raxol

# Scaling & Performance

Comprehensive guide to scaling Raxol applications horizontally and optimizing performance for enterprise workloads.

## Overview

Raxol applications can scale from single-user installations to enterprise deployments serving thousands of concurrent users. This guide covers horizontal scaling strategies, performance optimization, and resource management.

## Horizontal Scaling

### Clustering Architecture

```elixir
defmodule MyApp.Cluster do
  use Raxol.Enterprise.Clustering
  
  def start_cluster do
    # Configure libcluster for automatic node discovery
    topologies = [
      raxol: [
        strategy: Cluster.Strategy.Kubernetes,
        config: [
          kubernetes_selector: "app=raxol",
          kubernetes_namespace: "production",
          polling_interval: 10_000
        ]
      ]
    ]
    
    # Start the cluster supervisor
    children = [
      {Cluster.Supervisor, [topologies, [name: MyApp.ClusterSupervisor]]},
      MyApp.DistributedRegistry,
      MyApp.GlobalProcessManager
    ]
    
    Supervisor.start_link(children, strategy: :one_for_one)
  end
end
```

### Distributed Process Registry

```elixir
defmodule MyApp.DistributedRegistry do
  use Horde.Registry
  
  def start_link(_) do
    Horde.Registry.start_link(
      name: __MODULE__,
      keys: :unique,
      members: :auto
    )
  end
  
  def register_terminal_session(session_id, pid) do
    Horde.Registry.register(__MODULE__, {:terminal, session_id}, pid)
  end
  
  def find_terminal_session(session_id) do
    case Horde.Registry.lookup(__MODULE__, {:terminal, session_id}) do
      [{pid, _}] -> {:ok, pid}
      [] -> {:error, :not_found}
    end
  end
end
```

### Load Distribution

```elixir
defmodule MyApp.LoadBalancer do
  use Raxol.Enterprise.LoadBalancing
  
  # Consistent hashing for session affinity
  def route_session(session_id) do
    nodes = Node.list() ++ [Node.self()]
    hash = :erlang.phash2(session_id, length(nodes))
    target_node = Enum.at(nodes, hash)
    
    case :rpc.call(target_node, MyApp.SessionManager, :create_session, [session_id]) do
      {:ok, pid} -> {:ok, target_node, pid}
      error -> handle_failover(session_id, nodes -- [target_node])
    end
  end
  
  # Health-based routing
  def route_by_health(request) do
    nodes = get_healthy_nodes()
    
    # Sort by load
    sorted_nodes = Enum.sort_by(nodes, fn node ->
      get_node_metrics(node).cpu_usage
    end)
    
    # Route to least loaded node
    target = List.first(sorted_nodes)
    {:ok, target}
  end
end
```

## Performance Optimization

### Component Rendering Optimization

```elixir
defmodule MyApp.OptimizedComponent do
  use Raxol.UI.Components.Base.Component
  use Raxol.Performance.Optimizations
  
  # Memoize expensive computations
  @memoize ttl: 5000
  def calculate_complex_data(state) do
    # Expensive calculation
    Enum.reduce(state.large_dataset, %{}, fn item, acc ->
      Map.put(acc, item.id, process_item(item))
    end)
  end
  
  # Virtual rendering for large lists
  def render(state) do
    {:virtual_list,
      height: 500,
      item_height: 50,
      visible_range: {state.scroll_position, state.scroll_position + 10},
      total_items: length(state.items),
      render_item: &render_single_item/1
    }
  end
  
  # Batch updates
  def handle_events(events, state) when is_list(events) do
    new_state = Enum.reduce(events, state, &apply_event/2)
    {new_state, [{:command, :batch_update_complete}]}
  end
end
```

### Database Optimization

```elixir
defmodule MyApp.DatabaseOptimization do
  use Raxol.Enterprise.Database
  
  # Connection pooling
  def repo_config do
    [
      pool_size: System.schedulers_online() * 2,
      queue_target: 50,
      queue_interval: 1000,
      timeout: 15_000,
      ownership_timeout: 60_000,
      
      # Read replicas
      read_only_replicas: [
        [hostname: "replica1.db.local"],
        [hostname: "replica2.db.local"]
      ]
    ]
  end
  
  # Query optimization
  def optimized_query(user_id) do
    from(s in Session,
      where: s.user_id == ^user_id,
      where: s.active == true,
      preload: [:terminal_state],
      select: %{
        id: s.id,
        started_at: s.started_at,
        last_activity: s.last_activity
      }
    )
    |> Repo.all(timeout: 5_000)
  end
  
  # Batch operations
  def batch_insert(records) do
    Repo.insert_all(
      Record,
      records,
      on_conflict: :nothing,
      conflict_target: :id,
      returning: false,
      timeout: 30_000
    )
  end
end
```

### Caching Strategies

```elixir
defmodule MyApp.CacheManager do
  use Raxol.Enterprise.Caching
  
  # Multi-level caching
  def get_cached(key, fallback_fn) do
    # L1: Process cache (fastest)
    case ProcessCache.get(key) do
      {:ok, value} -> {:ok, value, :l1}
      :miss ->
        # L2: Distributed cache
        case DistributedCache.get(key) do
          {:ok, value} ->
            ProcessCache.put(key, value)
            {:ok, value, :l2}
          :miss ->
            # L3: Database
            value = fallback_fn.()
            cache_value(key, value)
            {:ok, value, :l3}
        end
    end
  end
  
  defp cache_value(key, value) do
    # Cache with appropriate TTL
    ttl = calculate_ttl(key, value)
    
    # Update all cache levels
    ProcessCache.put(key, value, ttl: ttl)
    DistributedCache.put(key, value, ttl: ttl)
  end
  
  # Cache invalidation
  def invalidate(pattern) do
    # Broadcast invalidation to all nodes
    Phoenix.PubSub.broadcast(
      MyApp.PubSub,
      "cache:invalidation",
      {:invalidate, pattern}
    )
  end
end
```

### WebSocket Optimization

```elixir
defmodule MyApp.WebSocketOptimization do
  use Raxol.Enterprise.WebSocket
  
  # Message batching
  def batch_messages(socket) do
    Process.send_after(self(), :flush_messages, 16)  # ~60fps
    
    socket
    |> assign(:message_buffer, [])
    |> assign(:batching, true)
  end
  
  def handle_info(:flush_messages, socket) do
    case socket.assigns.message_buffer do
      [] ->
        {:noreply, socket}
      messages ->
        # Send all messages in one frame
        push(socket, "batch_update", %{messages: Enum.reverse(messages)})
        
        # Schedule next flush
        Process.send_after(self(), :flush_messages, 16)
        
        {:noreply, assign(socket, :message_buffer, [])}
    end
  end
  
  # Compression
  def compress_large_payloads(data) when byte_size(data) > 1024 do
    compressed = :zlib.compress(data)
    
    if byte_size(compressed) < byte_size(data) * 0.9 do
      {:compressed, Base.encode64(compressed)}
    else
      {:raw, data}
    end
  end
end
```

## Resource Management

### Memory Management

```elixir
defmodule MyApp.MemoryManager do
  use Raxol.Enterprise.Resources
  
  def monitor_memory do
    memory_config = %{
      max_heap_size: 2_000_000,  # 2M words
      max_binary_vheap: 10_000_000,
      fullsweep_after: 20
    }
    
    # Apply to all terminal processes
    :pg.get_members(TerminalProcesses)
    |> Enum.each(fn pid ->
      Process.spawn(fn ->
        :erlang.process_flag(:max_heap_size, memory_config.max_heap_size)
        Process.garbage_collect(pid, type: :major)
      end)
    end)
  end
  
  # Memory pressure handling
  def handle_memory_pressure do
    memory_usage = :erlang.memory(:total) / :erlang.memory(:system)
    
    cond do
      memory_usage > 0.9 ->
        # Critical: Shed load
        shed_non_critical_processes()
        force_garbage_collection()
        
      memory_usage > 0.8 ->
        # Warning: Reduce caches
        reduce_cache_sizes()
        trigger_garbage_collection()
        
      true ->
        :ok
    end
  end
end
```

### CPU Optimization

```elixir
defmodule MyApp.CPUOptimizer do
  use Raxol.Enterprise.CPU
  
  # Work stealing for balanced CPU usage
  def distribute_work(tasks) do
    schedulers = System.schedulers_online()
    
    # Create work queues per scheduler
    queues = for i <- 1..schedulers, do: {:queue, i, []}
    
    # Distribute tasks round-robin
    distributed = Enum.reduce(tasks, {0, queues}, fn task, {idx, queues} ->
      queue_idx = rem(idx, schedulers)
      updated_queues = update_queue(queues, queue_idx, task)
      {idx + 1, updated_queues}
    end)
    
    # Start workers
    for {_, _, tasks} <- elem(distributed, 1) do
      Task.async(fn -> process_tasks(tasks) end)
    end
    |> Task.await_many()
  end
  
  # CPU-bound operation optimization
  def optimize_rendering(components) do
    # Use Flow for parallel processing
    components
    |> Flow.from_enumerable(max_demand: 100)
    |> Flow.partition(max_demand: 50)
    |> Flow.map(&render_component/1)
    |> Flow.reduce(fn -> [] end, &[&1 | &2])
    |> Enum.reverse()
  end
end
```

## Auto-Scaling

### Metrics-Based Scaling

```elixir
defmodule MyApp.AutoScaler do
  use Raxol.Enterprise.AutoScaling
  
  @scale_up_threshold 0.8
  @scale_down_threshold 0.3
  
  def evaluate_scaling do
    metrics = collect_metrics()
    
    cond do
      should_scale_up?(metrics) ->
        scale_up()
        
      should_scale_down?(metrics) ->
        scale_down()
        
      true ->
        :no_action
    end
  end
  
  defp should_scale_up?(metrics) do
    metrics.cpu_usage > @scale_up_threshold or
    metrics.memory_usage > @scale_up_threshold or
    metrics.connection_ratio > @scale_up_threshold
  end
  
  defp scale_up do
    current_nodes = get_node_count()
    target_nodes = min(current_nodes + 2, max_nodes())
    
    # Trigger Kubernetes scaling
    :os.cmd('kubectl scale deployment raxol-app --replicas=#{target_nodes}')
    
    # Pre-warm connections
    schedule_pre_warming(target_nodes - current_nodes)
  end
end
```

### Predictive Scaling

```elixir
defmodule MyApp.PredictiveScaling do
  use Raxol.Enterprise.ML.Scaling
  
  def predict_load do
    # Historical data
    historical = get_historical_metrics(days: 30)
    
    # Time-based patterns
    patterns = %{
      hourly: analyze_hourly_patterns(historical),
      daily: analyze_daily_patterns(historical),
      weekly: analyze_weekly_patterns(historical)
    }
    
    # Predict next hour's load
    prediction = predict_next_period(patterns, :hour)
    
    # Schedule scaling
    if prediction.confidence > 0.8 do
      schedule_scaling(prediction.expected_load)
    end
  end
  
  defp schedule_scaling(expected_load) do
    required_nodes = calculate_required_nodes(expected_load)
    current_nodes = get_node_count()
    
    if required_nodes > current_nodes do
      # Scale up before load arrives
      delay = calculate_optimal_delay()
      Process.send_after(self(), {:scale_to, required_nodes}, delay)
    end
  end
end
```

## Performance Monitoring

### Real-Time Metrics

```elixir
defmodule MyApp.PerformanceMetrics do
  use Raxol.Enterprise.Metrics
  
  def track_operation(name, metadata \\ %{}) do
    start_time = System.monotonic_time()
    
    try do
      result = yield()
      duration = System.monotonic_time() - start_time
      
      # Record success metrics
      :telemetry.execute(
        [:myapp, :operation, :complete],
        %{duration: duration},
        Map.merge(metadata, %{name: name, status: :success})
      )
      
      result
    rescue
      error ->
        duration = System.monotonic_time() - start_time
        
        # Record failure metrics
        :telemetry.execute(
          [:myapp, :operation, :complete],
          %{duration: duration},
          Map.merge(metadata, %{name: name, status: :error, error: error})
        )
        
        reraise error, __STACKTRACE__
    end
  end
end
```

### Performance Budgets

```elixir
defmodule MyApp.PerformanceBudgets do
  use Raxol.Enterprise.Budgets
  
  # Define budgets
  budgets do
    # Rendering must be under 16ms (60 FPS)
    operation :render, max_ms: 16, percentile: 95
    
    # API responses under 100ms
    operation :api_response, max_ms: 100, percentile: 99
    
    # Database queries under 50ms
    operation :db_query, max_ms: 50, percentile: 95
    
    # WebSocket latency under 50ms
    operation :ws_latency, max_ms: 50, percentile: 99
  end
  
  # Enforcement
  def check_budgets do
    violations = get_budget_violations()
    
    if Enum.any?(violations) do
      alert_performance_degradation(violations)
      apply_compensating_actions(violations)
    end
  end
end
```

## Best Practices

1. **Design for Scale**: Build with distribution in mind from the start
2. **Monitor Everything**: Comprehensive metrics and observability
3. **Cache Aggressively**: But invalidate correctly
4. **Optimize Hot Paths**: Profile and optimize critical code paths
5. **Test at Scale**: Load test with realistic workloads
6. **Plan Capacity**: Use predictive scaling and capacity planning
7. **Handle Failures**: Design for partial failures and degradation

## Troubleshooting

### Common Scaling Issues

1. **Split Brain**
   ```elixir
   # Detect and heal network partitions
   MyApp.Cluster.heal_partition()
   ```

2. **Message Queue Buildup**
   ```elixir
   # Monitor and alert on mailbox sizes
   MyApp.ProcessMonitor.check_mailboxes()
   ```

3. **Database Connection Exhaustion**
   ```elixir
   # Adjust pool size dynamically
   MyApp.Database.resize_pool()
   ```

## Performance Checklist

- [ ] Implement horizontal scaling
- [ ] Set up distributed caching
- [ ] Optimize database queries
- [ ] Configure connection pooling
- [ ] Implement message batching
- [ ] Set up auto-scaling
- [ ] Define performance budgets
- [ ] Monitor all metrics
- [ ] Test at expected scale
- [ ] Plan for failure scenarios

## Next Steps

- Set up [Monitoring](monitoring.md) for performance metrics
- Configure [Deployment](deployment.md) for scaling
- Implement [Security](security.md) at scale
- Review [Authentication](authentication.md) performance