Gleam Performance Optimization: AI-Optimized Technical Reference

Overview

Gleam inherits BEAM VM performance characteristics: excellent for concurrency and fault tolerance, comparable to Python for CPU-intensive tasks. Not suitable for number crunching but designed for latency optimization and handling massive concurrent workloads.

Critical Performance Characteristics

BEAM VM Limitations

• CPU Performance: Comparable to Python speed for computational tasks
• Design Purpose: Optimized for latency and fault tolerance, not raw computational speed
• Process Overhead: ~2KB minimum per process
• Maximum Concurrency: 200,000+ concurrent processes proven in production

Proven Scale Examples

• WhatsApp: 40+ billion messages daily, 2+ billion users
• Discord: Billions of messages stored using BEAM architecture
• Both demonstrate BEAM's production viability at massive scale

Memory Architecture

Per-Process Memory Layout

Each BEAM process allocates four memory blocks:

1. Stack: Return addresses, function arguments, local variables
2. Heap: Lists, tuples, larger data structures
3. Message Area: Inter-process communication mailbox
4. Process Control Block: Process metadata

Memory Management

• No global stop-the-world GC: Per-process garbage collection
• Preemptive scheduling: Prevents process monopolization
• Lightweight processes: Unlike OS threads, BEAM processes are VM-managed

Common Performance Failure Modes

Pattern Matching Overhead

• Issue: Complex nested patterns create expensive dispatch trees
• Impact: Performance degradation in hot paths
• Solution: Simplify patterns, use guard clauses strategically

List Operations Anti-Patterns

• Critical Failure: list.length() is O(n), not O(1)
• Real Example: 6-hour debugging session caused by list.length() on 50k-item lists in hot path
• Breaking Point: Calling list.length(my_list) > 1000 on large lists
• Solution: Use linked list properties, avoid length checks in hot paths

Tail Call Optimization Failures

• Issue: Non-tail recursive functions consume stack memory linearly
• Consequence: Memory exhaustion on large datasets
• Detection: Compiler won't warn about missing tail call optimization
• Fix: Use accumulator pattern for recursive functions

String Concatenation Performance

• Problem: BEAM strings are UTF-8 binaries, concatenation creates new binaries
• Impact: Exponential memory usage in loops
• Solution: Use iodata for building strings incrementally

Performance Monitoring Tools

Observer (Primary Tool)

• Purpose: GUI system monitoring for BEAM applications
• Compatibility: Full support for Gleam (compiles to BEAM bytecode)
• Remote Access: SSH tunnel support for production debugging
• Key Metrics: Process memory, CPU reductions, scheduler utilization

Critical Monitoring Points:

• Processes tab: Sort by memory/reductions to identify resource consumers
• System tab: Memory allocator analysis
• Load Charts: CPU and memory trends over time

Spectator (Gleam-Specific)

• Advantage: Native Gleam process understanding
• Interface: Web UI at localhost:9001
• Limitation: High overhead with millions of processes
• Best Practice: Tag processes for easier identification

recon (Production-Safe)

• Use Case: Command-line production debugging
• Safety: Built-in limits prevent system crashes
• Key Functions:
  • recon:proc_count(memory, 10): Top memory consumers
  • recon:proc_count(reductions, 10): Top CPU consumers
  • Safe function call tracing with automatic limits

Performance Profiling Tools Comparison

Tool	Overhead	Remote Access	Production Safe	Best For
Observer	Low	Yes (SSH)	Yes	General debugging
Spectator	Medium	Yes (Web)	Limited	Gleam-specific monitoring
recon	Very Low	Yes (Shell)	Yes	Production troubleshooting
eflame	High	No	No	CPU bottleneck analysis
perf	Very Low	No	Yes	System-level profiling

Configuration Issues

Scheduler Configuration Problems

• Default: One scheduler per CPU core
• Problem: Not optimal for all hardware configurations
• Detection: Uneven load across schedulers in Observer
• Solution: Tune +S (scheduler count) and +A (async thread pool) VM arguments

Memory Allocation Debugging

Key Allocators to Monitor:

• binary_alloc: Large binaries (strings, files)
• eheap_alloc: Process heaps
• ets_alloc: ETS table storage
• ll_alloc: Linked list data

Detection Command: recon_alloc:memory(used) shows allocator usage breakdown

Critical Production Issues

High CPU Usage When Idle

• Cause: Scheduler busy-wait behavior (designed for low latency)
• Symptom: 100% CPU usage on idle systems
• Fix: Add +sbwt none to VM flags (trades latency for lower idle CPU)

Memory "Leaks"

• Reality: Usually message queue buildup, not true memory leaks
• Detection: recon:proc_count(message_queue_len, 10)
• Threshold: Message queues >1000 indicate problems
• Root Cause: Processes cannot keep up with incoming messages

Function Clause Crashes in Production

• Cause: Non-exhaustive pattern matching with real-world edge cases
• Prevention: Add catch-all patterns with proper error handling and logging
• Debug Pattern: Log unexpected cases instead of crashing

Performance Optimization Patterns

Tail Call Optimization Template

// Bad: Non-tail recursive (memory grows)
pub fn sum_list(list: List(Int)) -> Int {
  case list {
    [] -> 0
    [head, ..tail] -> head + sum_list(tail)  // NOT tail recursive
  }
}

// Good: Tail recursive (constant memory)
pub fn sum_list(list: List(Int)) -> Int {
  sum_list_helper(list, 0)
}

fn sum_list_helper(list: List(Int), acc: Int) -> Int {
  case list {
    [] -> acc
    [head, ..tail] -> sum_list_helper(tail, acc + head)  // Tail recursive
  }
}

Debug Performance with Echo Keyword

import gleam/erlang/system_time

pub fn timed_operation(data) {
  let start = system_time.monotonic_time()

  let result = data
    |> expensive_operation()
    |> echo("Operation completed")

  let end = system_time.monotonic_time()
  let duration_ms = (end - start) / 1_000_000

  io.println("Operation took " <> int.to_string(duration_ms) <> "ms")
  result
}

Resource Requirements

Development Environment

• Observer: GUI access required, SSH tunneling for remote systems
• recon: Command-line proficiency with Erlang shell
• Flame graphs: External tools for visualization (Brendan Gregg's generator)

Production Monitoring

• Minimum Setup: recon library for safe production debugging
• Remote Access: SSH tunnel configuration for Observer
• Expertise Level: Understanding BEAM process model and memory management

Verified Performance Improvements

Compiler Optimizations (Shipped)

• v1.11.0: 30% faster JavaScript compilation (June 2025)
• v1.12.0: Function inlining enabled with conservative configuration
• Binary Operations: Constant-time sub-slice operations on JavaScript target

Performance Testing Validation

• Richard Viney benchmarked real workloads to verify improvements
• Not marketing metrics - actual production-relevant performance gains

Critical Warnings

What Official Documentation Doesn't Tell You

1. List Length Checks: Will destroy performance on large datasets
2. Recursive Functions: Compiler won't warn about non-tail-recursive patterns
3. String Building: Concatenation in loops creates exponential memory usage
4. Message Queues: Can grow infinitely without backpressure handling
5. Scheduler Busy-Wait: Shows as high CPU usage even when idle

Breaking Points and Failure Modes

• List Operations: O(n) operations disguised as constant time
• Pattern Matching: Complex nested patterns become dispatch bottlenecks
• Memory Allocation: Different allocators for different data types can bottleneck independently
• Process Spawning: 2KB overhead means millions of processes require careful memory planning

Real-World Failure Examples

• 6-hour debugging session: list.length() calls on 50k-item lists in API hot path
• Memory exhaustion: Non-tail recursive functions on large datasets
• Production crashes: Non-exhaustive pattern matching with edge case data

Essential Command Reference

Production Debugging Commands

% Connect to production node
$ erl -name debug@127.0.0.1 -setcookie production_cookie

% Find memory consumers
1> recon:proc_count(memory, 10).

% Find CPU consumers
2> recon:proc_count(reductions, 10).

% Check message queue buildup
3> recon:proc_count(message_queue_len, 10).

% Memory allocator breakdown
4> recon_alloc:memory(used).

% Trace function calls safely
5> recon_trace:calls({module, function, '_'}, 1000, [{time, 30000}]).

VM Configuration for Production

# Disable scheduler busy-wait for lower idle CPU
erl -sbwt none -sname myapp

# Custom scheduler configuration
erl +S 8:8 +A 64 -sname myapp

# Enable JIT profiling for perf integration
erl +JPperf true -name myapp

This technical reference provides the essential operational intelligence for optimizing Gleam applications on BEAM, with emphasis on production-proven techniques and real failure mode prevention.