Source code for mlrun.datastore.targets

# 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 random
import time
import typing
import warnings
from collections import Counter
from copy import copy

import pandas as pd

import mlrun
import mlrun.utils.helpers
from mlrun.config import config
from mlrun.model import DataTarget, DataTargetBase, TargetPathObject
from mlrun.utils import now_date
from mlrun.utils.v3io_clients import get_frames_client

from .. import errors
from ..data_types import ValueType
from ..platforms.iguazio import parse_v3io_path, split_path
from .utils import store_path_to_spark


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 generate_target_run_id():
    return f"{round(time.time() * 1000)}_{random.randint(0, 999)}"


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 update_targets_run_id_for_ingest(overwrite, targets, targets_in_status):
    run_id = generate_target_run_id()
    for target in targets:
        if overwrite or not (target.name in targets_in_status.keys()):
            target.run_id = run_id
        else:
            target.run_id = targets_in_status[target.name].run_id


def get_default_prefix_for_target(kind):
    data_prefixes = mlrun.mlconf.feature_store.data_prefixes
    data_prefix = getattr(data_prefixes, kind, None)
    if not data_prefix:
        data_prefix = data_prefixes.default
    return data_prefix


def get_default_prefix_for_source(kind):
    return get_default_prefix_for_target(kind)


def validate_target_list(targets):
    """Check that no target overrides another target in the list (name/path)"""

    if not targets:
        return
    targets_by_kind_name = [kind for kind in targets if type(kind) is str]
    no_name_target_types_count = Counter(
        [
            target.kind
            for target in targets
            if hasattr(target, "name") and hasattr(target, "kind") and not target.name
        ]
        + targets_by_kind_name
    )
    target_types_requiring_name = [
        target_type
        for target_type, target_type_count in no_name_target_types_count.items()
        if target_type_count > 1
    ]
    if target_types_requiring_name:
        raise mlrun.errors.MLRunInvalidArgumentError(
            "Only one default name per target type is allowed (please specify name for {0} target)".format(
                target_types_requiring_name
            )
        )

    target_names_count = Counter(
        [target.name for target in targets if hasattr(target, "name") and target.name]
    )

    targets_with_same_name = [
        target_name
        for target_name, target_name_count in target_names_count.items()
        if target_name_count > 1
    ]

    if targets_with_same_name:
        raise mlrun.errors.MLRunInvalidArgumentError(
            "Each target must have a unique name (more than one target with those names found {0})".format(
                targets_with_same_name
            )
        )

    no_path_target_types_count = Counter(
        [
            target.kind
            for target in targets
            if hasattr(target, "path") and hasattr(target, "kind") and not target.path
        ]
        + targets_by_kind_name
    )
    target_types_requiring_path = [
        target_type
        for target_type, target_type_count in no_path_target_types_count.items()
        if target_type_count > 1
    ]
    if target_types_requiring_path:
        raise mlrun.errors.MLRunInvalidArgumentError(
            "Only one default path per target type is allowed (please specify path for {0} target)".format(
                target_types_requiring_path
            )
        )

    target_paths_count = Counter(
        [target.path for target in targets if hasattr(target, "path") and target.path]
    )

    targets_with_same_path = [
        target_path
        for target_path, target_path_count in target_paths_count.items()
        if target_path_count > 1
    ]

    if targets_with_same_path:
        raise mlrun.errors.MLRunInvalidArgumentError(
            "Each target must have a unique path (more than one target with those names found {0})".format(
                targets_with_same_path
            )
        )


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_step:
            raise mlrun.errors.MLRunInvalidArgumentError(
                "writer step location is undetermined due to graph branching"
                ", set the target .after_step attribute or the graph .final_step"
            )


def add_target_states(graph, resource, targets, to_df=False, final_state=None):
    warnings.warn(
        "This method is deprecated. Use add_target_steps instead",
        # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove
        PendingDeprecationWarning,
    )
    return add_target_steps(graph, resource, targets, to_df, final_state)


def add_target_steps(graph, resource, targets, to_df=False, final_step=None):
    """add the target steps to the graph"""
    targets = targets or []
    key_columns = resource.spec.entities
    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_step(
            graph,
            target.after_step or final_step,
            features=features if not target.after_step else None,
            key_columns=key_columns,
            timestamp_key=timestamp_key,
            featureset_status=resource.status,
        )
    if to_df:
        # add dataframe target, will return a dataframe
        driver = DFTarget()

        driver.add_writer_step(
            graph,
            final_step,
            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, name=None):
    """return an optimal offline feature set target"""
    # todo: take status, start_time and lookup order into account
    offline_targets = [
        target
        for target in featureset.status.targets
        if kind_to_driver[target.kind].is_offline
    ]
    target = None
    if name:
        target = next((t for t in offline_targets if t.name == name), None)
    else:
        for kind in offline_lookup_order:
            target = next((t for t in offline_targets if t.kind == kind), None)
            if target:
                break
        if target is None and offline_targets:
            target = offline_targets[0]

    if target:
        return get_target_driver(target, featureset)
    return None


def get_online_target(resource, name=None):
    """return an optimal online feature set target"""
    # todo: take lookup order into account
    for target in resource.status.targets:
        if name and target.name != name:
            continue
        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
    support_append = False

    def __init__(
        self,
        name: str = "",
        path=None,
        attributes: typing.Dict[str, str] = None,
        after_step=None,
        columns=None,
        partitioned: bool = False,
        key_bucketing_number: typing.Optional[int] = None,
        partition_cols: typing.Optional[typing.List[str]] = None,
        time_partitioning_granularity: typing.Optional[str] = None,
        after_state=None,
        max_events: typing.Optional[int] = None,
        flush_after_seconds: typing.Optional[int] = None,
        storage_options: typing.Dict[str, str] = None,
    ):
        super().__init__(
            self.kind,
            name,
            path,
            attributes,
            after_step,
            partitioned,
            key_bucketing_number,
            partition_cols,
            time_partitioning_granularity,
            max_events,
            flush_after_seconds,
            after_state,
        )
        if after_state:
            warnings.warn(
                "The after_state parameter is deprecated. Use after_step instead",
                # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove
                PendingDeprecationWarning,
            )
            after_step = after_step or after_state

        self.name = name or self.kind
        self.path = str(path) if path is not None else None
        self.after_step = after_step
        self.attributes = attributes or {}
        self.columns = columns or []
        self.partitioned = partitioned
        self.key_bucketing_number = key_bucketing_number
        self.partition_cols = partition_cols
        self.time_partitioning_granularity = time_partitioning_granularity
        self.max_events = max_events
        self.flush_after_seconds = flush_after_seconds
        self.storage_options = storage_options

        self._target = None
        self._resource = None
        self._secrets = {}

    def _get_store(self):
        store, _ = mlrun.store_manager.get_or_create_store(self.get_target_path())
        return store

    def _get_column_list(self, features, timestamp_key, key_columns, with_type=False):
        result = []
        if self.columns:
            if with_type:
                columns = set(self.columns)
                for feature in features:
                    if feature.name in columns:
                        result.append((feature.name, feature.value_type))
            else:
                result = self.columns
        elif features:
            if with_type:
                for feature in features:
                    result.append((feature.name, feature.value_type))
            else:
                result = list(features.keys())
            if key_columns:
                for key in reversed(key_columns):
                    if key not in result:
                        if with_type:
                            result.insert(0, (key, ValueType.STRING))
                        else:
                            result.insert(0, key)

        if timestamp_key:
            if with_type:
                result = [(timestamp_key, ValueType.DATETIME)] + result
            else:
                result = [timestamp_key] + result

        return result

    def write_dataframe(
        self,
        df,
        key_column=None,
        timestamp_key=None,
        chunk_id=0,
        **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)
        elif hasattr(df, "dask"):
            dask_options = self.get_dask_options()
            storage_options = self._get_store().get_storage_options()
            df = df.repartition(partition_size="100MB")
            try:
                if dask_options["format"] == "parquet":
                    df.to_parquet(
                        generate_path_with_chunk(self, chunk_id),
                        storage_options=storage_options,
                    )
                elif dask_options["format"] == "csv":
                    df.to_csv(
                        generate_path_with_chunk(self, chunk_id),
                        storage_options=storage_options,
                    )
                else:
                    raise NotImplementedError(
                        "Format for writing dask dataframe should be CSV or Parquet!"
                    )
            except Exception as exc:
                raise RuntimeError(f"Failed to write Dask Dataframe for {exc}.")
        else:
            target_path = generate_path_with_chunk(self, chunk_id)
            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)
            partition_cols = []
            target_df = df
            if timestamp_key and (
                self.partitioned or self.time_partitioning_granularity
            ):
                target_df = df.copy(deep=False)
                time_partitioning_granularity = self.time_partitioning_granularity
                if not time_partitioning_granularity and self.partitioned:
                    time_partitioning_granularity = "hour"
                for unit, fmt in [
                    ("year", "%Y"),
                    ("month", "%m"),
                    ("day", "%d"),
                    ("hour", "%H"),
                    ("minute", "%M"),
                ]:
                    partition_cols.append(unit)
                    target_df[unit] = getattr(
                        pd.DatetimeIndex(target_df[timestamp_key]), unit
                    )
                    if unit == time_partitioning_granularity:
                        break
            self._write_dataframe(
                target_df, fs, target_path, partition_cols=partition_cols, **kwargs
            )
            try:
                return fs.size(target_path)
            except Exception:
                return None

    @staticmethod
    def _write_dataframe(df, fs, target_path, partition_cols, **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.partitioned = spec.partitioned

        driver.key_bucketing_number = spec.key_bucketing_number
        driver.partition_cols = spec.partition_cols

        driver.time_partitioning_granularity = spec.time_partitioning_granularity
        driver.max_events = spec.max_events
        driver.flush_after_seconds = spec.flush_after_seconds
        driver.storage_options = spec.storage_options

        driver._resource = resource
        driver.run_id = spec.run_id
        return driver

    def get_table_object(self):
        """get storey Table object"""
        return None

    def get_target_path(self):
        path_object = self._target_path_object
        return path_object.get_absolute_path() if path_object else None

    def get_target_templated_path(self):
        path_object = self._target_path_object
        return path_object.get_templated_path() if path_object else None

    @property
    def _target_path_object(self):
        """return the actual/computed target path"""
        is_single_file = hasattr(self, "is_single_file") and self.is_single_file()
        return self.get_path() or (
            TargetPathObject(
                _get_target_path(self, self._resource, self.run_id is not None),
                self.run_id,
                is_single_file,
            )
            if self._resource
            else None
        )

    def update_resource_status(self, status="", producer=None, size=None):
        """update the data target status"""
        self._target = self._target or DataTarget(
            self.kind, self.name, self.get_target_templated_path()
        )
        target = self._target
        target.run_id = self.run_id
        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_step(
        self,
        graph,
        after,
        features,
        key_columns=None,
        timestamp_key=None,
        featureset_status=None,
    ):
        raise NotImplementedError()

    def add_writer_state(
        self, graph, after, features, key_columns=None, timestamp_key=None
    ):
        warnings.warn(
            "This method is deprecated. Use add_writer_step instead",
            # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove
            PendingDeprecationWarning,
        )
        """add storey writer state to graph"""
        self.add_writer_step(graph, after, features, key_columns, timestamp_key)

    def purge(self):
        self._get_store().rm(self.get_target_path(), recursive=True)

    def as_df(
        self,
        columns=None,
        df_module=None,
        entities=None,
        start_time=None,
        end_time=None,
        time_column=None,
        **kwargs,
    ):
        """return the target data as dataframe"""
        return mlrun.get_dataitem(self.get_target_path()).as_df(
            columns=columns,
            df_module=df_module,
            start_time=start_time,
            end_time=end_time,
            time_column=time_column,
            **kwargs,
        )

    def get_spark_options(self, key_column=None, timestamp_key=None, overwrite=True):
        # options used in spark.read.load(**options)
        raise NotImplementedError()

    def prepare_spark_df(self, df):
        return df

    def get_dask_options(self):
        raise NotImplementedError()


[docs]class ParquetTarget(BaseStoreTarget): """parquet target storage driver, used to materialize feature set/vector data into parquet files :param name: optional, target name. By default will be called ParquetTarget :param path: optional, Output path. Can be either a file or directory. This parameter is forwarded as-is to pandas.DataFrame.to_parquet(). Default location v3io:///projects/{project}/FeatureStore/{name}/parquet/ :param attributes: optional, extra attributes for storey.ParquetTarget :param after_step: optional, after what step in the graph to add the target :param columns: optional, which columns from data to write :param partitioned: optional, whether to partition the file, False by default, if True without passing any other partition field, the data will be partitioned by /year/month/day/hour :param key_bucketing_number: optional, None by default will not partition by key, 0 will partition by the key as is, any other number X will create X partitions and hash the keys to one of them :param partition_cols: optional, name of columns from the data to partition by :param time_partitioning_granularity: optional. the smallest time unit to partition the data by. For example "hour" will yield partitions of the format /year/month/day/hour :param max_events: optional. Maximum number of events to write at a time. All events will be written on flow termination, or after flush_after_seconds (if flush_after_seconds is set). Default 10k events :param flush_after_seconds: optional. Maximum number of seconds to hold events before they are written. All events will be written on flow termination, or after max_events are accumulated (if max_events is set). Default 15 minutes """ kind = TargetTypes.parquet is_offline = True support_spark = True support_storey = True support_dask = True support_append = True def __init__( self, name: str = "", path=None, attributes: typing.Dict[str, str] = None, after_step=None, columns=None, partitioned: bool = None, key_bucketing_number: typing.Optional[int] = None, partition_cols: typing.Optional[typing.List[str]] = None, time_partitioning_granularity: typing.Optional[str] = None, after_state=None, max_events: typing.Optional[int] = 10000, flush_after_seconds: typing.Optional[int] = 900, storage_options: typing.Dict[str, str] = None, ): if after_state: warnings.warn( "The after_state parameter is deprecated. Use after_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) after_step = after_step or after_state if partitioned is None: if all( value is None for value in [ key_bucketing_number, partition_cols, time_partitioning_granularity, ] ): partitioned = False else: partitioned = True super().__init__( name, path, attributes, after_step, columns, partitioned, key_bucketing_number, partition_cols, time_partitioning_granularity, max_events=max_events, flush_after_seconds=flush_after_seconds, storage_options=storage_options, ) if ( time_partitioning_granularity is not None and time_partitioning_granularity not in self._legal_time_units ): raise errors.MLRunInvalidArgumentError( f"time_partitioning_granularity parameter must be one of {','.join(self._legal_time_units)}, " f"not {time_partitioning_granularity}." ) _legal_time_units = ["year", "month", "day", "hour", "minute", "second"] @staticmethod def _write_dataframe(df, fs, target_path, partition_cols, **kwargs): if partition_cols: df.to_parquet(target_path, partition_cols=partition_cols, **kwargs) else: with fs.open(target_path, "wb") as fp: # In order to save the DataFrame in parquet format, all of the column names must be strings: df.columns = [str(column) for column in df.columns.tolist()] # Save to parquet: df.to_parquet(fp, **kwargs)
[docs] def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key)
[docs] def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): if self.attributes.get("infer_columns_from_data"): column_list = None else: column_list = self._get_column_list( features=features, timestamp_key=timestamp_key, key_columns=None, with_type=True, ) # need to extract types from features as part of column list partition_cols = None if self.key_bucketing_number is not None: partition_cols = [("$key", self.key_bucketing_number)] if self.partition_cols: partition_cols = partition_cols or [] partition_cols.extend(self.partition_cols) time_partitioning_granularity = self.time_partitioning_granularity if self.partitioned and all( value is None for value in [ time_partitioning_granularity, self.key_bucketing_number, self.partition_cols, ] ): time_partitioning_granularity = "hour" if time_partitioning_granularity is not None: partition_cols = partition_cols or [] for time_unit in self._legal_time_units: partition_cols.append(f"${time_unit}") if time_unit == time_partitioning_granularity: break if ( not self.partitioned and not self.get_target_path().endswith(".parquet") and not self.get_target_path().endswith(".pq") ): partition_cols = [] tuple_key_columns = [] for key_column in key_columns: tuple_key_columns.append((key_column.name, key_column.value_type)) if self.attributes: self.attributes[ "update_last_written" ] = featureset_status.update_last_written_for_target else: self.attributes = { "update_last_written": featureset_status.update_last_written_for_target } graph.add_step( name=self.name or "ParquetTarget", after=after, graph_shape="cylinder", class_name="storey.ParquetTarget", path=self.get_target_path(), columns=column_list, index_cols=tuple_key_columns, partition_cols=partition_cols, storage_options=self.storage_options or self._get_store().get_storage_options(), max_events=self.max_events, flush_after_seconds=self.flush_after_seconds, **self.attributes, )
[docs] def get_spark_options(self, key_column=None, timestamp_key=None, overwrite=True): partition_cols = [] if timestamp_key: time_partitioning_granularity = self.time_partitioning_granularity if ( not time_partitioning_granularity and self.partitioned and not self.partition_cols ): time_partitioning_granularity = "hour" if time_partitioning_granularity: for unit in self._legal_time_units: partition_cols.append(unit) if unit == time_partitioning_granularity: break result = { "path": store_path_to_spark(self.get_target_path()), "format": "parquet", } for partition_col in self.partition_cols or []: partition_cols.append(partition_col) if partition_cols: result["partitionBy"] = partition_cols return result
[docs] def get_dask_options(self): return {"format": "parquet"}
[docs] def as_df( self, columns=None, df_module=None, entities=None, start_time=None, end_time=None, time_column=None, **kwargs, ): """return the target data as dataframe""" return mlrun.get_dataitem(self.get_target_path()).as_df( columns=columns, df_module=df_module, format="parquet", start_time=start_time, end_time=end_time, time_column=time_column, **kwargs, )
[docs] def is_single_file(self): if self.path: return self.path.endswith(".parquet") or self.path.endswith(".pq") return False
[docs]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, partition_cols, **kwargs): with fs.open(target_path, "wb") as fp: df.to_csv(fp, **kwargs)
[docs] def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key)
[docs] def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): key_columns = list(key_columns.keys()) column_list = self._get_column_list( features=features, timestamp_key=timestamp_key, key_columns=key_columns ) graph.add_step( name=self.name or "CSVTarget", after=after, graph_shape="cylinder", class_name="storey.CSVTarget", path=self.get_target_path(), columns=column_list, header=True, index_cols=key_columns, storage_options=self._get_store().get_storage_options(), **self.attributes, )
[docs] def get_spark_options(self, key_column=None, timestamp_key=None, overwrite=True): return { "path": store_path_to_spark(self.get_target_path()), "format": "csv", "header": "true", }
[docs] def prepare_spark_df(self, df): import pyspark.sql.functions as funcs for col_name, col_type in df.dtypes: if col_type == "timestamp": # df.write.csv saves timestamps with millisecond precision, but we want microsecond precision # for compatibility with storey. df = df.withColumn( col_name, funcs.date_format(col_name, "yyyy-MM-dd HH:mm:ss.SSSSSS") ) return df
[docs] def as_df( self, columns=None, df_module=None, entities=None, start_time=None, end_time=None, time_column=None, **kwargs, ): df = super().as_df( columns=columns, df_module=df_module, entities=entities, **kwargs ) df.set_index(keys=entities, inplace=True) return df
[docs] def is_single_file(self): if self.path: return self.path.endswith(".csv") return True
[docs]class NoSqlTarget(BaseStoreTarget): kind = TargetTypes.nosql is_table = True is_online = True support_spark = True support_storey = True support_append = True
[docs] def get_table_object(self): from storey import Table, V3ioDriver # TODO use options/cred endpoint, uri = parse_v3io_path(self.get_target_path()) return Table( uri, V3ioDriver(webapi=endpoint), flush_interval_secs=mlrun.mlconf.feature_store.flush_interval, )
[docs] def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key)
[docs] def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): key_columns = list(key_columns.keys()) table = self._resource.uri column_list = self._get_column_list( features=features, timestamp_key=None, key_columns=key_columns, with_type=True, ) 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[0] not in aggregate_features ] graph.add_step( name=self.name or "NoSqlTarget", after=after, graph_shape="cylinder", class_name="storey.NoSqlTarget", columns=column_list, table=table, **self.attributes, )
[docs] def get_spark_options(self, key_column=None, timestamp_key=None, overwrite=True): spark_options = { "path": store_path_to_spark(self.get_target_path()), "format": "io.iguaz.v3io.spark.sql.kv", } if isinstance(key_column, list) and len(key_column) >= 1: if len(key_column) > 2: raise mlrun.errors.MLRunInvalidArgumentError( f"Spark supports maximun of 2 keys and {key_column} are provided" ) spark_options["key"] = key_column[0] if len(key_column) > 1: spark_options["sorting-key"] = key_column[1] else: spark_options["key"] = key_column if not overwrite: spark_options["columnUpdate"] = True return spark_options
[docs] def get_dask_options(self): return {"format": "csv"}
[docs] def as_df(self, columns=None, df_module=None, **kwargs): raise NotImplementedError()
[docs] def prepare_spark_df(self, df): import pyspark.sql.functions as funcs for col_name, col_type in df.dtypes: if col_type.startswith("decimal("): # V3IO does not support this level of precision df = df.withColumn(col_name, funcs.col(col_name).cast("double")) return df
[docs] def write_dataframe( self, df, key_column=None, timestamp_key=None, chunk_id=0, **kwargs ): if hasattr(df, "rdd"): options = self.get_spark_options(key_column, timestamp_key) options.update(kwargs) df = self.prepare_spark_df(df) 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.get_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)
[docs]class StreamTarget(BaseStoreTarget): kind = TargetTypes.stream is_table = False is_online = False support_spark = False support_storey = True support_append = True
[docs] def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key)
[docs] def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): from storey import V3ioDriver key_columns = list(key_columns.keys()) endpoint, uri = parse_v3io_path(self.get_target_path()) column_list = self._get_column_list( features=features, timestamp_key=timestamp_key, key_columns=key_columns ) graph.add_step( name=self.name or "StreamTarget", after=after, graph_shape="cylinder", class_name="storey.StreamTarget", columns=column_list, storage=V3ioDriver(webapi=endpoint), stream_path=uri, **self.attributes, )
[docs] def as_df(self, columns=None, df_module=None, **kwargs): raise NotImplementedError()
class TSDBTarget(BaseStoreTarget): kind = TargetTypes.tsdb is_table = False is_online = False support_spark = False support_storey = True support_append = True def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key) def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): key_columns = list(key_columns.keys()) endpoint, uri = parse_v3io_path(self.get_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=self.name or "TSDBTarget", class_name="storey.TSDBTarget", 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, **kwargs): raise NotImplementedError() def write_dataframe( self, df, key_column=None, timestamp_key=None, chunk_id=0, **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.get_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_step=None, after_state=None, **attributes, ): if after_state: warnings.warn( "The after_state parameter is deprecated. Use after_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) after_step = after_step or after_state attributes = attributes or {} attributes["class_name"] = class_name super().__init__(name, "", attributes, after_step=after_step) def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key) def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=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): pass def add_writer_state( self, graph, after, features, key_columns=None, timestamp_key=None ): warnings.warn( "This method is deprecated. Use add_writer_step instead", # TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove PendingDeprecationWarning, ) """add storey writer state to graph""" self.add_writer_step(graph, after, features, key_columns, timestamp_key) def add_writer_step( self, graph, after, features, key_columns=None, timestamp_key=None, featureset_status=None, ): key_columns = list(key_columns.keys()) # todo: column filter graph.add_step( name=self.name or "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, start_time=None, end_time=None, time_column=None, **kwargs, ): 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, run_id_mode=False): """return the default target path given the resource and target kind""" kind = driver.kind suffix = driver.suffix if not suffix: if ( kind == ParquetTarget.kind and resource.kind == mlrun.api.schemas.ObjectKind.feature_vector ): suffix = ".parquet" kind_prefix = ( "sets" if resource.kind == mlrun.api.schemas.ObjectKind.feature_set else "vectors" ) name = resource.metadata.name project = resource.metadata.project or mlrun.mlconf.default_project data_prefix = get_default_prefix_for_target(kind).format( project=project, kind=kind, name=name, ) # todo: handle ver tag changes, may need to copy files? if not run_id_mode: version = resource.metadata.tag name = f"{name}-{version or 'latest'}" return f"{data_prefix}/{kind_prefix}/{name}{suffix}" def generate_path_with_chunk(target, chunk_id): prefix, suffix = os.path.splitext(target.get_target_path()) if chunk_id and not target.partitioned and not target.time_partitioning_granularity: return f"{prefix}/{chunk_id:0>4}{suffix}" return target.get_target_path()