}
									// Update to new config and add current
									valid := 0
									for shardIdx, shard := range splitFilled[:k] {
										shardConfig[shardIdx] = shard[offset]
										valid += int(shard[offset])
										if shard[offset] == 0 {
											shards[shardIdx] = shards[shardIdx][:0]
										} else {

    Random r = new Random(9);
    for (int i = 0; i < ITERS; i++) {
      countRemaps(r.nextLong(), map);
    }
    for (int shard = 2; shard <= MAX_SHARDS; shard++) {
      // Rough: don't exceed 1.2x the expected number of remaps by more than 20
      assertTrue(map.get(shard) <= 1.2 * ITERS / shard + 20);
    }
  }

  private void countRemaps(long h, AtomicLongMap<Integer> map) {
    int last = 0;

	StandardEnv = "MINIO_STORAGE_CLASS_STANDARD"
	// Optimize storage class environment variable
	OptimizeEnv = "MINIO_STORAGE_CLASS_OPTIMIZE"
	// Inline block indicates the size of the shard
	// that is considered for inlining, remember this
	// shard value is the value per drive shard it
	// will vary based on the parity that is configured
	// for the STANDARD storage_class.
	// inlining means data and metadata are written

          required: false
          schema:
            type: string
            example: en
        - name: preference
          in: query
          description: String to specify a shard for searching
          required: false
          schema:
            type: string
            example: abc
        - name: callback
          in: query
          description: Callback name for using JSONP

	if len(responses) == 0 {
		return &retval, nil
	}

	for _, response := range responses {
		// Combine all the shards as one, even if that means losing information about
		// the control plane version from each shard.
		retval.ControlPlane = response.ControlPlane
		retval.Resources = append(retval.Resources, response.Resources...)
	}

	return &retval, nil
}

func makeSan(istioNamespace, revision string) string {

	// To avoid these problems we must split the work at scale. With 1000 node
	// setup becoming a reality we must try to shard the work properly such as
	// pick 10 nodes that precisely can send those 100 requests the first node
	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//

page.searchlog.max.fetch.size=10000

page.searchlist.track.total.hits=true

# search page
paging.search.page.start=0
paging.search.page.size=10
paging.search.page.max.size=100

searchlog.agg.shard.size=-1
searchlog.request.headers=
searchlog.process.batch_size=100

thumbnail.html.image.min.width=100
thumbnail.html.image.min.height=100
thumbnail.html.image.max.aspect.ratio=3.0

    /**
     * Disconnect the transport
     * 
     * @param hard
     * @return whether conenction was in use
     * @throws IOException
     */
    public synchronized boolean disconnect ( boolean hard ) throws IOException {
        return disconnect(hard, true);
    }


    /**
     * Disconnect the transport
     * 
     * @param hard
     * @param inUse

   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time

   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time