def add_columns(self, frame, expression, schema, columns_accessed=None):
if not schema or not hasattr(schema, "__iter__"):
raise ValueError("add_columns requires a non-empty schema of (name, type)")
only_one_column = False
if isinstance(schema[0], basestring):
only_one_column = True
schema = [schema]
schema = self._format_schema(schema)
names, data_types = zip(*schema)
optimized_frame_schema = []
if columns_accessed:
if isinstance(columns_accessed, basestring):
columns_accessed = [columns_accessed]
frame_schema = frame.schema
for i in columns_accessed:
for j in frame_schema:
if i == j[0]:
optimized_frame_schema.append(j)
# By default columns_accessed is an empty list and optimized frame schema is empty which implies frame.schema is considered to evaluate
columns_accessed, optimized_frame_schema = ([], None) if columns_accessed is None else (columns_accessed, optimized_frame_schema)
add_columns_function = get_add_one_column_function(expression, data_types[0]) if only_one_column \
else get_add_many_columns_function(expression, data_types)
from itertools import imap
arguments = {'frame': frame.uri,
'column_names': names,
'column_types': [get_rest_str_from_data_type(t) for t in data_types],
'udf': get_udf_arg(frame, add_columns_function, imap, optimized_frame_schema),
'columns_accessed': columns_accessed}
execute_update_frame_command('add_columns', arguments, frame)
def add_columns(self, frame, expression, schema, columns_accessed=None):
if not schema or not hasattr(schema, "__iter__"):
raise ValueError("add_columns requires a non-empty schema of (name, type)")
only_one_column = False
if isinstance(schema[0], basestring):
only_one_column = True
schema = [schema]
schema = self._format_schema(schema)
names, data_types = zip(*schema)
optimized_frame_schema = []
if columns_accessed:
if isinstance(columns_accessed, basestring):
columns_accessed = [columns_accessed]
frame_schema = frame.schema
for i in columns_accessed:
for j in frame_schema:
if i == j[0]:
optimized_frame_schema.append(j)
# By default columns_accessed is an empty list and optimized frame schema is empty which implies frame.schema is considered to evaluate
columns_accessed, optimized_frame_schema = ([], None) if columns_accessed is None else (columns_accessed, optimized_frame_schema)
add_columns_function = get_add_one_column_function(expression, data_types[0]) if only_one_column \
else get_add_many_columns_function(expression, data_types)
from itertools import imap
arguments = {'frame': frame.uri,
'column_names': names,
'column_types': [get_rest_str_from_data_type(t) for t in data_types],
'udf': get_udf_arg(frame, add_columns_function, imap, optimized_frame_schema),
'columns_accessed': columns_accessed}
execute_update_frame_command('add_columns', arguments, frame)
def aggregate_with_udf(self, frame, group_by_column_keys, aggregator_expression, output_schema, init_acc_values=None):
if not output_schema or not hasattr(output_schema, "__iter__"):
raise ValueError("aggregate_with_udf requires a non-empty schema of (name, type)")
if isinstance(output_schema[0], basestring):
output_schema = [output_schema]
output_schema = self._format_schema(output_schema)
names, data_types = zip(*output_schema)
aggregate_with_udf_function = get_group_by_aggregator_function(aggregator_expression, data_types)
from itertools import imap
arguments = { "frame": frame.uri,
"aggregate_by_column_keys": group_by_column_keys,
"column_names": names,
"column_types": [get_rest_str_from_data_type(t) for t in data_types],
"udf": get_aggregator_udf_arg(frame, aggregate_with_udf_function, imap, output_schema, init_acc_values)
}
return execute_new_frame_command('frame/aggregate_with_udf', arguments)
def from_types_to_strings(s):
return [(name, get_rest_str_from_data_type(data_type))
for name, data_type in s]
def from_types_to_strings(s):
return [(name, get_rest_str_from_data_type(data_type)) for name, data_type in s]
