Also, the best approach was to use already existing standards.

So, before even starting to code **FastAPI**, I spent several months studying the specs for OpenAPI, JSON Schema, OAuth2, etc. Understanding their relationship, overlap, and differences.

## Design

Then I spent some time designing the developer "API" I wanted to have as a user (as a developer using FastAPI).

// IO wait, currently only network blocking event).
func computePprofIO() computePprofFunc {
	return makeComputePprofFunc(trace.GoWaiting, func(reason string) bool {
		return reason == "network"
	})
}

// computePprofBlock returns a computePprofFunc that generates blocking pprof-like profile
// (time spent blocked on synchronization primitives).

	for {
		if mp.count > 0 {
			mp.count--
			pthread_mutex_unlock(&mp.mutex)
			return 0
		}
		if ns >= 0 {
			spent := nanotime() - start
			if spent >= ns {
				pthread_mutex_unlock(&mp.mutex)
				return -1
			}
			var t timespec
			t.setNsec(ns - spent)
			err := pthread_cond_timedwait_relative_np(&mp.cond, &mp.mutex, &t)
			if err == _ETIMEDOUT {
				pthread_mutex_unlock(&mp.mutex)
				return -1

}

func init() {
	legacyregistry.MustRegister(Metrics.(*validationMetrics).RatchetingTime)
}

type validationMetrics struct {
	RatchetingTime *metrics.Histogram
}

// ObserveRatchetingTime records the time spent on ratcheting
func (m *validationMetrics) ObserveRatchetingTime(d time.Duration) {
	m.RatchetingTime.Observe(d.Seconds())
}

// Reset resets the metrics. This is meant to be used for testing. Panics

			Namespace:      namespace,
			Subsystem:      subsystem,
			Name:           "jwt_authenticator_latency_seconds",
			Help:           "Latency of jwt authentication operations in seconds. This is the time spent authenticating a token for cache miss only (i.e. when the token is not found in the cache).",
			StabilityLevel: metrics.ALPHA,
			// default histogram buckets with a 1ms starting point

// utilization without hurting throughput.
//
// Note that the bucket in the leaky bucket mechanism can never go negative,
// so the GC never gets credit for a lot of CPU time spent without the GC
// running. This is intentional, as an application that stays idle for, say,
// an entire day, could build up enough credit to fail to prevent a death

	}
	return d2
}

// DurationTracker is a simple interface for tracking functions duration,
// it is safe for concurrent use by multiple goroutines.
type DurationTracker interface {
	// Track measures time spent in the given function f and
	// aggregates measured duration using aggregateFunction.
	// if Track is invoked with f from multiple goroutines concurrently,
	// then f must be safe to be invoked concurrently by multiple goroutines.

	ScavengeTotalTime  int64

	IdleTime int64 // Time Ps spent in _Pidle.
	UserTime int64 // Time Ps spent in _Prunning or _Psyscall that's not any of the above.

	TotalTime int64 // GOMAXPROCS * (monotonic wall clock time elapsed)
}

// accumulateGCPauseTime add dt*stwProcs to the GC CPU pause time stats. dt should be
// the actual time spent paused, for orthogonality. maxProcs should be GOMAXPROCS,

    // Number of times the cluster has been (re-)compiled.
    int64_t compile_count = 0;

    // The number of times this cluster has been executed.
    int64_t execution_count = 0;

    // Cumulative time spent compiling the cluster.
    int64_t cumulative_compile_time_us = 0;

    // True if we have decided that this cluster is too dynamic (i.e. its shapes
    // change too frequently) to profitably JIT compile.  Once a cluster is

			select {
			case <-dctx.Done():
				if !timeout.Stop() {
					<-timeout.C
				}
			case <-timeout.C:
				spent := time.Since(started)
				goOffline(fmt.Errorf("unable to write+read for %v", spent.Round(time.Millisecond)), spent)
			}
		}()

		func() {
			defer dcancel()

			err := p.storage.WriteAll(ctx, minioMetaTmpBucket, fn, toWrite)
			if err != nil {