MockResponse.Builder()
        .socketPolicy(DisconnectDuringRequestBody)
        .build(),
    )
    // Limit the size of the request body that the server holds in memory to an arbitrary
    // 3.5 MBytes so this test can pass on devices with little memory.
    server.bodyLimit = 7 * 512 * 1024
    val connection = server.url("/").toUrl().openConnection() as HttpURLConnection
    connection.requestMethod = "POST"

		return err
	}

	// Goroutine to periodically save batch-expire job's in-memory state
	saverQuitCh := make(chan struct{})
	go func() {
		saveTicker := time.NewTicker(10 * time.Second)
		defer saveTicker.Stop()
		for {
			select {
			case <-saveTicker.C:
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))

			case <-ctx.Done():

      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes
      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi

#### Partial Computations

Along with caching, partial computations are a critical performance optimization to ensure that we do not need to build (or send) every resource to every proxy on every change. This is discussed more in the Config Serving section.

### Config Serving

All Go pointers passed to C must point to pinned Go memory. Go pointers
passed as function arguments to C functions have the memory they point to
implicitly pinned for the duration of the call. Go memory reachable from
these function arguments must be pinned as long as the C code has access
to it. Whether Go memory is pinned is a dynamic property of that memory
region; it has nothing to do with the type of the pointer.

      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes

      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi

   *
   * <p><i>Performance notes:</i> while the cartesian product of lists of size {@code m, n, p} is a
   * list of size {@code m x n x p}, its actual memory consumption is much smaller. When the
   * cartesian product is constructed, the input lists are merely copied. Only as the resulting list
   * is iterated are the individual lists created, and these are not retained after iteration.
   *

 * <p>This class should not be assumed to be universally superior to {@code java.util.HashSet}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

 * <p>This class should not be assumed to be universally superior to {@code java.util.HashMap}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

				} else {
					stopFn(result, nil)
				}
				ri.trackCurrentBucketObject(r.Bucket, result, success)
				ri.RetryAttempts = attempts
				globalBatchJobsMetrics.save(job.ID, ri)
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))
				if success {
					break
				}
				if delay > 0 {
					time.Sleep(delay + time.Duration(rnd.Float64()*float64(delay)))