Extending by a function output

To work around this issue in Kusto (Azure Data Explorer), you can restructure your approach by using a join instead of extend, as extend only works with scalar values. Since your function returns a table, join can be used to align your datatable with the function output for each row.

Here’s a workaround you can try:

1. Use mv-expand to expand the dynamic array of IDs.
2. Apply the function on each expanded value.
3. Re-aggregate if needed to get the original structure with function results.

Here’s an example of how this can be done:

// Sample function that takes dynamic input and returns a table with one row and two columns.
let function = (ids: dynamic)
{
    datatable(col1: string, col2: string)
    [
        tostring(ids[0]), tostring(ids[1])
    ]
};

// Original table with date and dynamic ids
let original_table = datatable(date: datetime, ids: dynamic)
[
    datetime(2022-01-01), dynamic(["value1", "value2"]),
    datetime(2022-01-02), dynamic(["value3", "value4"])
];

// Expand ids, apply function, and reassemble
original_table
| mv-expand id = ids // Expand to create a row for each id
| extend result = function(dynamic([id])) // Apply the function to each id
| project-away id // Remove intermediate column
| join kind=inner (
    original_table
) on date // Join back with the original table by date

This approach:

• Expands the ids dynamic array so each row has a single value from ids.
• Applies the function on each expanded value.
• Joins back with the original table on a common key (date in this case).

Alternatively, if you have control over the function, you could modify it to return a scalar result based on aggregated data. But if it’s essential to use the function as is, then this join method allows you to align it with your original data.

Para obtener un resultado escalado, puedes agregar un paso adicional al final para transformar los resultados tabulares en escalares si es necesario.

Por ejemplo, podrías sumar los valores obtenidos y así obtener un único valor escalar. Aquí te dejo un ejemplo:

```kusto
let function = (ids: dynamic) {
datatable(output1: string, output2: string)
[
tostring(ids[0]), tostring(ids[1])
]
};
datatable(date: datetime, ids: dynamic)
[
datetime(2022-01-01), dynamic(["value1", "value2"]),
datetime(2022-01-02), dynamic(["value3", "value4"])
]
| mv-apply ids on (
extend (output1, output2) = function(ids)
)
| summarize total_outputs = strcat_array(make_list(output1), ", ") by date
```

Este ejemplo utiliza `summarize` para concatenar los valores obtenidos en un solo valor escalar por fecha.

Cuando usas `extend` espera un escalar pero la función devuelve una tabla, puedes utilizar `mv-apply` para trabajar con datos tabulares dentro de la columna dinámica. Aquí hay un enfoque alternativo:

```kusto
let function = (ids: dynamic) {
datatable(output1: string, output2: string)
[
tostring(ids[0]), tostring(ids[1])
]
};
datatable(date: datetime, ids: dynamic)
[
datetime(2022-01-01), dynamic(["value1", "value2"]),
datetime(2022-01-02), dynamic(["value3", "value4"])
]
| mv-apply ids on (
extend (output1, output2) = function(ids)
)
```

Al usar `mv-apply`, puedes aplicar la función a cada elemento de la columna dinámica y extender los resultados a nuevas columnas. Prueba este enfoque y dime si te funciona.