Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

## 1. Prerequisites

* Familiarity with [Docker Compose](https://docs.docker.com/compose/overview/).
* Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).

## 2. Run Distributed MinIO on Docker Compose

    steps:
    - name: Checkout repository
      uses: actions/checkout@v5
      # Checkout must run before the caching key is computed using the `hashFiles` method

    - name: Cache Gradle Modules
      uses: actions/cache@v4
      with:
        path: |
          ~/.gradle/caches/modules-2/
          ~/.gradle/caches/build-cache-1/

    /**
     * Fetches more entries from the server.
     *
     * @return true if more entries were fetched, false otherwise
     * @throws CIFSException if an error occurs during fetching
     */
    protected abstract boolean fetchMore() throws CIFSException;

    /**
     * Gets the current batch of results.
     *
     * @return array of file entries in the current batch
     */

st ``` The MinIO deployment starts using default root credentials `minioadmin:minioadmin`. You can test the deployment using the MinIO Console, an embedded web-based object browser built into MinIO Server. Point a web browser running on the host machine to <http://127.0.0.1:9000> and log in with the root credentials. You can use the Browser to create buckets, upload objects, and browse the contents of the MinIO server. You can also connect using any S3-compatible tool, such as the MinIO Client `mc`...

Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

        }

        String p = "\\" + target + "\\" + root;
        if (path != null) {
            p += path;
        }
        try {
            if (log.isDebugEnabled()) {
                log.debug("Fetching referral for " + p);
            }
            final DfsReferralData dr = trans.getDfsReferrals(tf, p, targetHost, targetDomain, 0);
            if (dr != null) {

                if (log.isDebugEnabled()) {

      executeListener(reversedList.runnable, reversedList.executor);
      reversedList = reversedList.next;
    }
  }

  /**
   * Submits the given runnable to the given {@link Executor} catching and logging all {@linkplain
   * RuntimeException runtime exceptions} thrown by the executor.
   */
  @SuppressWarnings("CatchingUnchecked") // sneaky checked exception

    /** Milliseconds between 1970 and 1601 */
    long MILLISECONDS_BETWEEN_1970_AND_1601 = 11644473600000L;
    /** Default timezone */
    TimeZone TZ = TimeZone.getDefault();

    /** Whether to use batching */
    boolean USE_BATCHING = Config.getBoolean("jcifs.smb1.smb.client.useBatching", true);
    /** OEM encoding */
    String OEM_ENCODING = Config.getProperty("jcifs.smb1.encoding", Config.DEFAULT_OEM_ENCODING);

     * authentication.
     */
    int NTLMSSP_NEGOTIATE_OEM_WORKSTATION_SUPPLIED = 0x00002000;

    /**
     * Sent by the server to indicate that the server and client are
     * on the same machine. This implies that the server will include
     * a local security context handle in the Type 2 message, for
     * use in local authentication.
     */
    int NTLMSSP_NEGOTIATE_LOCAL_CALL = 0x00004000;

    /**