TypeClashTactic

Allows the choice of how to handle type clashes when reading a JSON file. Such as:

[
    { "a": "text" },
    { "a": { "b": 2 } },
    { "a": [6, 7, 8] }
 ]

ARRAY_AND_VALUE_COLUMNS (default) will create a DataFrame looking like (including null values):

⌌-----------------------------------------------⌍
|  | a:{b:Int?, value:String?, array:List<Int>?}|
|--|--------------------------------------------|
| 0|        { b:null, value:"text", array:null }|
| 1|             { b:2, value:null, array:null }|
| 2|     { b:null, value:null, array:[6, 7, 8] }|
⌎-----------------------------------------------⌏

So, for the type clashing argument it will create a ColumnGroup with the properties value, array, and the unwrapped properties of the objects the property can be.

Each of these columns only holds a value for the records that actually have that shape; for all other records it holds null, which makes all columns nullable:

• value holds the JSON primitive, or null if the record is not a primitive.

• array holds the JSON array as a List, or null if the record is not an array (note that this is different from an empty JSON array [], which is read as an empty list). When the arrays contain objects, array becomes a FrameColumn instead, in which a record without an array is an empty DataFrame, as frame columns cannot hold null.

• the unwrapped object properties hold null if the record is not an object, or if the object does not have that property.

A JSON null record, as well as a missing property, is null in all these columns.

Note that a type clash is not the only source of value/array columns; at the top level they're created for any JSON element that isn't an object. For more information: See the "value" and "array" columns on the documentation website.

ANY_COLUMNS will create a DataFrame looking like:

⌌-------------⌍
|  |     a:Any|
|--|----------|
| 0|    "text"|
| 1|   { b:2 }|
| 2| [6, 7, 8]|
⌎-------------⌏