Foundation.BEAM - Deep Runtime Integration

lib/foundation/beam.ex

defmodule Foundation.BEAM do @moduledoc """ Foundation 2.0 BEAM Primitives Layer

Provides deep integration with BEAM runtime for optimal performance:

• Process ecosystems that leverage isolation
• Message optimization for zero-copy semantics
• Scheduler-aware operations
• Memory management patterns """

defdelegate spawn_ecosystem(config), to: Foundation.BEAM.Processes defdelegate send_optimized(pid, message, opts \ []), to: Foundation.BEAM.Messages defdelegate cpu_intensive(data, fun), to: Foundation.BEAM.Schedulers defdelegate optimize_memory(strategy, opts \ []), to: Foundation.BEAM.Memory end

Foundation.BEAM.Processes - Revolutionary Process Ecosystems

lib/foundation/beam/processes.ex

defmodule Foundation.BEAM.Processes do @moduledoc """ Process ecosystem patterns that leverage BEAM’s isolation and fault tolerance.

Instead of single large processes, create coordinated ecosystems of specialized processes that communicate efficiently and fail gracefully. """

require Logger

@doc """ Creates a process ecosystem with coordinator and worker processes.

Examples

  # Basic ecosystem
  Foundation.BEAM.Processes.spawn_ecosystem(%{
    coordinator: MyCoordinator,
    workers: {MyWorker, count: 4}
  })
  
  # Advanced ecosystem with memory isolation
  Foundation.BEAM.Processes.spawn_ecosystem(%{
    coordinator: DataCoordinator,
    workers: {DataProcessor, count: :cpu_count},
    memory_strategy: :isolated_heaps,
    distribution: :cluster_wide,
    supervision: :one_for_all
  })

""" def spawn_ecosystem(config) do validate_ecosystem_config!(config)

ecosystem_id = generate_ecosystem_id()
Logger.info("Starting process ecosystem: #{ecosystem_id}")

# Start coordinator first
coordinator_spec = build_coordinator_spec(config, ecosystem_id)
{:ok, coordinator_pid} = start_coordinator(coordinator_spec)

# Start workers based on configuration
worker_specs = build_worker_specs(config, ecosystem_id, coordinator_pid)
worker_pids = start_workers(worker_specs)

# Create ecosystem structure
ecosystem = %{
  id: ecosystem_id,
  coordinator: coordinator_pid,
  workers: worker_pids,
  config: config,
  started_at: :os.system_time(:millisecond),
  health_status: :healthy
}

# Register ecosystem for monitoring
register_ecosystem(ecosystem)

# Return ecosystem reference
{:ok, ecosystem}

end

@doc """ Creates a distributed process society across cluster nodes. """ def create_distributed_society(society_name, config) do case Foundation.Distributed.available?() do true -> create_cluster_wide_society(society_name, config) false -> Logger.warning(“Distributed mode not available, creating local society”) spawn_ecosystem(config) end end

Ecosystem Configuration and Validation

defp validate_ecosystem_config!(config) do required_keys = [:coordinator, :workers]

Enum.each(required_keys, fn key ->
  unless Map.has_key?(config, key) do
    raise ArgumentError, "Ecosystem config missing required key: #{key}"
  end
end)

validate_coordinator_module!(config.coordinator)
validate_worker_config!(config.workers)

end

defp validate_coordinator_module!(module) when is_atom(module) do unless function_exported?(module, :start_link, 1) do raise ArgumentError, “Coordinator module #{module} must export start_link/1” end end

defp validate_worker_config!({module, opts}) when is_atom(module) and is_list(opts) do unless function_exported?(module, :start_link, 1) do raise ArgumentError, “Worker module #{module} must export start_link/1” end

count = Keyword.get(opts, :count, 1)
validate_worker_count!(count)

end

defp validate_worker_count!(:cpu_count), do: :ok defp validate_worker_count!(:auto_scale), do: :ok defp validate_worker_count!(count) when is_integer(count) and count > 0, do: :ok defp validate_worker_count!(invalid) do raise ArgumentError, “Invalid worker count: #{inspect(invalid)}” end

Memory Strategy Implementation

defp build_spawn_opts(memory_strategy, role) do base_opts = []

strategy_opts = case memory_strategy do
  :isolated_heaps ->
    # Separate heap space to prevent GC interference
    [
      {:min_heap_size, calculate_min_heap_size(role)},
      {:fullsweep_after, 10}
    ]
  
  :shared_binaries ->
    # Optimize for sharing large binaries
    [
      {:min_bin_vheap_size, 46368}
    ]
  
  :frequent_gc ->
    # For short-lived, high-allocation processes
    [
      {:fullsweep_after, 5},
      {:min_heap_size, 100}
    ]
  
  :low_latency ->
    # Minimize GC pause times
    [
      {:fullsweep_after, 20},
      {:min_heap_size, 2000}
    ]
  
  :default ->
    []
end

base_opts ++ strategy_opts

end

defp calculate_min_heap_size(:coordinator), do: 1000 defp calculate_min_heap_size(:worker), do: 500

Stub implementations

defp build_coordinator_spec(config, ecosystem_id) do memory_strategy = Map.get(config, :memory_strategy, :default) spawn_opts = build_spawn_opts(memory_strategy, :coordinator)

%{
  module: config.coordinator,
  ecosystem_id: ecosystem_id,
  spawn_opts: spawn_opts,
  config: config
}

end

defp start_coordinator(_spec), do: {:ok, spawn(fn -> :timer.sleep(:infinity) end)} defp build_worker_specs(_config, _ecosystem_id, _coordinator_pid), do: [] defp start_workers(_worker_specs), do: [] defp register_ecosystem(ecosystem), do: :ok defp generate_ecosystem_id(), do: “ecosystem#{:crypto.strong_rand_bytes(8) |> Base.encode64()}” defp create_cluster_wide_society(_society_name, config), do: spawn_ecosystem(config) end

Foundation.BEAM.Messages - Zero-Copy Message Optimization

lib/foundation/beam/messages.ex

defmodule Foundation.BEAM.Messages do @moduledoc """ Optimized message passing that minimizes copying for large data structures.

Leverages BEAM’s binary reference counting and process heap isolation to achieve zero-copy semantics where possible. """

require Logger

@doc """ Sends a message with optimal copying strategy based on data characteristics.

Strategies:

• :direct - Standard message passing (fastest for small data)

• :ets_reference - Store in ETS, send reference (best for large data)

• :binary_sharing - Convert to ref-counted binary (best for frequent sharing)

• :flow_controlled - Use flow control with backpressure """ def send_optimized(pid, data, opts \ []) do strategy = determine_optimal_strategy(data, opts)

  case strategy do :direct -> send_direct(pid, data, opts) :ets_reference -> send_via_ets_reference(pid, data, opts) :binary_sharing -> send_via_binary_sharing(pid, data, opts) :flow_controlled -> send_with_flow_control(pid, data, opts) end end

@doc """ Sends a message with explicit flow control and backpressure handling. """ def send_with_backpressure(pid, data, opts \ []) do max_queue_size = Keyword.get(opts, :max_queue_size, 1000)

case check_process_queue_size(pid) do
  queue_size when queue_size < max_queue_size ->
    send_optimized(pid, data, opts)
  queue_size ->
    Logger.warning("Process #{inspect(pid)} queue size #{queue_size} exceeds limit #{max_queue_size}")
    handle_backpressure(pid, data, opts)
end

end

Strategy Determination

defp determine_optimal_strategy(data, opts) do forced_strategy = Keyword.get(opts, :strategy) if forced_strategy, do: forced_strategy, else: analyze_data_for_strategy(data, opts) end

defp analyze_data_for_strategy(data, opts) do data_size = estimate_data_size(data) frequency = Keyword.get(opts, :frequency, :once) priority = Keyword.get(opts, :priority, :normal)

cond do
  data_size < 1024 and priority == :high ->
    :direct
  
  data_size > 10_240 ->
    :ets_reference
  
  frequency in [:frequent, :continuous] ->
    :binary_sharing
  
  Keyword.get(opts, :flow_control) ->
    :flow_controlled
  
  data_size > 1024 ->
    :binary_sharing
  
  true ->
    :direct
end

end

Direct Message Passing

defp send_direct(pid, data, opts) do priority = Keyword.get(opts, :priority, :normal)

case priority do
  :high ->
    # Use erlang:send with higher priority
    :erlang.send(pid, data, [:noconnect, :nosuspend])
  _ ->
    send(pid, data)
end

end

ETS Reference Strategy

defp send_via_ets_reference(pid, data, opts) do table_name = get_or_create_message_table() ref = make_ref() ttl = Keyword.get(opts, :ttl, 60_000) # 1 minute default

# Store data in ETS with TTL
:ets.insert(table_name, {ref, data, :os.system_time(:millisecond) + ttl})

# Send reference instead of data
send(pid, {:ets_ref, ref, table_name})

# Schedule cleanup
schedule_cleanup(ref, table_name, ttl)

{:ok, ref}

end

Binary Sharing Strategy

defp send_via_binary_sharing(pid, data, opts) do # Convert to binary for ref-counted sharing compression_level = Keyword.get(opts, :compression, :default)

optimized_binary = case compression_level do
  :high ->
    :erlang.term_to_binary(data, [compressed, {:compressed, 9}])
  :low ->
    :erlang.term_to_binary(data, [compressed, {:compressed, 1}])
  :default ->
    :erlang.term_to_binary(data, [:compressed])
end

# Send binary instead of term (allows ref-counting)
send(pid, {:shared_binary, optimized_binary})

{:ok, byte_size(optimized_binary)}

end

Flow Control Implementation

defp send_with_flow_control(pid, data, opts) do max_retries = Keyword.get(opts, :max_retries, 3) retry_delay = Keyword.get(opts, :retry_delay, 100)

case attempt_send_with_retries(pid, data, opts, max_retries, retry_delay) do
  :ok ->
    {:ok, :sent}
  {:error, reason} ->
    {:error, {:flow_control_failed, reason}}
end

end

defp attempt_send_with_retries(pid, data, opts, retries_left, retry_delay) do case check_process_queue_size(pid) do queue_size when queue_size < 1000 -> send_optimized(pid, data, Keyword.put(opts, :strategy, :direct)) :ok

  _queue_size when retries_left > 0 ->
    :timer.sleep(retry_delay)
    attempt_send_with_retries(pid, data, opts, retries_left - 1, retry_delay * 2)
  
  queue_size ->
    {:error, {:queue_full, queue_size}}
end

end

Utility Functions

defp check_process_queue_size(pid) do case Process.info(pid, :message_queue_len) do {:message_queue_len, size} -> size nil -> :infinity # Process dead end end

defp handle_backpressure(pid, _data, opts) do strategy = Keyword.get(opts, :backpressure_strategy, :drop)

case strategy do
  :drop ->
    Logger.warning("Dropping message due to backpressure")
    {:dropped, :backpressure}
  
  :queue ->
    # Would queue in external buffer
    {:queued, :backpressure}
  
  :redirect ->
    # Would find alternative process
    {:dropped, :no_alternative}
end

end

defp estimate_data_size(data) do case data do data when is_binary(data) -> byte_size(data)

  data when is_list(data) ->
    length = length(data)
    sample_size = if length > 0, do: estimate_data_size(hd(data)), else: 0
    length * sample_size
  
  data when is_map(data) ->
    map_size(data) * 100  # Rough estimate
  
  data when is_tuple(data) ->
    tuple_size(data) * 50  # Rough estimate
  
  _other ->
    1000  # Conservative estimate
end

end

defp get_or_create_message_table() do table_name = :foundation_message_cache

case :ets.whereis(table_name) do
  :undefined ->
    :ets.new(table_name, [:set, :public, :named_table])
    table_name
  _tid ->
    table_name
end

end

defp schedule_cleanup(ref, table_name, ttl) do spawn(fn -> :timer.sleep(ttl) :ets.delete(table_name, ref) end) end end

Foundation.BEAM.Schedulers - Reduction-Aware Operations

lib/foundation/beam/schedulers.ex

defmodule Foundation.BEAM.Schedulers do @moduledoc """ Scheduler-aware operations that cooperate with BEAM’s preemptive scheduling.

Provides utilities for CPU-intensive operations that yield appropriately to maintain system responsiveness. """

require Logger

@doc """ Executes CPU-intensive operations while yielding to scheduler.

Examples

  # Process large dataset cooperatively
  Foundation.BEAM.Schedulers.cpu_intensive(large_dataset, fn data ->
    Enum.map(data, &complex_transformation/1)
  end)
  
  # With custom reduction limit
  Foundation.BEAM.Schedulers.cpu_intensive(data, fn data ->
    process_data(data)
  end, reduction_limit: 4000)

""" def cpu_intensive(data, fun, opts \ []) do reduction_limit = Keyword.get(opts, :reduction_limit, 2000) chunk_size = Keyword.get(opts, :chunk_size, :auto)

case chunk_size do
  :auto ->
    auto_chunked_processing(data, fun, reduction_limit)
  size when is_integer(size) ->
    manual_chunked_processing(data, fun, size, reduction_limit)
end

end

@doc """ Monitors scheduler utilization across all schedulers. """ def get_scheduler_utilization() do scheduler_count = :erlang.system_info(:schedulers_online)

utilizations = for scheduler_id <- 1..scheduler_count do
  {scheduler_id, get_single_scheduler_utilization(scheduler_id)}
end

average_utilization = 
  utilizations
  |> Enum.map(fn {_id, util} -> util end)
  |> Enum.sum()
  |> Kernel./(scheduler_count)

%{
  individual: utilizations,
  average: average_utilization,
  scheduler_count: scheduler_count
}

end

@doc """ Balances work across schedulers by pinning processes appropriately. """ def balance_work_across_schedulers(work_items, worker_fun) do scheduler_count = :erlang.system_info(:schedulers_online) chunks = chunk_work_by_schedulers(work_items, scheduler_count)

tasks = for {chunk, scheduler_id} <- Enum.with_index(chunks, 1) do
  Task.async(fn ->
    # Pin this process to specific scheduler
    :erlang.process_flag(:scheduler, scheduler_id)
    
    Enum.map(chunk, fn item ->
      worker_fun.(item)
    end)
  end)
end

# Collect results
results = Task.await_many(tasks, 60_000)
List.flatten(results)

end

Auto-Chunked Processing

defp auto_chunked_processing(data, fun, reduction_limit) when is_list(data) do chunk_size = calculate_optimal_chunk_size(data, reduction_limit) manual_chunked_processing(data, fun, chunk_size, reduction_limit) end

defp auto_chunked_processing(data, fun, reduction_limit) do # For non-list data, process with periodic yielding process_with_periodic_yielding(data, fun, reduction_limit) end

Manual Chunked Processing

defp manual_chunked_processing(data, fun, chunk_size, reduction_limit) when is_list(data) do data |> Enum.chunk_every(chunk_size) |> Enum.reduce([], fn chunk, acc -> # Process chunk chunk_result = fun.(chunk)

  # Check if we should yield
  case should_yield?(reduction_limit) do
    true ->
      :erlang.yield()
    false ->
      :ok
  end
  
  [chunk_result | acc]
end)
|> Enum.reverse()
|> List.flatten()

end

Reduction Management

defp should_yield?(reduction_limit) do get_current_reductions() > reduction_limit end

defp get_current_reductions() do case Process.info(self(), :reductions) do {:reductions, count} -> count nil -> 0 end end

defp calculate_optimal_chunk_size(data, reduction_limit) do data_size = length(data) estimated_reductions_per_item = 10 # Conservative estimate

max(1, div(reduction_limit, estimated_reductions_per_item))

end

Utility functions

defp process_with_periodic_yielding(data, fun, _reduction_limit) do fun.(data) end

defp get_single_scheduler_utilization(_scheduler_id) do # Simplified implementation - would use actual scheduler stats :rand.uniform(100) / 100 end

defp chunk_work_by_schedulers(work_items, scheduler_count) do chunk_size = div(length(work_items), scheduler_count) Enum.chunk_every(work_items, max(1, chunk_size)) end end

Foundation.BEAM.Memory - Intelligent Memory Management

lib/foundation/beam/memory.ex

defmodule Foundation.BEAM.Memory do @moduledoc """ Memory management patterns optimized for BEAM’s garbage collector.

Provides utilities for:

• Binary optimization and ref-counting
• Atom table safety
• GC isolation patterns
• Memory pressure monitoring """

require Logger

@doc """ Optimizes binary data handling to minimize copying and maximize ref-counting. """ def optimize_binary(binary_data, strategy \ :automatic) when is_binary(binary_data) do case strategy do :automatic -> choose_binary_strategy(binary_data) :sharing -> optimize_for_sharing(binary_data) :storage -> optimize_for_storage(binary_data) :transmission -> optimize_for_transmission(binary_data) end end

@doc """ Safely creates atoms with table exhaustion protection. """ def safe_atom(string, opts \ []) when is_binary(string) do max_atoms = Keyword.get(opts, :max_atoms, 800_000) # Conservative limit current_atom_count = :erlang.system_info(:atom_count)

if current_atom_count < max_atoms do
  try do
    {:ok, String.to_atom(string)}
  rescue
    ArgumentError ->
      {:error, :invalid_atom}
    SystemLimitError ->
      {:error, :atom_table_full}
  end
else
  Logger.warning("Atom table approaching limit: #{current_atom_count}/#{max_atoms}")
  {:error, :atom_table_nearly_full}
end

end

@doc """ Monitors memory usage and provides recommendations. """ def analyze_memory_usage() do memory_info = :erlang.memory()

analysis = %{
  total_memory: memory_info[:total],
  process_memory: memory_info[:processes],
  atom_memory: memory_info[:atom],
  binary_memory: memory_info[:binary],
  code_memory: memory_info[:code],
  ets_memory: memory_info[:ets],
  
  # Calculated metrics
  process_memory_percentage: memory_info[:processes] / memory_info[:total] * 100,
  binary_memory_percentage: memory_info[:binary] / memory_info[:total] * 100,
  atom_count: :erlang.system_info(:atom_count),
  atom_limit: :erlang.system_info(:atom_limit),
  
  # Process-specific info
  process_count: :erlang.system_info(:process_count),
  process_limit: :erlang.system_info(:process_limit),
  
  # Recommendations
  recommendations: generate_memory_recommendations(memory_info)
}

analysis

end

Binary Optimization Strategies

defp choose_binary_strategy(binary_data) do size = byte_size(binary_data)

cond do
  size < 64 ->
    # Small binaries - keep as heap binaries
    {:heap_binary, binary_data}
  
  size < 65536 ->
    # Medium binaries - optimize for ref-counting
    optimize_for_sharing(binary_data)
  
  true ->
    # Large binaries - optimize for storage/transmission
    optimize_for_storage(binary_data)
end

end

defp optimize_for_sharing(binary_data) do # Ensure binary is refc (not heap) for sharing if :erlang.binary_part(binary_data, 0, 0) == «» do {:refc_binary, binary_data} else # Convert to refc binary refc_binary = :binary.copy(binary_data) {:refc_binary, refc_binary} end end

defp optimize_for_storage(binary_data) do # Compress for storage compressed = :zlib.compress(binary_data) compression_ratio = byte_size(compressed) / byte_size(binary_data)

if compression_ratio < 0.9 do
  {:compressed_binary, compressed}
else
  # Compression not beneficial
  optimize_for_sharing(binary_data)
end

end

defp optimize_for_transmission(binary_data) do # Optimize for network transmission compressed = :zlib.compress(binary_data) {:transmission_ready, compressed} end

Memory Analysis and Recommendations

defp generate_memory_recommendations(memory_info) do recommendations = []

# Check atom usage
atom_percentage = :erlang.system_info(:atom_count) / :erlang.system_info(:atom_limit) * 100
recommendations = if atom_percentage > 80 do
  ["Atom table usage high (#{trunc(atom_percentage)}%) - review dynamic atom creation" | recommendations]
else
  recommendations
end

# Check binary memory
binary_percentage = memory_info[:binary] / memory_info[:total] * 100
recommendations = if binary_percentage > 40 do
  ["Binary memory high (#{trunc(binary_percentage)}%) - consider binary optimization" | recommendations]
else
  recommendations
end

# Check process memory
process_percentage = memory_info[:processes] / memory_info[:total] * 100
recommendations = if process_percentage > 60 do
  ["Process memory high (#{trunc(process_percentage)}%) - review process count and heap sizes" | recommendations]
else
  recommendations
end

recommendations

end end