final byte[] buf = buffer.array();
            int len;
            int amount = 0;
            while ((len = ChannelUtil.read(channel, buffer, amount)) != -1) {
                out.write(buf, 0, len);
                buffer.clear();
                amount += len;
            }
            out.flush();
            return amount;
        } catch (final IOException e) {
            throw new IORuntimeException(e);

     *
     * @return the offset
     */
    public final int getOffset() {
        return this.offset;
    }

    /**
     * Adjusts the offset by the specified amount.
     *
     * @param n the amount to adjust the offset
     */
    public void adjustOffset(final int n) {
        this.offset += n;
    }

    /**
     * Gets the data length.
     *
     * @return the dataLength

  }

  private fun updateMaxConcurrentStreams(
    connection: Http2Connection,
    amount: Int,
  ) {
    val settings = Settings()
    settings[Settings.MAX_CONCURRENT_STREAMS] = amount
    connection.readerRunnable.applyAndAckSettings(true, settings)
    assertThat(connection.peerSettings[Settings.MAX_CONCURRENT_STREAMS]).isEqualTo(amount)
    taskFaker.runTasks()
  }

        }

        try {
            // In real implementation, this would perform RDMA read into the buffer
            // For skeleton implementation, simulate reading the requested amount
            int bytesToRead = Math.min(length, buffer.remaining());

            // Simulate RDMA read operation

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

### Server Memory { #server-memory }

  @CanIgnoreReturnValue
  public FakeTicker setAutoIncrementStep(long autoIncrementStep, TimeUnit timeUnit) {
    checkArgument(autoIncrementStep >= 0, "May not auto-increment by a negative amount");
    this.autoIncrementStepNanos = timeUnit.toNanos(autoIncrementStep);
    return this;
  }

  /**
   * Sets the increment applied to the ticker whenever it is queried.
   *

    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() {

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...

      return state.policy.backoffDelayMillis.jitterBy(state.policy.backoffJitterMillis) * 1_000_000
    }
  }

  private fun Long.jitterBy(amount: Int): Long = this + ThreadLocalRandom.current().nextInt(amount * -1, amount)

  class AddressState(
    val address: Address,
    val queue: TaskQueue,
    var policy: AddressPolicy,
  ) {
    /**

        this.crawlerRunning = crawlerRunning;
    }

    /**
     * Represents a rule for controlling crawler intervals.
     * Each rule defines a time range, applicable days, and delay amount.
     */
    public static class IntervalRule {
        /** Starting hour of the interval */
        protected int fromHours;

        /** Starting minute of the interval */
        protected int fromMinutes;