if len(buckets) == 0 {
		return func(bucket string) bool {
			return true
		}
	}
	return func(bucket string) bool {
		return slices.Contains(buckets, bucket)
	}
}

// Details for the measured bandwidth
type Details struct {
	LimitInBytesPerSecond            int64   `json:"limitInBits"`
	CurrentBandwidthInBytesPerSecond float64 `json:"currentBandwidth"`
}

 * @author mbechler
 */
public interface SmbTreeHandleInternal extends SmbTreeHandle {

    /**
     * Releases this tree handle back to the pool for reuse
     */
    void release();

    /**
     * Ensures that DFS referrals have been resolved for this tree
     * @throws SmbException if an SMB-specific error occurs
     * @throws CIFSException if a general CIFS error occurs
     */

Many, many users, but your server is waiting 🕙 for their not-so-good connection to send their requests.

And then waiting 🕙 again for the responses to come back.

This "waiting" 🕙 is measured in microseconds, but still, summing it all, it's a lot of waiting in the end.

That's why it makes a lot of sense to use asynchronous ⏸🔀⏯ code for web APIs.

/**
 * Represents a hierarchical trace of nested requests within a session, enabling correlation between
 * session events and their originating operations in the application code. The trace structure
 * supports the following key features:
 *
 * <ul>
 *   <li>Maintains parent-child relationships between requests to track operation nesting</li>
 *   <li>Carries contextual data describing the current request or operation</li>

// so there is no risk of polluting the entire cache with data accessed once.
// Another goal of DirectIO is to minimize the mutation of data by the kernel
// before issuing IO to underlying devices. ZFS users often enable features like
// compression and checksumming which currently necessitates mutating data in
// the kernel.
//
// DirectIO semantics for a filesystem like ZFS would be quite different than

   * this is subject to change. Also, {@code junit4.TearDownTestCase} will.
   *
   * @throws Exception for any reason. {@code TearDownTestCase} ensures that any exception thrown
   *     will not interfere with other TearDown operations.
   */
  void tearDown() throws Exception;

	"github.com/minio/minio/internal/ioutil"
)

// Checker allows to verify the checksum of a reader.
type Checker struct {
	c io.Closer
	r io.Reader
	h hash.Hash

	want []byte
}

// NewChecker ensures that content with the specified length is read from rc.
// Calling Close on this will close upstream.
func NewChecker(rc io.ReadCloser, h hash.Hash, wantSum []byte, length int64) *Checker {

import com.google.common.collect.testing.NavigableSetTestSuiteBuilder;
import com.google.common.collect.testing.TestStringSetGenerator;
import com.google.common.collect.testing.features.CollectionFeature;
import com.google.common.collect.testing.features.CollectionSize;
import com.google.common.collect.testing.google.MultisetFeature;
import com.google.common.collect.testing.google.SortedMultisetTestSuiteBuilder;

You could put an **arbitrary number** to target, for example, something **between 50% to 90%** of resource utilization. The point is that those are probably the main things you will want to measure and use to tweak your deployments.

name: MinIO advanced tests

on:
  pull_request:
    branches:
      - master

# This ensures that previous jobs for the PR are canceled when the PR is
# updated.
concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref }}
  cancel-in-progress: true

permissions:
  contents: read

jobs:
  replication-test:
    name: Advanced Tests with Go ${{ matrix.go-version }}
    runs-on: ubuntu-latest

    strategy: