`max_sleep` to a *lower* value and setting `max_io` to a *higher* value would make heal go faster.

Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/ heal...

![hdfs-configs](https://github.com/minio/minio/blob/master/docs/bigdata/images/image2.png?raw=true "hdfs advanced configs")

Navigate to **Custom core-site** to configure MinIO parameters for `_s3a_` connector

![s3a-config](https://github.com/minio/minio/blob/master/docs/bigdata/images/image5.png?raw=true "custom core-site")

```
sudo pip install yq
alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
```

#### Configuration Migration

Map Solr configuration to Fess:

| Solr Feature | Fess Equivalent |
|--------------|----------------|
| Solr Core | Fess doesn't use cores; all documents in one index |
| Schema Fields | Define in crawl configuration or use default fields |
| RequestHandlers | Use Fess JSON API with parameters |
| Facet Fields | Configure in Fess search settings |

Por exemplo:

* **Processamento de áudio** ou **imagem**
* **Visão Computacional**: uma imagem é composta por milhões de pixels, cada pixel tem 3 valores / cores, processar isso normalmente exige alguma computação em todos esses pixels ao mesmo tempo

### Multiple Processes - Workers { #multiple-processes-workers }

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.

process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled...

    }

    /**
     * Gets the successful HTTP codes.
     *
     * @return the successful HTTP codes
     */
    public int[] getSuccessfulHttpCodes() {
        return successfulHttpCodes;
    }

    /**
     * Sets the successful HTTP codes.
     *
     * @param successfulHttpCodes the successful HTTP codes to set
     */

  }

  /**
   * Returns a hash code, having the same bit length as each of the input hash codes, that combines
   * the information of these hash codes in an ordered fashion. That is, whenever two equal hash
   * codes are produced by two calls to this method, it is <i>as likely as possible</i> that each
   * was computed from the <i>same</i> input hash codes in the <i>same</i> order.
   *

    @Nested
    @DisplayName("Edge Cases and Boundary Tests")
    class EdgeCasesTests {

        @Test
        @DisplayName("Should handle various result codes correctly")
        void testVariousResultCodes() throws Exception {
            // Test all known result codes
            int[] validCodes = { 0, 1, 2, 3 };
            String[] expectedMessages = { null, // 0 returns null (no exception)

      }
      if (lhs == null) {
        // lhs (null) comes just before justAfterNull.
        // If rhs is b, lhs comes first.
        if (rhs.equals(justAfterNull)) {
          return -1;
        }
        return justAfterNull.compareTo(rhs);
      }
      if (rhs == null) {
        // rhs (null) comes just before justAfterNull.
        // If lhs is b, rhs comes first.
        if (lhs.equals(justAfterNull)) {