      
      computation_start = System.monotonic_time(:millisecond)
      
      # Simulate computational work
      processing_time = case workload_type do
        :image_processing -> :rand.uniform(500) + 200    # 200-700ms
        :ml_inference -> :rand.uniform(300) + 100        # 100-400ms
        :data_aggregation -> :rand.uniform(200) + 50     # 50-250ms
        :real_time_analytics -> :rand.uniform(150) + 25  # 25-175ms
        :iot_processing -> :rand.uniform(100) + 10       # 10-110ms
        :video_transcoding -> :rand.uniform(2000) + 1000 # 1000-3000ms
      end
      
      :timer.sleep(processing_time)
      
      computation_time = System.monotonic_time(:millisecond) - computation_start
      
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, computation_event} = Foundation.Events.new_event(:edge_computation, %{
        workload_type: workload_type,
        processing_time_ms: processing_time,
        computation_time_ms: computation_time,
        edge_region: Node.self(),
        timestamp: System.os_time(:microsecond)
      }, correlation_id: correlation_id)
      
      Foundation.Events.store(computation_event)
      
      Foundation.Telemetry.emit_counter([:edge_computing, :workloads], %{
        workload_type: workload_type
      })
      
      Foundation.Telemetry.emit_gauge([:edge_computing, :processing_time], processing_time, %{
        workload_type: workload_type
      })
      
      # Edge computing workloads every few seconds
      :timer.sleep(:rand.uniform(5000) + 1000)  # 1-6 seconds
      
      edge_computing_loop(end_time, computations + 1)
    else
      %{computations: computations}
    end
  end
  
  defp simulate_cloud_outage(cloud, regions, duration_ms) do
    IO.puts("💥 Simulating #{cloud} cloud outage")
    
    outage_start = System.monotonic_time(:millisecond)
    
    # Simulate taking regions offline
    for region <- regions do
      try do
        :rpc.call(region, :erlang, :disconnect_node, [Node.self()])
      catch
        _, _ -> :ok  # Already disconnected
      end
    end
    
    :timer.sleep(duration_ms)
    
    # Simulate regions coming back online
    for region <- regions do
      try do
        Node.connect(region)
      catch
        _, _ -> :ok
      end
    end
    
    outage_duration = System.monotonic_time(:millisecond) - outage_start
    
    %{outage_duration_ms: outage_duration, affected_regions: length(regions)}
  end
  
  defp coordinate_global_failover(failed_cloud, cloud_regions, duration_ms) do
    failover_start = System.monotonic_time(:millisecond)
    
    # Redirect traffic from failed cloud to healthy clouds
    healthy_clouds = Map.delete(cloud_regions, failed_cloud)
    healthy_regions = List.flatten(Map.values(healthy_clouds))
    
    IO.puts("🔄 Coordinating failover to #{length(healthy_regions)} healthy regions")
    
    # Simulate traffic redistribution
    for region <- healthy_regions do
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, failover_event} = Foundation.Events.new_event(:traffic_failover, %{
        failed_cloud: failed_cloud,
        failover_region: region,
        timestamp: System.os_time(:microsecond)
      }, correlation_id: correlation_id)
      
      :rpc.call(region, Foundation.Events, :store, [failover_event])
      
      :rpc.call(region, Foundation.Telemetry, :emit_counter, [
        [:failover, :traffic_redirected], %{failed_cloud: failed_cloud}
      ])
    end
    
    failover_time = System.monotonic_time(:millisecond) - failover_start
    
    %{failover_time_ms: failover_time, healthy_regions: length(healthy_regions)}
  end
  
  defp coordinate_global_recovery(recovered_cloud, recovered_regions, duration_ms) do
    recovery_start = System.monotonic_time(:millisecond)
    
    IO.puts("🔄 Coordinating recovery for #{recovered_cloud}")
    
    # Gradually redirect traffic back to recovered regions
    for region <- recovered_regions do
      correlation_id = Foundation.Utils.generate_correlation_id()
      
      {:ok, recovery_event} = Foundation.Events.new_event(:traffic_recovery, %{
        recovered_cloud: recovered_cloud,
        recovered_region: region,
        timestamp: System.os_time(:microsecond)
      }, correlation_id: correlation_id)
      
      try do
        :rpc.call(region, Foundation.Events, :store, [recovery_event])
        
        :rpc.call(region, Foundation.Telemetry, :emit_counter, [
          [:recovery, :traffic_restored], %{recovered_cloud: recovered_cloud}
        ])
      catch
        _, _ -> 
          IO.puts("⚠️  Failed to communicate with recovered region #{region}")
      end
    end
    
    recovery_time = System.monotonic_time(:millisecond) - recovery_start
    
    %{recovery_time_ms: recovery_time, recovered_regions: length(recovered_regions)}
  end
  
  # Helper functions for cloud region simulation
  defp simulate_inter_region_latency(region1, region2) do
    # Simulate realistic inter-region latencies
    cloud1 = get_cloud_from_region(region1)
    cloud2 = get_cloud_from_region(region2)
    
    base_latency = if cloud1 == cloud2 do
      :rand.uniform(50) + 10   # Same cloud: 10-60ms
    else
      :rand.uniform(150) + 50  # Cross-cloud: 50-200ms
    end
    
    # Add geographic distance simulation
    base_latency + :rand.uniform(100)
  end
  
  defp get_cloud_from_region(region) do
    region_str = Atom.to_string(region)
    cond do
      String.contains?(region_str, "aws") -> :aws
      String.contains?(region_str, "gcp") -> :gcp
      String.contains?(region_str, "azure") -> :azure
      true -> :unknown
    end
  end
end

ExtremeE1Test.run_scenario()

# CI/CD Pipeline Integration
# Run Foundation scenarios on every commit
mix test --only foundation_scenario

# Run Integration scenarios on PR merge
mix test --only integration_scenario

# Run Production scenarios nightly
mix test --only production_scenario

# Run Extreme scenarios weekly
mix test --only extreme_scenario