atomic.AddInt64(&lat, latency)
				})
				spent := time.Since(t)
				if spent > 0 && n > 0 {
					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
					b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")
					b.ReportMetric(latency/float64(ops), "ms/op")

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).

			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 {

     */
    private final MavenProject project;

    /**
     * The build time of the project in milliseconds.
     */
    private final long wallTime;

    /**
     * The total amount of time spent for to run mojos in milliseconds.
     */
    private final long execTime;

    /**
     * Creates a new build summary for the specified project.
     *

		}
		atomic.StoreInt64(&dt.timeout, timeout)
	} else if failPct < dynamicTimeoutDecreaseThresholdPct {
		// We are hitting the timeout relatively few times,
		// so decrease the timeout towards 25 % of maximum time spent.
		max = max * 125 / 100

		timeout := atomic.LoadInt64(&dt.timeout)
		if max < time.Duration(timeout) {
			// Move 50% toward the max.
			timeout = (int64(max) + timeout) / 2
		}

    return (totalLoadCount == 0) ? 0.0 : (double) loadExceptionCount / totalLoadCount;
  }

  /**
   * Returns the total number of nanoseconds the cache has spent loading new values. This can be
   * used to calculate the miss penalty. This value is increased every time {@code loadSuccessCount}
   * or {@code loadExceptionCount} is incremented.
   */

    return (totalLoadCount == 0) ? 0.0 : (double) loadExceptionCount / totalLoadCount;
  }

  /**
   * Returns the total number of nanoseconds the cache has spent loading new values. This can be
   * used to calculate the miss penalty. This value is increased every time {@code loadSuccessCount}
   * or {@code loadExceptionCount} is incremented.
   */

        }
        return sb.toString();
      }
    },
    // It really seems like this should be faster than TO_BYTE_ARRAY_NEW_STRING.  But it just isn't
    // my best guess is that the jdk authors have spent more time optimizing that callpath than this
    // one. (StringCoding$StringDecoder vs. StreamDecoder).  StringCoding has a ton of special cases

		dur = 0
	}
	a.Total += int64(dur)
	a.N++
}

// Merge b into a.
func (a *AccElem) merge(b AccElem) {
	a.N += b.N
	a.Total += b.Total
	a.Size += b.Size
}

// Avg returns average time spent.
func (a AccElem) avg() time.Duration {
	if a.N >= 1 && a.Total > 0 {
		return time.Duration(a.Total / a.N)
	}
	return 0
}

// asTimedAction returns the element as a madmin.TimedAction.

=====================

OkHttp 4.x upgrades our implementation language from Java to Kotlin and keeps everything else the
same. We’ve chosen Kotlin because it gives us powerful new capabilities while integrating closely
with Java.

We spent a lot of time and energy on retaining strict compatibility with OkHttp 3.x. We’re even
keeping the package name the same: `okhttp3`!

There are three kinds of compatibility we’re tracking: