def convert_check(
self,
input_name,
input_namespace,
output_name,
output_namespace,
computing_engine: ComputingEngine = ComputingEngine.EGGROLL,
output_storage_address={}
) -> (StorageTableMetaABC, AddressABC, StorageEngine):
input_table_meta = StorageTableMeta(name=input_name,
namespace=input_namespace)
if not input_table_meta:
raise RuntimeError(
f"can not found table name: {input_name} namespace: {input_namespace}"
)
address_dict = output_storage_address.copy()
if input_table_meta.get_engine() in [StorageEngine.PATH]:
from fate_arch.storage import PathStorageType
address_dict["name"] = output_name
address_dict["namespace"] = output_namespace
address_dict["storage_type"] = PathStorageType.PICTURE
address_dict["path"] = input_table_meta.get_address().path
output_table_address = StorageTableMeta.create_address(
storage_engine=StorageEngine.PATH, address_dict=address_dict)
output_table_engine = StorageEngine.PATH
elif computing_engine == ComputingEngine.STANDALONE:
from fate_arch.storage import StandaloneStorageType
address_dict["name"] = output_name
address_dict["namespace"] = output_namespace
address_dict["storage_type"] = StandaloneStorageType.ROLLPAIR_LMDB
output_table_address = StorageTableMeta.create_address(
storage_engine=StorageEngine.STANDALONE,
address_dict=address_dict)
output_table_engine = StorageEngine.STANDALONE
elif computing_engine == ComputingEngine.EGGROLL:
from fate_arch.storage import EggRollStorageType
address_dict["name"] = output_name
address_dict["namespace"] = output_namespace
address_dict["storage_type"] = EggRollStorageType.ROLLPAIR_LMDB
output_table_address = StorageTableMeta.create_address(
storage_engine=StorageEngine.EGGROLL,
address_dict=address_dict)
output_table_engine = StorageEngine.EGGROLL
elif computing_engine == ComputingEngine.SPARK:
address_dict["path"] = data_utils.default_output_fs_path(
name=output_name,
namespace=output_namespace,
prefix=address_dict.get("path_prefix"))
output_table_address = StorageTableMeta.create_address(
storage_engine=StorageEngine.HDFS, address_dict=address_dict)
output_table_engine = StorageEngine.HDFS
else:
raise RuntimeError(
f"can not support computing engine {computing_engine}")
return input_table_meta, output_table_address, output_table_engine
def run(self, component_parameters=None, args=None):
self.parameters = component_parameters["ReaderParam"]
output_storage_address = args["job_parameters"].engines_address[
EngineType.STORAGE]
table_key = [key for key in self.parameters.keys()][0]
computing_engine = args["job_parameters"].computing_engine
output_table_namespace, output_table_name = data_utils.default_output_table_info(
task_id=self.tracker.task_id,
task_version=self.tracker.task_version)
input_table_meta, output_table_address, output_table_engine = self.convert_check(
input_name=self.parameters[table_key]['name'],
input_namespace=self.parameters[table_key]['namespace'],
output_name=output_table_name,
output_namespace=output_table_namespace,
computing_engine=computing_engine,
output_storage_address=output_storage_address)
with storage.Session.build(session_id=job_utils.generate_session_id(
self.tracker.task_id,
self.tracker.task_version,
self.tracker.role,
self.tracker.party_id,
suffix="storage",
random_end=True),
storage_engine=input_table_meta.get_engine(
)) as input_table_session:
input_table = input_table_session.get_table(
name=input_table_meta.get_name(),
namespace=input_table_meta.get_namespace())
# update real count to meta info
input_table.count()
# Table replication is required
if input_table_meta.get_engine() != output_table_engine:
LOGGER.info(
f"the {input_table_meta.get_engine()} engine input table needs to be converted to {output_table_engine} engine to support computing engine {computing_engine}"
)
else:
LOGGER.info(
f"the {input_table_meta.get_engine()} input table needs to be transform format"
)
with storage.Session.build(
session_id=job_utils.generate_session_id(
self.tracker.task_id,
self.tracker.task_version,
self.tracker.role,
self.tracker.party_id,
suffix="storage",
random_end=True),
storage_engine=output_table_engine
) as output_table_session:
output_table = output_table_session.create_table(
address=output_table_address,
name=output_table_name,
namespace=output_table_namespace,
partitions=input_table_meta.partitions)
self.copy_table(src_table=input_table, dest_table=output_table)
# update real count to meta info
output_table.count()
output_table_meta = StorageTableMeta(
name=output_table.get_name(),
namespace=output_table.get_namespace())
self.tracker.log_output_data_info(
data_name=component_parameters.get('output_data_name')[0]
if component_parameters.get('output_data_name') else table_key,
table_namespace=output_table_meta.get_namespace(),
table_name=output_table_meta.get_name())
headers_str = output_table_meta.get_schema().get('header')
table_info = {}
if output_table_meta.get_schema() and headers_str:
if isinstance(headers_str, str):
data_list = [headers_str.split(',')]
is_display = True
else:
data_list = [headers_str]
is_display = False
if is_display:
for data in output_table_meta.get_part_of_data():
data_list.append(data[1].split(','))
data = np.array(data_list)
Tdata = data.transpose()
for data in Tdata:
table_info[data[0]] = ','.join(list(set(data[1:]))[:5])
data_info = {
"table_name": self.parameters[table_key]['name'],
"namespace": self.parameters[table_key]['namespace'],
"table_info": table_info,
"partitions": output_table_meta.get_partitions(),
"storage_engine": output_table_meta.get_engine()
}
if input_table_meta.get_engine() in [StorageEngine.PATH]:
data_info["file_count"] = output_table_meta.get_count()
data_info["file_path"] = input_table_meta.get_address().path
else:
data_info["count"] = output_table_meta.get_count()
self.tracker.set_metric_meta(metric_namespace="reader_namespace",
metric_name="reader_name",
metric_meta=MetricMeta(
name='reader',
metric_type='data_info',
extra_metas=data_info))
def _run(self, cpn_input: ComponentInputProtocol):
self.parameters = cpn_input.parameters
self.job_parameters = cpn_input.job_parameters
output_storage_address = self.job_parameters.engines_address[
EngineType.STORAGE]
# only support one input table
table_key = [key for key in self.parameters.keys()][0]
input_table_namespace, input_table_name = self.get_input_table_info(
parameters=self.parameters[table_key],
role=self.tracker.role,
party_id=self.tracker.party_id,
)
(
output_table_namespace,
output_table_name,
) = default_output_info(
task_id=self.tracker.task_id,
task_version=self.tracker.task_version,
output_type="data",
)
(
input_table_meta,
output_table_address,
output_table_engine,
) = self.convert_check(
input_name=input_table_name,
input_namespace=input_table_namespace,
output_name=output_table_name,
output_namespace=output_table_namespace,
computing_engine=self.job_parameters.computing_engine,
output_storage_address=output_storage_address,
)
sess = Session.get_global()
input_table = sess.get_table(
name=input_table_meta.get_name(),
namespace=input_table_meta.get_namespace())
# update real count to meta info
input_table.count()
# Table replication is required
if input_table_meta.get_engine() != output_table_engine:
LOGGER.info(
f"the {input_table_meta.get_engine()} engine input table needs to be converted to {output_table_engine} engine to support computing engine {self.job_parameters.computing_engine}"
)
else:
LOGGER.info(
f"the {input_table_meta.get_engine()} input table needs to be transform format"
)
LOGGER.info("reader create storage session2")
output_table_session = sess.storage(storage_engine=output_table_engine)
output_table = output_table_session.create_table(
address=output_table_address,
name=output_table_name,
namespace=output_table_namespace,
partitions=input_table_meta.partitions,
origin=StorageTableOrigin.READER)
self.save_table(src_table=input_table, dest_table=output_table)
# update real count to meta info
output_table_meta = StorageTableMeta(name=output_table.name,
namespace=output_table.namespace)
# todo: may be set output data, and executor support pass persistent
self.tracker.log_output_data_info(
data_name=cpn_input.flow_feeded_parameters.get("output_data_name")
[0] if cpn_input.flow_feeded_parameters.get("output_data_name")
else table_key,
table_namespace=output_table_meta.get_namespace(),
table_name=output_table_meta.get_name(),
)
DataTableTracker.create_table_tracker(
output_table_meta.get_name(),
output_table_meta.get_namespace(),
entity_info={
"have_parent": True,
"parent_table_namespace": input_table_namespace,
"parent_table_name": input_table_name,
"job_id": self.tracker.job_id,
},
)
headers_str = output_table_meta.get_schema().get("header")
table_info = {}
if output_table_meta.get_schema() and headers_str:
if isinstance(headers_str, str):
data_list = [headers_str.split(",")]
is_display = True
else:
data_list = [headers_str]
is_display = False
if is_display:
for data in output_table_meta.get_part_of_data():
data_list.append(data[1].split(","))
data = np.array(data_list)
Tdata = data.transpose()
for data in Tdata:
table_info[data[0]] = ",".join(list(set(data[1:]))[:5])
data_info = {
"table_name": input_table_name,
"namespace": input_table_namespace,
"table_info": table_info,
"partitions": output_table_meta.get_partitions(),
"storage_engine": output_table_meta.get_engine(),
}
if input_table_meta.get_engine() in [StorageEngine.PATH]:
data_info["file_count"] = output_table_meta.get_count()
data_info["file_path"] = input_table_meta.get_address().path
else:
data_info["count"] = output_table_meta.get_count()
self.tracker.set_metric_meta(
metric_namespace="reader_namespace",
metric_name="reader_name",
metric_meta=MetricMeta(name="reader",
metric_type="data_info",
extra_metas=data_info),
)