def _setup_hudi_read_opts(self, start_timestamp, end_timestamp, read_options):
_hudi_commit_start_time = util.get_hudi_datestr_from_timestamp(start_timestamp)
_hudi_commit_end_time = util.get_hudi_datestr_from_timestamp(end_timestamp)
hudi_options = {
self.HUDI_QUERY_TYPE_OPT_KEY: self.HUDI_QUERY_TYPE_INCREMENTAL_OPT_VAL,
self.HUDI_BEGIN_INSTANTTIME_OPT_KEY: _hudi_commit_start_time,
self.HUDI_END_INSTANTTIME_OPT_KEY: _hudi_commit_end_time,
}
if read_options:
hudi_options.update(read_options)
return hudi_options
def commit_details(self, feature_group, wallclock_time, limit):
if (feature_group._time_travel_format is None
or feature_group._time_travel_format.upper() != "HUDI"):
raise exceptions.FeatureStoreException(
"commit_details can only be used on time travel enabled feature groups"
)
wallclock_timestamp = (
util.get_timestamp_from_date_string(wallclock_time)
if wallclock_time is not None else None)
feature_group_commits = self._feature_group_api.get_commit_details(
feature_group, wallclock_timestamp, limit)
commit_details = {}
for feature_group_commit in feature_group_commits:
commit_details[feature_group_commit.commitid] = {
"committedOn":
util.get_hudi_datestr_from_timestamp(
feature_group_commit.commitid),
"rowsUpdated":
feature_group_commit.rows_updated,
"rowsInserted":
feature_group_commit.rows_inserted,
"rowsDeleted":
feature_group_commit.rows_deleted,
}
return commit_details
def compute_statistics(self,
metadata_instance,
feature_dataframe=None,
feature_group_commit_id=None):
"""Compute statistics for a dataframe and send the result json to Hopsworks."""
if engine.get_type() == "spark":
# If the feature dataframe is None, then trigger a read on the metadata instance
# We do it here to avoid making a useless request when using the Hive engine
# and calling compute_statistics
if feature_dataframe is None:
if feature_group_commit_id is not None:
feature_dataframe = (metadata_instance.select_all().as_of(
util.get_hudi_datestr_from_timestamp(
feature_group_commit_id)).read(
online=False,
dataframe_type="default",
read_options={}))
else:
feature_dataframe = metadata_instance.read()
commit_time = int(
float(datetime.datetime.now().timestamp()) * 1000)
if len(feature_dataframe.head(1)) == 0:
raise exceptions.FeatureStoreException(
"There is no data in the entity that you are trying to compute "
"statistics for. A possible cause might be that you inserted only data "
"to the online storage of a feature group.")
content_str = engine.get_instance().profile(
feature_dataframe,
metadata_instance.statistics_config.columns,
metadata_instance.statistics_config.correlations,
metadata_instance.statistics_config.histograms,
)
stats = statistics.Statistics(commit_time, feature_group_commit_id,
content_str)
self._statistics_api.post(metadata_instance, stats)
return stats
else:
# Hive engine
engine.get_instance().profile(metadata_instance)
def compute_statistics(
self,
metadata_instance,
feature_dataframe=None,
feature_group_commit_id=None,
feature_view_obj=None,
):
"""Compute statistics for a dataframe and send the result json to Hopsworks."""
if engine.get_type() == "spark" or feature_view_obj is not None:
# If the feature dataframe is None, then trigger a read on the metadata instance
# We do it here to avoid making a useless request when using the Python engine
# and calling compute_statistics
if feature_dataframe is None:
if feature_group_commit_id is not None:
feature_dataframe = (metadata_instance.select_all().as_of(
util.get_hudi_datestr_from_timestamp(
feature_group_commit_id)).read(
online=False,
dataframe_type="default",
read_options={}))
else:
feature_dataframe = metadata_instance.read()
commit_time = int(
float(datetime.datetime.now().timestamp()) * 1000)
content_str = self.profile_statistics(metadata_instance,
feature_dataframe)
if content_str:
stats = statistics.Statistics(
commit_time=commit_time,
content=content_str,
feature_group_commit_id=feature_group_commit_id,
)
self._save_statistics(stats, metadata_instance,
feature_view_obj)
else:
# Python engine
engine.get_instance().profile_by_spark(metadata_instance)
