* Cookies.
    * Headers.
    * Form.
    * File.
* <abbr title="detta anche: serialization, parsing, marshalling">Conversione</abbr> dei dati di output: converte dati e tipi di Python a dati per la rete (come JSON):
    * Converte i tipi di Python (`str`, `int`, `float`, `bool`, `list`, ecc).
    * Oggetti `datetime`.
    * Oggetti `UUID`.
    * Modelli del database.
    * ...e molto di più.

	return
}

// Get upload latency of each object size range
func (rl ReplicationLatency) getUploadLatency() (ret map[string]uint64) {
	ret = make(map[string]uint64)
	avg := rl.UploadHistogram.GetAvgData()
	for k, v := range avg {
		// Convert nanoseconds to milliseconds
		ret[sizeTagToString(k)] = uint64(v.avg() / time.Millisecond)
	}
	return
}

// Update replication upload latency with a new value

 * <p>Rate limiters are often used to restrict the rate at which some physical or logical resource
 * is accessed. This is in contrast to {@link java.util.concurrent.Semaphore} which restricts the
 * number of concurrent accesses instead of the rate (note though that concurrency and rate are
 * closely related, e.g. see <a href="http://en.wikipedia.org/wiki/Little%27s_law">Little's
 * Law</a>).
 *

 * <p>Rate limiters are often used to restrict the rate at which some physical or logical resource
 * is accessed. This is in contrast to {@link java.util.concurrent.Semaphore} which restricts the
 * number of concurrent accesses instead of the rate (note though that concurrency and rate are
 * closely related, e.g. see <a href="http://en.wikipedia.org/wiki/Little%27s_law">Little's
 * Law</a>).
 *

   * we change the rate.
   */
  public void testWeNeverGetABurstMoreThanOneSec() {
    RateLimiter limiter = RateLimiter.create(1.0, stopwatch);
    int[] rates = {1000, 1, 10, 1000000, 10, 1};
    for (int rate : rates) {
      int oneSecWorthOfWork = rate;
      stopwatch.sleepMillis(rate * 1000);
      limiter.setRate(rate);

   * we change the rate.
   */
  public void testWeNeverGetABurstMoreThanOneSec() {
    RateLimiter limiter = RateLimiter.create(1.0, stopwatch);
    int[] rates = {1000, 1, 10, 1000000, 10, 1};
    for (int rate : rates) {
      int oneSecWorthOfWork = rate;
      stopwatch.sleepMillis(rate * 1000);
      limiter.setRate(rate);

  }
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(options, ret));
  return ret;
}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));

            DfsState state = stateMap.putIfAbsent(v, DfsState.VISITING);
            if (state == null) {
                cycle.addLast(v.label);
                List<String> ret = visitCycle(v.children, stateMap, cycle);
                if (ret != null) {
                    return ret;
                }
                cycle.removeLast();
                stateMap.put(v, DfsState.VISITED);
            } else if (state == DfsState.VISITING) {

sleep 30s ## sleep for 30s idea is that we give 300ms per object.

./mc ls -r --versions sitea/bucket >/tmp/sitea.txt
./mc ls -r --versions siteb/bucket >/tmp/siteb.txt

out=$(diff -qpruN /tmp/sitea.txt /tmp/siteb.txt)
ret=$?
if [ $ret -ne 0 ]; then
	echo "BUG: expected no missing entries after replication: $out"
	exit 1
fi

./mc cp /tmp/data/file_1.txt sitea/bucket/marker_new
./mc rm sitea/bucket/marker_new

<img src="/img/tutorial/body/image03.png">

Et vous obtenez aussi de la vérification d'erreur pour les opérations incorrectes de types :

<img src="/img/tutorial/body/image04.png">

Ce n'est pas un hasard, ce framework entier a été bâti avec ce design comme objectif.