import com.google.caliper.AfterExperiment;
import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.caliper.api.VmOptions;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import com.google.common.util.concurrent.AbstractFutureBenchmarks.OldAbstractFuture;

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code int[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

import com.google.caliper.AfterExperiment;
import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.caliper.api.VmOptions;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import com.google.common.util.concurrent.AbstractFutureBenchmarks.OldAbstractFuture;

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code double[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

the value in the form of a `Handle[T]`.
Two `Handle[T]` are equal if and only if the values used to produce the
handles are equal, allowing programs to deduplicate values and reduce
their memory footprint.
Comparing two `Handle[T]` values is efficient, reducing down to a simple

 * lock, but also maximizes memory footprint. On the other extreme, one could use a single lock for
 * all tasks, which minimizes memory footprint but also minimizes concurrency. Instead of choosing
 * either of these extremes, {@code Striped} allows the user to trade between required concurrency
 * and memory footprint. For example, if a set of tasks are CPU-bound, one could easily create a

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code long[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

 * traditional {@code LinkedHashMap} implementation which stores keys and count values as map
 * entries, {@code ObjectCountLinkedHashMap} minimizes object allocation and reduces memory
 * footprint.
 */
@GwtCompatible(serializable = true, emulated = true)
@ElementTypesAreNonnullByDefault
class ObjectCountLinkedHashMap<K extends @Nullable Object> extends ObjectCountHashMap<K> {

Ztunnel is not general purpose; it has an extremely tight goal.
We can exploit this to make a more efficient protocol, which is critical to achieve our resource footprint goals.

For example, configuring Istio mTLS in Envoy takes roughly 50 lines of JSON (it is in Protobuf, of course, but still relevant).

 * a traditional {@code HashMap} implementation which stores keys and count values as map entries,
 * {@code ObjectCountHashMap} minimizes object allocation and reduces memory footprint.
 *
 * <p>In the absence of element deletions, this will iterate over elements in insertion order.
 */
@GwtCompatible(serializable = true, emulated = true)
@ElementTypesAreNonnullByDefault