def batch_size(value):
"""The number of results to attempt to retrieve in a batch.
Raises:
BadArgumentError if value is not a integer or is not greater than zero.
"""
datastore_types.ValidateInteger(value, 'batch_size',
datastore_errors.BadArgumentError)
return value
def prefetch_size(value):
"""Number of results to attempt to return on the initial request.
Raises:
BadArgumentError if value is not an integer or is not greater than zero.
"""
datastore_types.ValidateInteger(value,
'prefetch_size',
datastore_errors.BadArgumentError,
zero_ok=True)
return value
def limit(value):
"""Limit on the number of results to return.
Raises:
BadArgumentError if value is not an integer or is less than zero.
"""
datastore_types.ValidateInteger(value,
'limit',
datastore_errors.BadArgumentError,
zero_ok=True)
return value
def offset(value):
"""The number of results to skip before returning the first result.
Only applies to the first request it is used with and is ignored if present
on datastore_rpc.Connection.config.
Raises:
BadArgumentError if value is not a integer or is less than zero.
"""
datastore_types.ValidateInteger(value,
'offset',
datastore_errors.BadArgumentError,
zero_ok=True)
return value
def next_batch(self, min_batch_size):
"""Get the next batch.
The batch returned by this function cannot be used to fetch the next batch
(through Batch.next_batch()). Instead this function will always return None.
To retrieve the next batch use .next() or .next_batch(N).
This function may return a batch larger than min_to_fetch, but will never
return smaller unless there are no more results.
Args:
min_batch_size: The minimum number of results to retrieve.
Returns:
The next Batch of results.
"""
datastore_types.ValidateInteger(min_batch_size,
'min_batch_size',
datastore_errors.BadArgumentError,
zero_ok=True)
if not self.__next_batch:
raise StopIteration
batch = self.__next_batch.get_result()
self.__next_batch = None
self.__skipped_results += batch.skipped_results
needed_results = min_batch_size - len(batch.results)
while (batch.more_results
and (self.__skipped_results < self.__initial_offset
or needed_results > 0)):
if batch.query_options.batch_size:
batch_size = max(batch.query_options.batch_size,
needed_results)
elif needed_results:
batch_size = needed_results
else:
batch_size = None
next_batch = batch.next_batch(
FetchOptions(offset=max(
0, self.__initial_offset - self.__skipped_results),
batch_size=batch_size))
self.__skipped_results += next_batch.skipped_results
needed_results = max(0, needed_results - len(next_batch.results))
batch._extend(next_batch)
self.__next_batch = batch.next_batch_async()
return batch
def advance(self, offset, query, conn):
"""Advances a Cursor by the given offset.
Args:
offset: The amount to advance the current query.
query: A Query identical to the one this cursor was created from.
conn: The datastore_rpc.Connection to use.
Returns:
A new cursor that is advanced by offset using the given query.
"""
datastore_types.ValidateInteger(offset,
'offset',
datastore_errors.BadArgumentError)
if not isinstance(query, Query):
raise datastore_errors.BadArgumentError(
'query argument should be datastore_query.Query (%r)' % (query,))
query_options = QueryOptions(
start_cursor=self, offset=offset, limit=0, produce_cursors=True)
return query.run(conn, query_options).next_batch(0).cursor(0)
