def _call_fut(self, mapping, schema):
from google.cloud.bigquery.table import _row_from_mapping
return _row_from_mapping(mapping, schema)
async def insert_rows(self, table, rows, selected_fields=None, **kwargs):
"""__Asynchronous__ insertion of rows into a table via the streaming API
Credit:
http://google-cloud-python.readthedocs.io/en/latest/_modules/google/cloud
/bigquery/client.html#Client.insert_rows
https://cloud.google.com/bigquery/docs/reference/rest/v2/
tabledata/insertAll
:type table: One of:
:class:`~google.cloud.bigquery.table.Table`
:class:`~google.cloud.bigquery.table.TableReference`
:param table: the destination table for the row data, or a reference
to it.
:type rows: One of:
list of tuples
list of dictionaries
:param rows: Row data to be inserted. If a list of tuples is given,
each tuple should contain data for each schema field on
the current table and in the same order as the schema
fields. If a list of dictionaries is given, the keys must
include all required fields in the schema. Keys which do
not correspond to a field in the schema are ignored.
:type selected_fields:
list of :class:`~google.cloud.bigquery.schema.SchemaField`
:param selected_fields:
The fields to return. Required if ``table`` is a
:class:`~google.cloud.bigquery.table.TableReference`.
:type kwargs: dict
:param kwargs:
Keyword arguments to
:meth:`~google.cloud.bigquery.client.Client.insert_rows_json`
:rtype: list of mappings
:returns: One mapping per row with insert errors: the "index" key
identifies the row, and the "errors" key contains a list
of the mappings describing one or more problems with the
row.
:raises: ValueError if table's schema is not set
"""
if selected_fields is not None:
schema = selected_fields
elif isinstance(table, TableReference):
raise ValueError('need selected_fields with TableReference')
elif isinstance(table, Table):
if len(table.schema) == 0:
raise ValueError(_TABLE_HAS_NO_SCHEMA)
schema = table.schema
else:
raise TypeError('table should be Table or TableReference')
json_rows = []
for index, row in enumerate(rows):
if isinstance(row, dict):
row = _row_from_mapping(row, schema)
json_row = {}
for field, value in zip(schema, row):
converter = _SCALAR_VALUE_TO_JSON_ROW.get(field.field_type)
if converter is not None: # STRING doesn't need converting
value = converter(value)
json_row[field.name] = value
json_rows.append(json_row)
return await self.insert_rows_json(table, json_rows, **kwargs)
