you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

And then you can set those same memory limits and requirements in your configurations for your container management system (for example in **Kubernetes**). That way it will be able to **replicate the containers** in the **available machines** taking into account the amount of memory needed by them, and the amount available in the machines...

    /**
     * Gets the current state of the named pipe
     * @return the namedPipeState
     */
    public int getNamedPipeState() {
        return this.namedPipeState;
    }

    /**
     * Gets the amount of data available to read from the pipe
     * @return the readDataAvailable
     */
    public int getReadDataAvailable() {
        return this.readDataAvailable;
    }

    /**

     */
    private final MavenProject project;

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

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

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

		tok := p.next()
		ext = tok.String()
	}
	if p.peek() == lex.LSH {
		// parses left shift amount applied after extension: <<Amount
		p.get(lex.LSH)
		tok := p.get(scanner.Int)
		amount, err := strconv.ParseInt(tok.String(), 10, 16)
		if err != nil {
			p.errorf("parsing left shift amount: %s", err)
		}
		num = int16(amount)
	} else if p.peek() == '[' {
		// parses an element: [Index]
		p.get('[')

	  token: "Bearer xxxxx" # optional authentication token for the notification endpoint

	retry:
	  attempts: 10 # number of retries for the job before giving up
	  delay: "500ms" # least amount of delay between each retry
```

You can create and run multiple 'replication' jobs at a time there are no predefined limits set.

## Batch Jobs Terminology

### Job

  }

  /**
   * Sends the given method call to this thread.
   *
   * @throws TimeoutException if this thread does not accept the request within a reasonable amount
   *     of time
   */
  private void sendRequest(String methodName, Object... arguments) throws Exception {
    if (!requestQueue.offer(new Request(methodName, arguments), TIMEOUT_MILLIS, MILLISECONDS)) {

    list.add(new MockRunnable(countDownLatch), exec);
    assertEquals(3L, countDownLatch.getCount());

    list.execute();

    // Verify that all of the runnables execute in a reasonable amount of time.
    assertTrue(countDownLatch.await(1L, SECONDS));
  }

  public void testExecute_idempotent() {
    AtomicInteger runCalled = new AtomicInteger();
    list.add(
        new Runnable() {

   * "expected arrival time of the next request" is actually in the past, then the difference (now -
   * past) is the amount of time that the RateLimiter was formally unused, and it is that amount of
   * time which we translate to storedPermits. (We increase storedPermits with the amount of permits
   * that would have been produced in that idle time). So, if rate == 1 permit per second, and

  }

  /**
   * Sends the given method call to this thread.
   *
   * @throws TimeoutException if this thread does not accept the request within a reasonable amount
   *     of time
   */
  private void sendRequest(String methodName, Object... arguments) throws Exception {
    if (!requestQueue.offer(new Request(methodName, arguments), TIMEOUT_MILLIS, MILLISECONDS)) {

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all...