Source code for nvtabular.ops.join_external

# Copyright (c) 2021, NVIDIA CORPORATION.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings

import dask.dataframe as dd
import pandas as pd

from merlin.core.compat import cudf
from merlin.core.dispatch import (
from merlin.schema import Schema
from nvtabular.ops.operator import ColumnSelector, Operator

[docs]class JoinExternal(Operator): """ Join each dataset partition to an external table. For performance reasons, only "left" and "inner" join transformations are supported. Example usage:: # Load dataset which should be joined to the main dataset df_external = cudf.read_parquet('external.parquet') # Use JoinExternal to define a NVTabular workflow joined = ColumnSelector(columns_left) >> nvt.ops.JoinExternal( df_ext, on=['key1', 'key2'], on_ext=['key1_ext', 'key2_ext'], how='left', columns_ext=['key1_ext', 'key2_ext', 'cat1', 'cat2', 'num1'], kind_ext='cudf', cache='device' ) >> ... processor = nvtabular.Workflow(joined) Parameters ----------- df_ext : DataFrame, pyarrow.Table, Dataset, dd.DataFrame, or file path(s) The external table to join to each partition of the dataset. Note that the join must be a partition-wise transformation. Therefore, if ``df_ext`` is a multi-partition Dask collection, it will need to be broadcasted to every partition. on : str or list(str) Column name(s) to merge on how : {"left", "inner"}; default "left" Type of join operation to perform. on_ext : str or list(str); Optional Column name(s) on external table to join on. By default, we assume ``on_ext`` is the same as ``on``. columns_ext : list(str); Optional Subset of columns to select from external table before join. drop_duplicates_ext : bool; Default False Drop duplicates from external table before join. kind_ext : ExtData; Optional Format of ``df_ext``. If nothing is specified, the format will be inferred. cache : {"device", "host", "disk"} Where to cache ``df_ext`` between transformations. Only used if the data is originally stored on disk. The "host" option is also supported when ``df_ext`` is a ``cudf.DataFrame``. """
[docs] def __init__( self, df_ext, on, how="left", on_ext=None, columns_ext=None, drop_duplicates_ext=None, kind_ext=None, cache="host", **kwargs, ): super(JoinExternal).__init__() self.on = on self.df_ext = create_merlin_dataset(df_ext) self.on_ext = on_ext or self.on = how self.kind_ext = kind_ext or detect_format(self.df_ext) self.columns_ext = columns_ext self.drop_duplicates_ext = drop_duplicates_ext self.cache = cache self.kwargs = kwargs self.cpu = None self._ext_cache = None if cudf is None: self.cpu = True if not in ("left", "inner"): raise ValueError("Only left join is currently supported.") if not isinstance(self.kind_ext, ExtData): raise ValueError("kind_ext option not recognized.") super().__init__()
@property def _ext(self): if self._ext_cache is not None: # Return cached result if present return convert_data(self._ext_cache, cpu=self.cpu) # Use Dataset.to_ddf _dataset = self.df_ext if self.cpu: _dataset.to_cpu() else: _dataset.to_gpu() _ext = _check_partition_count(_dataset.to_ddf(columns=self.columns_ext)) # Take subset of columns if a list is specified if self.columns_ext: _ext = _ext[self.columns_ext] # Drop duplicates if requested if self.drop_duplicates_ext: if isinstance(_ext, dd.DataFrame): _ext = _ext.drop_duplicates(ignore_index=True) else: _ext.drop_duplicates(ignore_index=True, inplace=True) # Cache and return if self.cache == "host": self._ext_cache = to_host(_ext) elif self.cache == "device" or self.kind_ext not in (ExtData.PARQUET, ExtData.CSV): self._ext_cache = _ext return _ext def _merge(self, df, _ext): if isinstance(_ext, dd.DataFrame): _ddf = dd.from_pandas(df, npartitions=1) return _ddf.merge(_ext, left_on=self.on, right_on=self.on_ext, else: return df.merge(_ext, left_on=self.on, right_on=self.on_ext,
[docs] def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType: self.cpu = isinstance(df, pd.DataFrame) tmp = "__tmp__" # Temporary column for sorting df[tmp] = arange(len(df), like_df=df, dtype="int32") new_df = self._merge(df, self._ext) new_df = new_df.sort_values(tmp) new_df.drop(columns=[tmp], inplace=True) df.drop(columns=[tmp], inplace=True) new_df.reset_index(drop=True, inplace=True) return new_df
transform.__doc__ = Operator.transform.__doc__
[docs] def compute_selector( self, input_schema: Schema, selector: ColumnSelector, parents_selector: ColumnSelector, dependencies_selector: ColumnSelector, ) -> ColumnSelector: self._validate_matching_cols(input_schema, parents_selector, "computing input selector") return parents_selector
[docs] def compute_output_schema(self, input_schema, col_selector, prev_output_schema=None): # must load in the schema from the external dataset input_schema = input_schema + self.df_ext.schema return super().compute_output_schema(input_schema, col_selector, prev_output_schema)
[docs] def column_mapping(self, col_selector): column_mapping = {} ext_columns = self.columns_ext if self.columns_ext else self._ext.columns # This maintains the order which set() does not combined_col_names = dict.fromkeys(col_selector.names + list(ext_columns)).keys() for col_name in combined_col_names: column_mapping[col_name] = [col_name] return column_mapping
def _compute_dtype(self, col_schema, input_schema): if in input_schema.column_names: return super()._compute_dtype(col_schema, input_schema) else: col_dtype = self.df_ext.schema.column_schemas[].dtype return col_schema.with_dtype(col_dtype) def _compute_tags(self, col_schema, input_schema): return col_schema def _compute_properties(self, col_schema, input_schema): return col_schema
def _check_partition_count(df): if hasattr(df, "npartitions"): if df.npartitions == 1: # Materialize single-partition collections return df.compute() if df.npartitions > 3: warnings.warn( f"Joining an external Dask collection with " f"{df.npartitions} partitions. This transformation " f"requires a broadcast merge, which can be problematic " f"when the external collection is too large." ) return df