Snakepit 🐍

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A high-performance, generalized process pooler and session manager for external language integrations in Elixir

CI Hex Version License: MIT

🚀 What is Snakepit?

Snakepit is a battle-tested Elixir library that provides a robust pooling system for managing external processes (Python, Node.js, Ruby, R, etc.). Born from the need for reliable ML/AI integrations, it offers:

  • Lightning-fast concurrent initialization - 1000x faster than sequential approaches
  • Session-based execution with automatic worker affinity
  • Adapter pattern for any external language/runtime
  • Built on OTP primitives - DynamicSupervisor, Registry, GenServer
  • Production-ready with telemetry, health checks, and graceful shutdowns

📋 Table of Contents

🏃 Quick Start

# In your mix.exs
def deps do
  [
    {:snakepit, "~> 0.1.2"}
  ]
end

# Configure and start
Application.put_env(:snakepit, :pooling_enabled, true)
Application.put_env(:snakepit, :adapter_module, Snakepit.Adapters.GenericPython)
Application.put_env(:snakepit, :pool_config, %{pool_size: 4})

{:ok, _} = Application.ensure_all_started(:snakepit)

# Execute commands
{:ok, result} = Snakepit.execute("ping", %{test: true})
{:ok, result} = Snakepit.execute("compute", %{operation: "add", a: 5, b: 3})

# Session-based execution (maintains state)
{:ok, result} = Snakepit.execute_in_session("user_123", "echo", %{message: "hello"})

📦 Installation

Hex Package

def deps do
  [
    {:snakepit, "~> 0.1.2"}
  ]
end

GitHub (Latest)

def deps do
  [
    {:snakepit, github: "nshkrdotcom/snakepit"}
  ]
end

Requirements

  • Elixir 1.18+
  • Erlang/OTP 27+
  • External runtime (Python 3.8+, Node.js 16+, etc.) depending on adapter

🎯 Core Concepts

1. Adapters

Adapters define how Snakepit communicates with external processes. They specify:

  • The runtime executable (python3, node, ruby, etc.)
  • The bridge script to execute
  • Supported commands and validation
  • Request/response transformations

2. Workers

Each worker is a GenServer that:

  • Owns one external process via Erlang Port
  • Handles request/response communication
  • Manages health checks and metrics
  • Auto-restarts on crashes

3. Pool

The pool manager:

  • Starts workers concurrently on initialization
  • Routes requests to available workers
  • Handles queueing when all workers are busy
  • Supports session affinity for stateful operations

4. Sessions

Sessions provide:

  • State persistence across requests
  • Worker affinity (same session prefers same worker)
  • TTL-based expiration
  • Centralized storage in ETS

⚙️ Configuration

Basic Configuration

# config/config.exs
config :snakepit,
  pooling_enabled: true,
  adapter_module: Snakepit.Adapters.GenericPython,
  pool_config: %{
    pool_size: 8  # Default: System.schedulers_online() * 2
  }

Advanced Configuration

config :snakepit,
  # Pool settings
  pooling_enabled: true,
  pool_config: %{
    pool_size: 16
  },
  
  # Adapter
  adapter_module: MyApp.CustomAdapter,
  
  # Timeouts (milliseconds)
  pool_startup_timeout: 10_000,      # Max time for worker initialization
  pool_queue_timeout: 5_000,         # Max time in request queue
  worker_init_timeout: 20_000,       # Max time for worker to respond to init
  worker_health_check_interval: 30_000,  # Health check frequency
  worker_shutdown_grace_period: 2_000,   # Grace period for shutdown
  
  # Cleanup settings
  cleanup_retry_interval: 100,       # Retry interval for cleanup
  cleanup_max_retries: 10,          # Max cleanup retries
  
  # Queue management
  pool_max_queue_size: 1000         # Max queued requests before rejection

Runtime Configuration

# Override configuration at runtime
Application.put_env(:snakepit, :adapter_module, Snakepit.Adapters.GenericJavaScript)
Application.stop(:snakepit)
Application.start(:snakepit)

📖 Usage Examples

Basic Stateless Execution

# Simple computation
{:ok, %{"result" => 8}} = Snakepit.execute("compute", %{
  operation: "add",
  a: 5,
  b: 3
})

# With timeout
{:ok, result} = Snakepit.execute("long_running_task", %{data: "..."}, timeout: 60_000)

# Error handling
case Snakepit.execute("risky_operation", %{}) do
  {:ok, result} -> handle_success(result)
  {:error, :worker_timeout} -> handle_timeout()
  {:error, :pool_saturated} -> handle_overload()
  {:error, reason} -> handle_error(reason)
end

Session-Based Execution

# Create a session and maintain state
session_id = "user_#{user.id}"

# First request - initializes session
{:ok, _} = Snakepit.execute_in_session(session_id, "initialize", %{
  user_id: user.id,
  preferences: user.preferences
})

# Subsequent requests use same worker when possible
{:ok, recommendations} = Snakepit.execute_in_session(session_id, "get_recommendations", %{
  category: "books"
})

# Session data persists across requests
{:ok, history} = Snakepit.execute_in_session(session_id, "get_history", %{})

ML/AI Workflow Example

# Using SessionHelpers for ML program management
alias Snakepit.SessionHelpers

# Create an ML program/model
{:ok, response} = SessionHelpers.execute_program_command(
  "ml_session_123",
  "create_program",
  %{
    signature: "question -> answer",
    model: "gpt-3.5-turbo",
    temperature: 0.7
  }
)

program_id = response["program_id"]

# Execute the program multiple times
{:ok, result} = SessionHelpers.execute_program_command(
  "ml_session_123", 
  "execute_program",
  %{
    program_id: program_id,
    input: %{question: "What is the capital of France?"}
  }
)

Parallel Processing

# Process multiple items in parallel across the pool
items = ["item1", "item2", "item3", "item4", "item5"]

tasks = Enum.map(items, fn item ->
  Task.async(fn ->
    Snakepit.execute("process_item", %{item: item})
  end)
end)

results = Task.await_many(tasks, 30_000)

🔌 Built-in Adapters

Python Adapter

# Configure
Application.put_env(:snakepit, :adapter_module, Snakepit.Adapters.GenericPython)

# Available commands
{:ok, _} = Snakepit.execute("ping", %{})
{:ok, _} = Snakepit.execute("echo", %{message: "hello"})
{:ok, _} = Snakepit.execute("compute", %{operation: "multiply", a: 10, b: 5})
{:ok, _} = Snakepit.execute("info", %{})

JavaScript/Node.js Adapter

# Configure
Application.put_env(:snakepit, :adapter_module, Snakepit.Adapters.GenericJavaScript)

# Additional commands
{:ok, _} = Snakepit.execute("random", %{type: "uniform", min: 0, max: 100})
{:ok, _} = Snakepit.execute("compute", %{operation: "sqrt", a: 16})

🛠️ Creating Custom Adapters

Elixir Adapter Implementation

defmodule MyApp.RubyAdapter do
  @behaviour Snakepit.Adapter
  
  @impl true
  def executable_path do
    System.find_executable("ruby")
  end
  
  @impl true
  def script_path do
    Path.join(:code.priv_dir(:my_app), "ruby/bridge.rb")
  end
  
  @impl true
  def script_args do
    ["--mode", "pool-worker"]
  end
  
  @impl true
  def supported_commands do
    ["ping", "process_data", "generate_report"]
  end
  
  @impl true
  def validate_command("process_data", args) do
    if Map.has_key?(args, :data) do
      :ok
    else
      {:error, "Missing required field: data"}
    end
  end
  
  def validate_command("ping", _args), do: :ok
  def validate_command(cmd, _args), do: {:error, "Unsupported command: #{cmd}"}
  
  # Optional callbacks
  @impl true
  def prepare_args("process_data", args) do
    # Transform args before sending
    Map.update(args, :data, "", &String.trim/1)
  end
  
  @impl true
  def process_response("generate_report", %{"report" => report} = response) do
    # Post-process the response
    {:ok, Map.put(response, "processed_at", DateTime.utc_now())}
  end
  
  @impl true
  def command_timeout("generate_report", _args), do: 120_000  # 2 minutes
  def command_timeout(_command, _args), do: 30_000  # Default 30 seconds
end

External Bridge Script (Ruby Example)

#!/usr/bin/env ruby
# priv/ruby/bridge.rb

require 'json'

class BridgeHandler
  def initialize
    @commands = {
      'ping' => method(:handle_ping),
      'process_data' => method(:handle_process_data),
      'generate_report' => method(:handle_generate_report)
    }
  end
  
  def run
    STDERR.puts "Ruby bridge started"
    
    loop do
      # Read 4-byte length header
      length_bytes = STDIN.read(4)
      break unless length_bytes
      
      # Unpack length (big-endian)
      length = length_bytes.unpack('N')[0]
      
      # Read JSON payload
      json_data = STDIN.read(length)
      request = JSON.parse(json_data)
      
      # Process command
      response = process_command(request)
      
      # Send response
      json_response = JSON.generate(response)
      length_header = [json_response.bytesize].pack('N')
      
      STDOUT.write(length_header)
      STDOUT.write(json_response)
      STDOUT.flush
    end
  end
  
  private
  
  def process_command(request)
    command = request['command']
    args = request['args'] || {}
    
    handler = @commands[command]
    if handler
      result = handler.call(args)
      {
        'id' => request['id'],
        'success' => true,
        'result' => result,
        'timestamp' => Time.now.iso8601
      }
    else
      {
        'id' => request['id'],
        'success' => false,
        'error' => "Unknown command: #{command}",
        'timestamp' => Time.now.iso8601
      }
    end
  rescue => e
    {
      'id' => request['id'],
      'success' => false,
      'error' => e.message,
      'timestamp' => Time.now.iso8601
    }
  end
  
  def handle_ping(args)
    { 'status' => 'ok', 'message' => 'pong' }
  end
  
  def handle_process_data(args)
    data = args['data'] || ''
    { 'processed' => data.upcase, 'length' => data.length }
  end
  
  def handle_generate_report(args)
    # Simulate report generation
    sleep(1)
    { 
      'report' => {
        'title' => args['title'] || 'Report',
        'generated_at' => Time.now.iso8601,
        'data' => args['data'] || {}
      }
    }
  end
end

# Handle signals gracefully
Signal.trap('TERM') { exit(0) }
Signal.trap('INT') { exit(0) }

# Run the bridge
BridgeHandler.new.run

🗃️ Session Management

Session Store API

alias Snakepit.Bridge.SessionStore

# Create a session
{:ok, session} = SessionStore.create_session("session_123", ttl: 7200)

# Store data in session
:ok = SessionStore.store_program("session_123", "prog_1", %{
  model: "gpt-4",
  temperature: 0.8
})

# Retrieve session data
{:ok, session} = SessionStore.get_session("session_123")
{:ok, program} = SessionStore.get_program("session_123", "prog_1")

# Update session
{:ok, updated} = SessionStore.update_session("session_123", fn session ->
  Map.put(session, :last_activity, DateTime.utc_now())
end)

# Check if session exists
true = SessionStore.session_exists?("session_123")

# List all sessions
session_ids = SessionStore.list_sessions()

# Manual cleanup
SessionStore.delete_session("session_123")

# Get session statistics
stats = SessionStore.get_stats()

Global Program Storage

# Store programs accessible by any worker
:ok = SessionStore.store_global_program("template_1", %{
  type: "qa_template",
  prompt: "Answer the following question: {question}"
})

# Retrieve from any worker
{:ok, template} = SessionStore.get_global_program("template_1")

📊 Monitoring & Telemetry

Available Events

# Worker request completed
[:snakepit, :worker, :request]
# Measurements: %{duration: milliseconds}
# Metadata: %{result: :ok | :error}

# Worker initialized
[:snakepit, :worker, :initialized]
# Measurements: %{initialization_time: seconds}
# Metadata: %{worker_id: string}

Setting Up Monitoring

# In your application startup
:telemetry.attach_many(
  "snakepit-metrics",
  [
    [:snakepit, :worker, :request],
    [:snakepit, :worker, :initialized]
  ],
  &MyApp.Metrics.handle_event/4,
  %{}
)

defmodule MyApp.Metrics do
  require Logger
  
  def handle_event([:snakepit, :worker, :request], measurements, metadata, _config) do
    # Log slow requests
    if measurements.duration > 5000 do
      Logger.warning("Slow request: #{measurements.duration}ms")
    end
    
    # Send to StatsD/Prometheus/DataDog
    MyApp.Metrics.Client.histogram(
      "snakepit.request.duration",
      measurements.duration,
      tags: ["result:#{metadata.result}"]
    )
  end
  
  def handle_event([:snakepit, :worker, :initialized], measurements, metadata, _config) do
    Logger.info("Worker #{metadata.worker_id} started in #{measurements.initialization_time}s")
  end
end

Pool Statistics

stats = Snakepit.get_stats()
# Returns:
# %{
#   workers: 8,          # Total workers
#   available: 6,        # Available workers
#   busy: 2,            # Busy workers
#   requests: 1534,     # Total requests
#   queued: 0,          # Currently queued
#   errors: 12,         # Total errors
#   queue_timeouts: 3,  # Queue timeout count
#   pool_saturated: 0   # Saturation rejections
# }

🏗️ Architecture Deep Dive

Component Overview


                    Snakepit Application               

                                                       
       
      Pool        SessionStore     Registries    
    Manager         (ETS)          (Worker/Proc) 
       
                                                      
  
              WorkerSupervisor (Dynamic)             
  
                                                      
        
     Worker         Worker          Worker       
    Starter        Starter         Starter       
  (Supervisor)   (Supervisor)    (Supervisor)    
        
                                                    
        
     Worker         Worker          Worker       
   (GenServer)    (GenServer)     (GenServer)    
        
                                                    

                                            
            
      External        External        External  
      Process         Process         Process   
      (Python)        (Node.js)        (Ruby)   
            

Key Design Decisions

  1. Concurrent Initialization: Workers start in parallel using Task.async_stream
  2. Permanent Wrapper Pattern: Worker.Starter supervises Workers for auto-restart
  3. Centralized State: All session data in ETS, workers are stateless
  4. Registry-Based: O(1) worker lookups and reverse PID lookups
  5. Port Communication: Binary protocol with 4-byte length headers

Process Lifecycle

  1. Startup:

    • Pool manager starts
    • Concurrently spawns N workers via WorkerSupervisor
    • Each worker starts its external process
    • Workers send init ping and register when ready
  2. Request Flow:

    • Client calls Snakepit.execute/3
    • Pool finds available worker (with session affinity if applicable)
    • Worker sends request to external process
    • External process responds
    • Worker returns result to client
  3. Crash Recovery:

    • Worker crashes → Worker.Starter restarts it automatically
    • External process dies → Worker detects and crashes → restart
    • Pool crashes → Supervisor restarts entire pool
  4. Shutdown:

    • Pool manager sends shutdown to all workers
    • Workers close ports gracefully (SIGTERM)
    • ApplicationCleanup ensures no orphaned processes (SIGKILL)

⚡ Performance

Benchmarks

Configuration: 16 workers, Python adapter
Hardware: 8-core CPU, 32GB RAM

Startup Time:
- Sequential: 16 seconds (1s per worker)
- Concurrent: 1.2 seconds (13x faster)

Throughput:
- Simple computation: 50,000 req/s
- Complex ML inference: 1,000 req/s
- Session operations: 45,000 req/s

Latency (p99):
- Simple computation: < 2ms
- Complex ML inference: < 100ms
- Session operations: < 1ms

Optimization Tips

  1. Pool Size: Start with System.schedulers_online() * 2
  2. Queue Size: Monitor pool_saturated errors and adjust
  3. Timeouts: Set appropriate timeouts per command type
  4. Session TTL: Balance memory usage vs cache hits
  5. Health Checks: Increase interval for stable workloads

🔧 Troubleshooting

Common Issues

Workers Not Starting

# Check adapter configuration
adapter = Application.get_env(:snakepit, :adapter_module)
adapter.executable_path()  # Should return valid path
File.exists?(adapter.script_path())  # Should return true

# Check logs for errors
Logger.configure(level: :debug)

Port Exits

# Enable port tracing
:erlang.trace(Process.whereis(Snakepit.Pool.Worker), true, [:receive, :send])

# Check external process logs
# Python: Add logging to bridge script
# Node.js: Check stderr output

Memory Leaks

# Monitor ETS usage
:ets.info(:snakepit_sessions, :memory)

# Check for orphaned processes
Snakepit.Pool.ProcessRegistry.get_stats()

# Force cleanup
Snakepit.Bridge.SessionStore.cleanup_expired_sessions()

Debug Mode

# Enable debug logging
Logger.configure(level: :debug)

# Trace specific worker
:sys.trace(Snakepit.Pool.Registry.via_tuple("worker_1"), true)

# Get internal state
:sys.get_state(Snakepit.Pool)

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repo
git clone https://github.com/nshkrdotcom/snakepit.git
cd snakepit

# Install dependencies
mix deps.get

# Run tests
mix test

# Run with example scripts
elixir examples/session_based_demo.exs
elixir examples/javascript_session_demo.exs

# Check code quality
mix format --check-formatted
mix dialyzer

Running Tests

# All tests
mix test

# With coverage
mix test --cover

# Specific test
mix test test/snakepit_test.exs:42

📝 License

Snakepit is released under the MIT License. See the LICENSE file for details.

🙏 Acknowledgments

  • Inspired by the need for reliable ML/AI integrations in Elixir
  • Built on battle-tested OTP principles
  • Special thanks to the Elixir community

📚 Resources


Made with ❤️ by NSHkr