2CF4..2CF8    ; disallowed                             # NA   <reserved-2CF4>..<reserved-2CF8>
2CF9..2CFF    ; valid                  ;      ; NV8    # 4.1  COPTIC OLD NUBIAN FULL STOP..COPTIC MORPHOLOGICAL DIVIDER
2D00..2D25    ; valid                                  # 4.1  GEORGIAN SMALL LETTER AN..GEORGIAN SMALL LETTER HOE
2D26          ; disallowed                             # NA   <reserved-2D26>

- **MinIO creates erasure-coding sets of _2_ to _16_ drives per set.  The number of drives you provide in total must be a multiple of one of those numbers.**
- **MinIO chooses the largest EC set size which divides into the total number of drives or total number of nodes given - making sure to keep the uniform distribution i.e each node participates equal number of drives per set**.

size is automatic based on the number of drives available, let's say for example if there are 32 servers and 32 drives which is a total of 1024 drives. In this scenario 16 becomes the erasure set size. This is decided based on the greatest common divisor (GCD) of acceptable erasure set sizes ranging from *4 to 16*.

- *If total drives has many common divisors the algorithm chooses the minimum amounts of erasure sets possible for a erasure set size of any N*.  In the example with 1024 drives...

Ainsi, vous pouvez ajouter des données supplémentaires au schéma généré automatiquement.

Par exemple, vous pouvez décider de lire et de valider la requête avec votre propre code, sans utiliser les fonctionnalités automatiques de validation proposée par Pydantic, mais vous pouvez toujours définir la requête dans le schéma OpenAPI.

        input.equals(".%2e", ignoreCase = true) ||
        input.equals("%2e%2e", ignoreCase = true)
    }

    /**
     * Cuts this string up into alternating parameter names and values. This divides a query string
     * like `subject=math&easy&problem=5-2=3` into the list `["subject", "math", "easy", null,
     * "problem", "5-2=3"]`. Note that values may be null and may contain '=' characters.
     */

## How are drives used for Erasure Code?

MinIO divides the drives you provide into erasure-coding sets of *2 to 16* drives.  Therefore, the number of drives you present must be a multiple of one of these numbers.  Each object is written to a single erasure-coding set.

        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.

   The `read_vs_write_current_requests` metric histogram is adjusted, in the case of using API Priority and Fairness instead of max-in-flight, to divide by the relevant limit: sum of queue capacities for waiting requests, sum of seat limits for executing requests. ([#110164](https://github.com/kubernetes/kubernetes/pull/110164), [@MikeSpreitzer](https://github.com/MikeSpreitzer))

    }

    val expectedBucketSize = list.sumOf(toIntFunction) / expectedBucketNumber

    if (expectedBucketSize == 0) {
        // The elements in the list are so small that they can't even be divided into {expectedBucketNumber}.
        // For example, how do you split [0,0,0,0,0] into 3 buckets?
        // In this case, we simply put the elements into these buckets evenly.

Below is a list of data/parity drives and corresponding _approximate_ storage space usage on a 16 drive MinIO deployment. The field _storage
usage ratio_ is simply the drive space used by the file after erasure-encoding, divided by actual file size.

| Total Drives (N) | Data Drives (D) | Parity Drives (P) | Storage Usage Ratio |
|------------------|-----------------|-------------------|---------------------|