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")

			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 {

    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.
   */

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

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:

   * granted. Tells the amount of time slept, if any.
   *
   * <p>This method is equivalent to {@code acquire(1)}.
   *
   * @return time spent sleeping to enforce rate, in seconds; 0.0 if not rate-limited
   * @since 16.0 (present in 13.0 with {@code void} return type})
   */
  @CanIgnoreReturnValue
  public double acquire() {
    return acquire(1);
  }

   * instead.
   *
   * <p><b>Performance note:</b> {@link ArrayList} and {@link java.util.ArrayDeque} consistently
   * outperform {@code LinkedList} except in certain rare and specific situations. Unless you have
   * spent a lot of time benchmarking your specific needs, use one of those instead.
   *
   * <p><b>Note:</b> this method is now unnecessary and should be treated as deprecated. Instead,

   * instead.
   *
   * <p><b>Performance note:</b> {@link ArrayList} and {@link java.util.ArrayDeque} consistently
   * outperform {@code LinkedList} except in certain rare and specific situations. Unless you have
   * spent a lot of time benchmarking your specific needs, use one of those instead.
   *
   * <p><b>Note:</b> this method is now unnecessary and should be treated as deprecated. Instead,

     *
     * <p><b>Performance note:</b> {@link ArrayList} and {@link java.util.ArrayDeque} consistently
     * outperform {@code LinkedList} except in certain rare and specific situations. Unless you have
     * spent a lot of time benchmarking your specific needs, use one of those instead. (However, we
     * do not currently offer a {@link Multimap} implementation based on {@link
     * java.util.ArrayDeque}.)
     */

     *
     * <p><b>Performance note:</b> {@link ArrayList} and {@link java.util.ArrayDeque} consistently
     * outperform {@code LinkedList} except in certain rare and specific situations. Unless you have
     * spent a lot of time benchmarking your specific needs, use one of those instead. (However, we
     * do not currently offer a {@link Multimap} implementation based on {@link
     * java.util.ArrayDeque}.)
     */