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targets.py
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targets.py
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# Copyright 2018 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import typing
from copy import copy
import mlrun
import mlrun.utils.helpers
from mlrun.config import config
from mlrun.model import DataTarget, DataTargetBase
from mlrun.utils import now_date
from mlrun.utils.v3io_clients import get_frames_client
from ..platforms.iguazio import split_path
from .utils import store_path_to_spark
from .v3io import parse_v3io_path
class TargetTypes:
csv = "csv"
parquet = "parquet"
nosql = "nosql"
tsdb = "tsdb"
stream = "stream"
dataframe = "dataframe"
custom = "custom"
@staticmethod
def all():
return [
TargetTypes.csv,
TargetTypes.parquet,
TargetTypes.nosql,
TargetTypes.tsdb,
TargetTypes.stream,
TargetTypes.dataframe,
TargetTypes.custom,
]
def default_target_names():
targets = mlrun.mlconf.feature_store.default_targets
return [target.strip() for target in targets.split(",")]
def get_default_targets():
"""initialize the default feature set targets list"""
return [
DataTargetBase(target, name=str(target)) for target in default_target_names()
]
def validate_target_placement(graph, final_step, targets):
if final_step or graph.is_empty():
return True
for target in targets:
if not target.after_state:
raise mlrun.errors.MLRunInvalidArgumentError(
"writer step location is undetermined due to graph branching"
", set the target .after_state attribute or the graph .final_state"
)
def add_target_states(graph, resource, targets, to_df=False, final_state=None):
"""add the target states to the graph"""
targets = targets or []
key_columns = list(resource.spec.entities.keys())
timestamp_key = resource.spec.timestamp_key
features = resource.spec.features
table = None
for target in targets:
driver = get_target_driver(target, resource)
table = driver.get_table_object() or table
driver.update_resource_status()
driver.add_writer_state(
graph,
target.after_state or final_state,
features=features if not target.after_state else None,
key_columns=key_columns,
timestamp_key=timestamp_key,
)
if to_df:
# add dataframe target, will return a dataframe
driver = DFTarget()
driver.add_writer_state(
graph,
final_state,
features=features,
key_columns=key_columns,
timestamp_key=timestamp_key,
)
return table
offline_lookup_order = [TargetTypes.parquet, TargetTypes.csv]
online_lookup_order = [TargetTypes.nosql]
def get_offline_target(featureset, start_time=None, name=None):
"""return an optimal offline feature set target"""
# todo: take status, start_time and lookup order into account
for target in featureset.status.targets:
driver = kind_to_driver[target.kind]
if driver.is_offline and (not name or name == target.name):
return get_target_driver(target, featureset)
return None
def get_online_target(resource):
"""return an optimal online feature set target"""
# todo: take lookup order into account
for target in resource.status.targets:
driver = kind_to_driver[target.kind]
if driver.is_online:
return get_target_driver(target, resource)
return None
def get_target_driver(target_spec, resource=None):
if isinstance(target_spec, dict):
target_spec = DataTargetBase.from_dict(target_spec)
driver_class = kind_to_driver[target_spec.kind]
return driver_class.from_spec(target_spec, resource)
class BaseStoreTarget(DataTargetBase):
"""base target storage driver, used to materialize feature set/vector data"""
kind = ""
is_table = False
suffix = ""
is_online = False
is_offline = False
support_spark = False
support_storey = False
def __init__(
self,
name: str = "",
path=None,
attributes: typing.Dict[str, str] = None,
after_state=None,
columns=None,
):
self.name = name
self.path = str(path) if path is not None else None
self.after_state = after_state
self.attributes = attributes or {}
self.columns = columns or []
self._target = None
self._resource = None
self._secrets = {}
def _get_store(self):
store, _ = mlrun.store_manager.get_or_create_store(self._target_path)
return store
def _get_column_list(self, features, timestamp_key, key_columns):
column_list = None
if self.columns:
return self.columns
elif features:
column_list = list(features.keys())
if timestamp_key and timestamp_key not in column_list:
column_list = [timestamp_key] + column_list
if key_columns:
for key in reversed(key_columns):
if key not in column_list:
column_list.insert(0, key)
return column_list
def write_dataframe(
self, df, key_column=None, timestamp_key=None, **kwargs,
) -> typing.Optional[int]:
if hasattr(df, "rdd"):
options = self.get_spark_options(key_column, timestamp_key)
options.update(kwargs)
df.write.mode("overwrite").save(**options)
else:
target_path = self._target_path
fs = self._get_store().get_filesystem(False)
if fs.protocol == "file":
dir = os.path.dirname(target_path)
if dir:
os.makedirs(dir, exist_ok=True)
self._write_dataframe(df, fs, target_path, **kwargs)
try:
return fs.size(target_path)
except Exception:
return None
@staticmethod
def _write_dataframe(df, fs, target_path, **kwargs):
raise NotImplementedError()
def set_secrets(self, secrets):
self._secrets = secrets
def set_resource(self, resource):
self._resource = resource
@classmethod
def from_spec(cls, spec: DataTargetBase, resource=None):
"""create target driver from target spec or other target driver"""
driver = cls()
driver.name = spec.name
driver.path = spec.path
driver.attributes = spec.attributes
if hasattr(spec, "columns"):
driver.columns = spec.columns
driver._resource = resource
return driver
def get_table_object(self):
"""get storey Table object"""
return None
@property
def _target_path(self):
"""return the actual/computed target path"""
return self.path or _get_target_path(self, self._resource)
def update_resource_status(self, status="", producer=None, is_dir=None, size=None):
"""update the data target status"""
self._target = self._target or DataTarget(
self.kind, self.name, self._target_path
)
target = self._target
target.is_dir = is_dir
target.status = status or target.status or "created"
target.updated = now_date().isoformat()
target.size = size
target.producer = producer or target.producer
self._resource.status.update_target(target)
return target
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
"""add storey writer state to graph"""
raise NotImplementedError()
def as_df(self, columns=None, df_module=None):
"""return the target data as dataframe"""
return mlrun.get_dataitem(self._target_path).as_df(
columns=columns, df_module=df_module
)
def get_spark_options(self, key_column=None, timestamp_key=None):
# options used in spark.read.load(**options)
raise NotImplementedError()
class ParquetTarget(BaseStoreTarget):
kind = TargetTypes.parquet
suffix = ".parquet"
is_offline = True
support_spark = True
support_storey = True
@staticmethod
def _write_dataframe(df, fs, target_path, **kwargs):
with fs.open(target_path, "wb") as fp:
df.to_parquet(fp, **kwargs)
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
column_list = self._get_column_list(
features=features, timestamp_key=timestamp_key, key_columns=None
)
graph.add_step(
name="WriteToParquet",
after=after,
graph_shape="cylinder",
class_name="storey.WriteToParquet",
path=self._target_path,
columns=column_list,
index_cols=key_columns,
storage_options=self._get_store().get_storage_options(),
**self.attributes,
)
def get_spark_options(self, key_column=None, timestamp_key=None):
return {
"path": store_path_to_spark(self._target_path),
"format": "parquet",
}
class CSVTarget(BaseStoreTarget):
kind = TargetTypes.csv
suffix = ".csv"
is_offline = True
support_spark = True
support_storey = True
@staticmethod
def _write_dataframe(df, fs, target_path, **kwargs):
mode = "wb"
# We generally prefer to open in a binary mode so that different encodings could be used, but pandas had a bug
# with such files until version 1.2.0, in this version they dropped support for python 3.6.
# So only for python 3.6 we're using text mode which might prevent some features
if sys.version_info[0] == 3 and sys.version_info[1] == 6:
mode = "wt"
with fs.open(target_path, mode) as fp:
df.to_csv(fp, **kwargs)
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
column_list = self._get_column_list(
features=features, timestamp_key=timestamp_key, key_columns=key_columns
)
graph.add_step(
name="WriteToCSV",
after=after,
graph_shape="cylinder",
class_name="storey.WriteToCSV",
path=self._target_path,
columns=column_list,
header=True,
index_cols=key_columns,
storage_options=self._get_store().get_storage_options(),
**self.attributes,
)
def get_spark_options(self, key_column=None, timestamp_key=None):
return {
"path": store_path_to_spark(self._target_path),
"format": "csv",
"header": "true",
}
class NoSqlTarget(BaseStoreTarget):
kind = TargetTypes.nosql
is_table = True
is_online = True
support_spark = True
support_storey = True
def get_table_object(self):
from storey import Table, V3ioDriver
# TODO use options/cred
endpoint, uri = parse_v3io_path(self._target_path)
return Table(uri, V3ioDriver(webapi=endpoint))
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
table = self._resource.uri
column_list = self._get_column_list(
features=features, timestamp_key=None, key_columns=key_columns
)
if not self.columns:
aggregate_features = (
[key for key, feature in features.items() if feature.aggregate]
if features
else []
)
column_list = [col for col in column_list if col in aggregate_features]
graph.add_step(
name="WriteToTable",
after=after,
graph_shape="cylinder",
class_name="storey.WriteToTable",
columns=column_list,
table=table,
**self.attributes,
)
def get_spark_options(self, key_column=None, timestamp_key=None):
return {
"path": store_path_to_spark(self._target_path),
"format": "io.iguaz.v3io.spark.sql.kv",
"key": key_column,
}
def as_df(self, columns=None, df_module=None):
raise NotImplementedError()
def write_dataframe(self, df, key_column=None, timestamp_key=None, **kwargs):
if hasattr(df, "rdd"):
options = self.get_spark_options(key_column, timestamp_key)
options.update(kwargs)
df.write.mode("overwrite").save(**options)
else:
access_key = self._secrets.get(
"V3IO_ACCESS_KEY", os.getenv("V3IO_ACCESS_KEY")
)
_, path_with_container = parse_v3io_path(self._target_path)
container, path = split_path(path_with_container)
frames_client = get_frames_client(
token=access_key, address=config.v3io_framesd, container=container
)
frames_client.write("kv", path, df, index_cols=key_column, **kwargs)
class StreamTarget(BaseStoreTarget):
kind = TargetTypes.stream
is_table = False
is_online = False
support_spark = False
support_storey = True
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
from storey import V3ioDriver
endpoint, uri = parse_v3io_path(self._target_path)
column_list = self._get_column_list(
features=features, timestamp_key=timestamp_key, key_columns=key_columns
)
graph.add_step(
name="WriteToStream",
after=after,
graph_shape="cylinder",
class_name="storey.WriteToV3IOStream",
columns=column_list,
storage=V3ioDriver(webapi=endpoint),
stream_path=uri,
**self.attributes,
)
def as_df(self, columns=None, df_module=None):
raise NotImplementedError()
class TSDBTarget(BaseStoreTarget):
kind = TargetTypes.tsdb
is_table = False
is_online = False
support_spark = False
support_storey = True
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
endpoint, uri = parse_v3io_path(self._target_path)
if not timestamp_key:
raise mlrun.errors.MLRunInvalidArgumentError(
"feature set timestamp_key must be specified for TSDBTarget writer"
)
column_list = self._get_column_list(
features=features, timestamp_key=None, key_columns=key_columns
)
graph.add_step(
name="WriteToTSDB",
class_name="storey.WriteToTSDB",
after=after,
graph_shape="cylinder",
path=uri,
time_col=timestamp_key,
index_cols=key_columns,
columns=column_list,
**self.attributes,
)
def as_df(self, columns=None, df_module=None):
raise NotImplementedError()
def write_dataframe(self, df, key_column=None, timestamp_key=None, **kwargs):
access_key = self._secrets.get("V3IO_ACCESS_KEY", os.getenv("V3IO_ACCESS_KEY"))
new_index = []
if timestamp_key:
new_index.append(timestamp_key)
if key_column:
if isinstance(key_column, str):
key_column = [key_column]
new_index.extend(key_column)
_, path_with_container = parse_v3io_path(self._target_path)
container, path = split_path(path_with_container)
frames_client = get_frames_client(
token=access_key, address=config.v3io_framesd, container=container,
)
frames_client.write(
"tsdb", path, df, index_cols=new_index if new_index else None, **kwargs
)
class CustomTarget(BaseStoreTarget):
kind = "custom"
is_table = False
is_online = False
support_spark = False
support_storey = True
def __init__(
self, class_name: str, name: str = "", after_state=None, **attributes,
):
attributes = attributes or {}
attributes["class_name"] = class_name
super().__init__(name, "", attributes, after_state=after_state)
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
attributes = copy(self.attributes)
class_name = attributes.pop("class_name")
graph.add_step(
name=self.name,
after=after,
graph_shape="cylinder",
class_name=class_name,
**attributes,
)
class DFTarget(BaseStoreTarget):
support_storey = True
def __init__(self):
self.name = "dataframe"
self._df = None
def set_df(self, df):
self._df = df
def update_resource_status(self, status="", producer=None, is_dir=None):
pass
def add_writer_state(
self, graph, after, features, key_columns=None, timestamp_key=None
):
# todo: column filter
graph.add_step(
name="WriteToDataFrame",
after=after,
graph_shape="cylinder",
class_name="storey.ReduceToDataFrame",
index=key_columns,
insert_key_column_as=key_columns,
insert_time_column_as=timestamp_key,
)
def as_df(self, columns=None, df_module=None):
return self._df
kind_to_driver = {
TargetTypes.parquet: ParquetTarget,
TargetTypes.csv: CSVTarget,
TargetTypes.nosql: NoSqlTarget,
TargetTypes.dataframe: DFTarget,
TargetTypes.stream: StreamTarget,
TargetTypes.tsdb: TSDBTarget,
TargetTypes.custom: CustomTarget,
}
def _get_target_path(driver, resource):
"""return the default target path given the resource and target kind"""
kind = driver.kind
suffix = driver.suffix
kind_prefix = (
"sets"
if resource.kind == mlrun.api.schemas.ObjectKind.feature_set
else "vectors"
)
name = resource.metadata.name
version = resource.metadata.tag
project = resource.metadata.project or mlrun.mlconf.default_project
data_prefixes = mlrun.mlconf.feature_store.data_prefixes
data_prefix = getattr(data_prefixes, kind, None)
if not data_prefix:
data_prefix = data_prefixes.default
data_prefix = data_prefix.format(project=project, kind=kind)
# todo: handle ver tag changes, may need to copy files?
name = f"{name}-{version or 'latest'}"
return f"{data_prefix}/{kind_prefix}/{name}{suffix}"