type MigrateDecimalColumn struct {
			RecID1 int64 `gorm:"column:recid1;type:decimal(9,0);not null" json:"recid1"`
			RecID2 int64 `gorm:"column:recid2;type:decimal(8);not null" json:"recid2"`
			RecID3 int64 `gorm:"column:recid3;type:decimal(8,1);not null" json:"recid3"`
		}
		type MigrateDecimalColumn2 struct {
			RecID1 int64 `gorm:"column:recid1;type:decimal(8);not null" json:"recid1"`

///

## Crear un middleware { #create-a-middleware }

Para crear un middleware usas el decorador `@app.middleware("http")` encima de una función.

La función middleware recibe:

* La `request`.
* Una función `call_next` que recibirá la `request` como parámetro.
    * Esta función pasará la `request` a la correspondiente *path operation*.

Para eso, **FastAPI** proporciona una función `jsonable_encoder()`.

## Usando el `jsonable_encoder` { #using-the-jsonable-encoder }

Imaginemos que tienes una base de datos `fake_db` que solo recibe datos compatibles con JSON.

Por ejemplo, no recibe objetos `datetime`, ya que no son compatibles con JSON.

          "format": "time_series",
          "instant": false,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{bucket,range}}",
          "refId": "A",
          "step": 300
        }
      ],
      "title": "Object Size Distribution",
      "type": "bargauge"
    },
    {
      "datasource": {
        "type": "prometheus",

          "expr": "sum by (server) (minio_node_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"

          "expr": "sum by (server) (minio_cluster_replication_received_bytes{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Received Data",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },

          "instant": true,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}",
          "metric": "process_start_time_seconds",
          "refId": "A",
          "step": 60
        }
      ],
      "title": "Uptime",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"

import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Use this to meta-annotate {@code XxxFeature.Require} annotations, so that those annotations can
 * be used to decide whether to apply a test to a given class-under-test.
 *
 * <p>This is needed because annotations can't implement interfaces, which is also why reflection is
 * used to extract values from the properties of the various annotations.

import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Use this to meta-annotate {@code XxxFeature.Require} annotations, so that those annotations can
 * be used to decide whether to apply a test to a given class-under-test.
 *
 * <p>This is needed because annotations can't implement interfaces, which is also why reflection is
 * used to extract values from the properties of the various annotations.

          "hide": false,
          "instant": true,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "",
          "metric": "process_start_time_seconds",
          "refId": "A",
          "step": 60
        }
      ],
      "title": "Total Drives",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",