Thanks all for feedback.
> 1. when merging NullType with another type, the result should always be
that type.
> 2. when merging StringType with another type, the result should always be
StringType.
> 3. when merging integral types, the priority from high to low:
DecimalType, LongType,
Most of those thoughts from Wenchen make sense to me.
Rather than a list, can we create a table? X-axis is data type, and Y-axis
is also data type, and the intersection explains what the coerced type is?
Can we also look at what Hive, standard SQL (Postgres?) do?
Also, this shouldn't be
My 2 cents:
1. when merging NullType with another type, the result should always be
that type.
2. when merging StringType with another type, the result should always be
StringType.
3. when merging integral types, the priority from high to low: DecimalType,
LongType, IntegerType. This is because
Hi dev,
I would like to post a proposal about partitioned column type inference
(related with 'spark.sql.sources.partitionColumnTypeInference.enabled'
configuration).
This thread focuses on the type coercion (finding the common type) in
partitioned columns, in particular, when the different form
