def _TimestampRangeFromTuple(self, ts_tuple):
start, end = ts_tuple
if start is not None:
if start == 0:
start = None
else:
# Convert RDFDatetime to usec
start = float(start)
# Bigtable can only handle ms precision:
# https://github.com/GoogleCloudPlatform/google-cloud-python/issues/2626
# If we give it a filter with usec values it raises RPC error with
# "Timestamp granularity mismatch". Truncate to ms here.
start -= start % 1000
start = self.DatetimeFromMicroseconds(start)
if end is not None:
# Convert RDFDatetime to usec
end = float(end)
# Some searches use 2**64 signed int to signal "no upper limit", there's a
# better way to do that with the API using None.
if end >= (2**64) / 2:
end = None
else:
# Truncate to ms
end -= end % 1000
# GRR expects inclusive timestamps for upper and lower. TimestampRange
# is exclusive on the end. So we add 1ms to the upper bound, which is
# the next smallest timestamp bigtable will accept.
# https://github.com/GoogleCloudPlatform/google-cloud-python/issues/2608
end += 1000
end = self.DatetimeFromMicroseconds(end)
return row_filters.TimestampRange(start=start, end=end)
def test_timestamp_filter_millisecond_granularity(self):
from google.cloud.bigtable import row_filters
end = datetime.datetime.now()
start = end - datetime.timedelta(minutes=60)
timestamp_range = row_filters.TimestampRange(start=start, end=end)
timefilter = row_filters.TimestampRangeFilter(timestamp_range)
row_data = self._table.read_rows(filter_=timefilter)
row_data.consume_all()
def filter_limit_timestamp_range(project_id, instance_id, table_id):
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)
table = instance.table(table_id)
end = datetime.datetime(2019, 5, 1)
rows = table.read_rows(filter_=row_filters.TimestampRangeFilter(
row_filters.TimestampRange(end=end)))
for row in rows:
print_row(row)
