* lets all futures share the same listener.
       *
       * We store `localFuturesOrNull` inside the listener because `this.futures` might be nulled
       * out by the time the listener runs for the final future -- at which point we need to check
       * all inputs for exceptions *if* we're collecting values. If we're not, then the listener
       * doesn't need access to the futures again, so we can just pass `null`.

   * to calling it exactly once.
   *
   * @since 10.0 (present in 1.0 as {@code run})
   */
  public void execute() {
    // Lock while we update our state so the add method above will finish adding any listeners
    // before we start to run them.
    RunnableExecutorPair list;
    synchronized (this) {
      if (executed) {
        return;
      }

}()

// Cleans up tmp directory of the local disk.
func bgFormatErasureCleanupTmp(diskPath string) {
	// Need to move temporary objects left behind from previous run of minio
	// server to a unique directory under `minioMetaTmpBucket-old` to clean
	// up `minioMetaTmpBucket` for the current run.
	//
	// /disk1/.minio.sys/tmp-old/
	//  |__ 33a58b40-aecc-4c9f-a22f-ff17bfa33b62
	//  |__ e870a2c1-d09c-450c-a69c-6eaa54a89b3e
	//

   * to calling it exactly once.
   *
   * @since 10.0 (present in 1.0 as {@code run})
   */
  public void execute() {
    // Lock while we update our state so the add method above will finish adding any listeners
    // before we start to run them.
    RunnableExecutorPair list;
    synchronized (this) {
      if (executed) {
        return;
      }

Follow these steps to run the Spark Pi example:

- Login as user **‘spark’**.
- When the job runs, the library can now use **MinIO** during intermediate processing.
- Navigate to a node with the Spark client and access the spark2-client directory:

```
cd /usr/hdp/current/spark2-client
su spark
```

 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in
 * a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still runs
 * quickly, since the row key is provided. However, {@code column(columnKey).size()} takes longer,
 * since an iteration across all row keys occurs.
 *

I'll show you how to **build your own image from scratch** to run a single Uvicorn process. It is a simple process and is probably what you would want to do when using a distributed container management system like **Kubernetes**.

## Recap { #recap }

You can use multiple worker processes with the `--workers` CLI option with the `fastapi` or `uvicorn` commands to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

  }

  /**
   * Returns the value corresponding to the specified row and column indices. The same value is
   * returned by {@code get(rowKeyList().get(rowIndex), columnKeyList().get(columnIndex))}, but this
   * method runs more quickly.
   *
   * @param rowIndex position of the row key in {@link #rowKeyList()}
   * @param columnIndex position of the row key in {@link #columnKeyList()}
   * @return the value with the specified row and column

  }

  /**
   * Registers separate success and failure callbacks to be run when this {@code Future}'s
   * computation is {@linkplain java.util.concurrent.Future#isDone() complete} or, if the
   * computation is already complete, immediately.
   *
   * <p>The callback is run on {@code executor}. There is no guaranteed ordering of execution of

import okhttp3.internal.threadFactory
import okhttp3.internal.unmodifiable

/**
 * Policy on when async requests are executed.
 *
 * Each dispatcher uses an [ExecutorService] to run calls internally. If you supply your own
 * executor, it should be able to run [the configured maximum][maxRequests] number of calls
 * concurrently.
 */
class Dispatcher() {
  /**