) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

    /** Trailers to send at the end of the stream. */
    var trailers: Headers? = null

    var closed: Boolean = false

    /**
     * Returns an {@code ImmutableRangeMap} containing the associations previously added to this
     * builder.
     *
     * @throws IllegalArgumentException if any two ranges inserted into this builder overlap
     */
    public ImmutableRangeMap<K, V> build() {
      sort(entries, Range.<K>rangeLexOrdering().onKeys());
      ImmutableList.Builder<Range<K>> rangesBuilder = new ImmutableList.Builder<>(entries.size());

                if (restriction.getLowerBound() == null
                        || restriction.getLowerBound().compareTo(upperBound) < 0) {
                    throw new InvalidVersionSpecificationException("Ranges overlap: " + spec);
                }
            }
            restrictions.add(restriction);
            upperBound = restriction.getUpperBound();

            process = process.substring(index + 1).trim();

### Major Themes

  * <strong>Significant scale improvements</strong>. Increased cluster scale by 400% to 1000 nodes with 30,000 pods per cluster.
Kubelet supports 100 pods per node with 4x reduced system overhead.
  * <strong>Simplified application deployment and management. </strong>
     * Dynamic Configuration (ConfigMap API in the core API group) enables application

   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific
   * hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition
   * to increasing the memory footprint of every thread that interacted with it. In order to release

### Multi destination replication

The replication design for multiple sites works in a similar manner as described above for two site scenario. However there are some
important exceptions.

  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.
  // * We want to behave reasonably for timeouts of 0

     * Wrapper<Number>, Wrapper<Integer>, Wrapper<@Nullable Integer>, etc. If we used just
     * Equivalence<? super T> below, no type could satisfy both that bound and T's own
     * bound. With this type, they have some overlap: in our example, Equivalence<Number>
     * and Equivalence<Object>.
     */
    private final Equivalence<? super @NonNull T> equivalence;

    @ParametricNullness private final T reference;

  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.
  // * We want to behave reasonably for timeouts of 0

    /**
     * The smallest message that will be compressed. We use 1024 because smaller messages already
     * fit comfortably within a single ethernet packet (1500 bytes) even with framing overhead.
     *
     * For tests this must be big enough to realize real compression on test messages like
     * 'aaaaaaaaaa...'. Our tests check if compression was applied just by looking at the size if
     * the inbound buffer.