Advanced Features
After building several production WebAssembly apps, I’ve discovered features that aren’t covered in basic tutorials but are essential for sophisticated applications. These techniques often make the difference between a demo and a production-ready system.
The advanced features I’ll share represent solutions to problems that only become apparent when you’re building complex, performance-critical applications.
Advanced Memory Management
WebAssembly gives you more control over memory than typical web applications. I’ve learned to take advantage of this for performance-critical code:
type MemoryPool struct {
buffers chan []byte
size int
}
func NewMemoryPool(poolSize, bufferSize int) *MemoryPool {
pool := &MemoryPool{
buffers: make(chan []byte, poolSize),
size: bufferSize,
}
// Pre-allocate buffers
for i := 0; i < poolSize; i++ {
pool.buffers <- make([]byte, bufferSize)
}
return pool
}
func (mp *MemoryPool) Get() []byte {
select {
case buffer := <-mp.buffers:
return buffer[:0] // Reset length but keep capacity
default:
return make([]byte, 0, mp.size) // Pool empty, create new
}
}
func (mp *MemoryPool) Put(buffer []byte) {
if cap(buffer) != mp.size {
return // Wrong size, don't pool it
}
select {
case mp.buffers <- buffer:
// Successfully returned to pool
default:
// Pool full, let GC handle it
}
}
This memory pooling reduces garbage collection pressure and provides more predictable performance.
Concurrency Patterns
Go’s goroutines work in WebAssembly, but they’re cooperative rather than preemptive. I’ve developed patterns that work well with this model:
type WorkerPool struct {
jobs chan Job
results chan Result
workers int
}
type Job struct {
ID string
Data interface{}
}
type Result struct {
JobID string
Data interface{}
Error error
}
func NewWorkerPool(workers int) *WorkerPool {
wp := &WorkerPool{
jobs: make(chan Job, 100),
results: make(chan Result, 100),
workers: workers,
}
// Start workers
for i := 0; i < workers; i++ {
go wp.worker()
}
return wp
}
func (wp *WorkerPool) worker() {
for job := range wp.jobs {
result := Result{JobID: job.ID}
// Process job
processed, err := processJob(job.Data)
result.Data = processed
result.Error = err
wp.results <- result
}
}
func (wp *WorkerPool) Submit(job Job) {
wp.jobs <- job
}
func (wp *WorkerPool) GetResult() Result {
return <-wp.results
}
This pattern provides controlled concurrency that works well in WebAssembly environments.
Data Processing Pipelines
For applications that process large amounts of data, I use pipeline patterns that maximize throughput:
type Pipeline struct {
stages []Stage
}
type Stage func(<-chan interface{}) <-chan interface{}
func NewPipeline(stages ...Stage) *Pipeline {
return &Pipeline{stages: stages}
}
func (p *Pipeline) Process(input <-chan interface{}) <-chan interface{} {
current := input
for _, stage := range p.stages {
current = stage(current)
}
return current
}
// Example stages
func FilterStage(predicate func(interface{}) bool) Stage {
return func(input <-chan interface{}) <-chan interface{} {
output := make(chan interface{})
go func() {
defer close(output)
for item := range input {
if predicate(item) {
output <- item
}
}
}()
return output
}
}
func TransformStage(transform func(interface{}) interface{}) Stage {
return func(input <-chan interface{}) <-chan interface{} {
output := make(chan interface{})
go func() {
defer close(output)
for item := range input {
output <- transform(item)
}
}()
return output
}
}
This pipeline approach processes data efficiently while keeping the main thread responsive.
Advanced JavaScript Integration
For complex applications, I create sophisticated integration layers that handle type conversion and error propagation automatically:
type APIRegistry struct {
functions map[string]*APIFunction
}
type APIFunction struct {
Handler func([]interface{}) (interface{}, error)
InputTypes []reflect.Type
OutputType reflect.Type
}
func (ar *APIRegistry) Register(name string, fn interface{}) error {
fnType := reflect.TypeOf(fn)
if fnType.Kind() != reflect.Func {
return fmt.Errorf("not a function")
}
// Extract input and output types
var inputTypes []reflect.Type
for i := 0; i < fnType.NumIn(); i++ {
inputTypes = append(inputTypes, fnType.In(i))
}
var outputType reflect.Type
if fnType.NumOut() > 0 {
outputType = fnType.Out(0)
}
// Create wrapper
handler := func(args []interface{}) (interface{}, error) {
return ar.callFunction(fn, args)
}
ar.functions[name] = &APIFunction{
Handler: handler,
InputTypes: inputTypes,
OutputType: outputType,
}
return nil
}
func (ar *APIRegistry) callFunction(fn interface{}, args []interface{}) (interface{}, error) {
fnValue := reflect.ValueOf(fn)
// Convert arguments
var callArgs []reflect.Value
for _, arg := range args {
callArgs = append(callArgs, reflect.ValueOf(arg))
}
// Call function
results := fnValue.Call(callArgs)
if len(results) == 0 {
return nil, nil
}
return results[0].Interface(), nil
}
This registry automatically handles type conversion and provides a clean API for JavaScript integration.
Performance Monitoring
For production applications, I implement comprehensive performance monitoring:
type PerformanceMonitor struct {
metrics map[string]*Metric
}
type Metric struct {
Count int64
Total time.Duration
Min time.Duration
Max time.Duration
Average time.Duration
}
func (pm *PerformanceMonitor) Time(name string, fn func()) {
start := time.Now()
fn()
duration := time.Since(start)
pm.recordMetric(name, duration)
}
func (pm *PerformanceMonitor) recordMetric(name string, duration time.Duration) {
metric, exists := pm.metrics[name]
if !exists {
metric = &Metric{Min: duration, Max: duration}
pm.metrics[name] = metric
}
metric.Count++
metric.Total += duration
metric.Average = metric.Total / time.Duration(metric.Count)
if duration < metric.Min {
metric.Min = duration
}
if duration > metric.Max {
metric.Max = duration
}
}
func (pm *PerformanceMonitor) GetReport() map[string]interface{} {
report := make(map[string]interface{})
for name, metric := range pm.metrics {
report[name] = map[string]interface{}{
"count": metric.Count,
"average": metric.Average.Milliseconds(),
"min": metric.Min.Milliseconds(),
"max": metric.Max.Milliseconds(),
}
}
return report
}
This monitoring system provides detailed performance insights for optimization.
Advanced Error Recovery
Production applications need sophisticated error recovery mechanisms:
type ErrorRecovery struct {
handlers map[reflect.Type]func(error) error
fallback func(error) error
}
func (er *ErrorRecovery) RegisterHandler(errorType reflect.Type, handler func(error) error) {
er.handlers[errorType] = handler
}
func (er *ErrorRecovery) Handle(err error) error {
errorType := reflect.TypeOf(err)
if handler, exists := er.handlers[errorType]; exists {
return handler(err)
}
if er.fallback != nil {
return er.fallback(err)
}
return err
}
func (er *ErrorRecovery) Recover(fn func() error) error {
defer func() {
if r := recover(); r != nil {
var err error
switch v := r.(type) {
case error:
err = v
default:
err = fmt.Errorf("panic: %v", v)
}
err = er.Handle(err)
if err != nil {
panic(err) // Re-panic if not handled
}
}
}()
return fn()
}
This system provides structured error recovery with type-specific handlers.
Module Composition
For large applications, I compose multiple WebAssembly modules that work together:
type ModuleManager struct {
modules map[string]Module
}
type Module interface {
Initialize() error
GetAPI() map[string]interface{}
Cleanup() error
}
func (mm *ModuleManager) LoadModule(name string, module Module) error {
if err := module.Initialize(); err != nil {
return err
}
mm.modules[name] = module
// Expose module API to JavaScript
api := module.GetAPI()
for funcName, fn := range api {
js.Global().Set(fmt.Sprintf("%s_%s", name, funcName), fn)
}
return nil
}
This approach allows you to build modular applications with clear separation of concerns.
These advanced features separate toy examples from production applications. They require more upfront complexity but enable capabilities that would be impossible with simpler approaches.
Real-world applications come next - complete examples that demonstrate how these advanced features work together to solve actual problems.