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.
     *

     * #recordMisses}, this method should only be called by the loading thread.
     *
     * @param loadTime the number of nanoseconds the cache spent computing or retrieving the new
     *     value
     */
    @SuppressWarnings("GoodTime") // should accept a java.time.Duration
    void recordLoadSuccess(long loadTime);

    /**

     * #recordMisses}, this method should only be called by the loading thread.
     *
     * @param loadTime the number of nanoseconds the cache spent computing or retrieving the new
     *     value
     */
    @SuppressWarnings("GoodTime") // should accept a java.time.Duration
    void recordLoadSuccess(long loadTime);

    /**

		}
		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