Table.fields is currently an OrderedDict, but we can't make some operations off of an OrderedDict, like deserializing an entire row. We must create a class Schema, set it to Table.schema and use it internally whenever possible (like in pgimport and all functions that are currently calling load_schema - they should receive a Schema object instead).
I've created a basic implementation to start:
rows.fields:
from .utils import load_schema
class Schema:
@classmethod
def from_file(cls, filename):
obj = cls()
obj.filename = filename
obj.fields = load_schema(
str(filename)
)
return obj
def deserialize(self, row):
field_names = list(row.keys())
field_mapping = {
old: self.fields[new]
for old, new in zip(field_names, make_header(field_names))
}
return {
key: field_mapping[key].deserialize(value) for key, value in row.items()
}
We may need to check for schema serialization formats, such as datapackage, avro, protocol buffers etc. so we can serialize/deserialize these schema definitions to these formats (and maybe also SQL, at least for serialization, using postgresql and sqlite plugins' code).
Table.fieldsis currently anOrderedDict, but we can't make some operations off of anOrderedDict, like deserializing an entire row. We must create a classSchema, set it toTable.schemaand use it internally whenever possible (like inpgimportand all functions that are currently callingload_schema- they should receive aSchemaobject instead).I've created a basic implementation to start:
rows.fields:We may need to check for schema serialization formats, such as datapackage, avro, protocol buffers etc. so we can serialize/deserialize these schema definitions to these formats (and maybe also SQL, at least for serialization, using postgresql and sqlite plugins' code).