row.new('Resource Usage')
    + row.withPanels([
      panels.timeSeries.bytes('Memory Usage', queries.goMemoryUsage, 'Memory usage of each running instance'),
      panels.timeSeries.allocations('Memory Allocations', queries.goAllocations, 'Details about memory allocations'),
      panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),

// should not result in any allocations.
// This can be used to reset the underlying []byte of an existing Buffer.
func TestNewBufferShallow(t *testing.T) {
	testenv.SkipIfOptimizationOff(t)
	n := testing.AllocsPerRun(1000, func() {
		buf = *NewBuffer(testBytes)
	})
	if n > 0 {
		t.Errorf("allocations occurred while shallow copying")
	}
	check(t, "NewBuffer", &buf, testString)

         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "description": "Details about memory allocations",
         "fieldConfig": {
            "defaults": {
               "custom": {
                  "fillOpacity": 10,
                  "gradientMode": "hue",
                  "showPoints": "never"

      + timeSeries.standardOptions.withUnit('reqps'),

    statPercent(title, targets, desc=''):
      self.stat(title, targets, desc)
      + timeSeries.standardOptions.withUnit('percentunit'),

    allocations(title, targets, desc=''):
      self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('Bps')
      + timeSeries.standardOptions.withOverrides([
        fieldOverride.byQuery.new('B')

     *
     * https://github.com/google/guava/issues/2254
     *
     * Other kinds of Errors are possible:
     *
     * - OutOfMemoryError from allocations in setFuture(): The calculus here is similar to
     * StackOverflowError: We can't reliably call setException(error).
     *
     * - Any kind of Error from a listener. Even if we could distinguish that case (by exposing some

  //   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;
  ExecutorPtr executor_ TF_GUARDED_BY(execution_mutex_);

   * toIndex} exclusive. This is equivalent to {@code
   * Collections.rotate(Chars.asList(array).subList(fromIndex, toIndex), distance)}, but is
   * considerably faster and avoids allocations and garbage collection.
   *
   * <p>The provided "distance" may be negative, which will rotate left.
   *
   * @throws IndexOutOfBoundsException if {@code fromIndex < 0}, {@code toIndex > array.length}, or

   * toIndex} exclusive. This is equivalent to {@code
   * Collections.rotate(Shorts.asList(array).subList(fromIndex, toIndex), distance)}, but is
   * considerably faster and avoids allocations and garbage collection.
   *
   * <p>The provided "distance" may be negative, which will rotate left.
   *
   * @throws IndexOutOfBoundsException if {@code fromIndex < 0}, {@code toIndex > array.length}, or

   * toIndex} exclusive. This is equivalent to {@code
   * Collections.rotate(Shorts.asList(array).subList(fromIndex, toIndex), distance)}, but is
   * considerably faster and avoids allocations and garbage collection.
   *
   * <p>The provided "distance" may be negative, which will rotate left.
   *
   * @throws IndexOutOfBoundsException if {@code fromIndex < 0}, {@code toIndex > array.length}, or

Instead, an Istio specific field like `ExpectedTLSIdentity: spiffe://foo.bar` can encode the same information, at a fraction of the cost.

In our testing, even the most generous representations give custom types a 10x edge (in size, allocations, and CPU time) over Envoy types.
In addition, they are more clear and strictly typed; using Envoy types would require us to put a lot of information in untyped `metadata` maps.