# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """ Superset wrapper around pyarrow.Table. """ import datetime import json import logging from typing import Any, Dict, List, Optional, Tuple, Type import numpy as np import pandas as pd import pyarrow as pa from superset.db_engine_specs import BaseEngineSpec from superset.superset_typing import DbapiDescription, DbapiResult, ResultSetColumnType from superset.utils import core as utils logger = logging.getLogger(__name__) def dedup(l: List[str], suffix: str = "__", case_sensitive: bool = True) -> List[str]: """De-duplicates a list of string by suffixing a counter Always returns the same number of entries as provided, and always returns unique values. Case sensitive comparison by default. >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar']))) foo,bar,bar__1,bar__2,Bar >>> print( ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False)) ) foo,bar,bar__1,bar__2,Bar__3 """ new_l: List[str] = [] seen: Dict[str, int] = {} for item in l: s_fixed_case = item if case_sensitive else item.lower() if s_fixed_case in seen: seen[s_fixed_case] += 1 item += suffix + str(seen[s_fixed_case]) else: seen[s_fixed_case] = 0 new_l.append(item) return new_l def stringify(obj: Any) -> str: return json.dumps(obj, default=utils.json_iso_dttm_ser) def stringify_values(array: np.ndarray) -> np.ndarray: vstringify = np.vectorize(stringify) return vstringify(array) def destringify(obj: str) -> Any: return json.loads(obj) def convert_to_string(value: Any) -> str: """ Used to ensure column names from the cursor description are strings. """ if isinstance(value, str): return value if isinstance(value, bytes): return value.decode("utf-8") return str(value) class SupersetResultSet: def __init__( # pylint: disable=too-many-locals self, data: DbapiResult, cursor_description: DbapiDescription, db_engine_spec: Type[BaseEngineSpec], ): self.db_engine_spec = db_engine_spec data = data or [] column_names: List[str] = [] pa_data: List[pa.Array] = [] deduped_cursor_desc: List[Tuple[Any, ...]] = [] numpy_dtype: List[Tuple[str, ...]] = [] stringified_arr: np.ndarray if cursor_description: # get deduped list of column names column_names = dedup( [convert_to_string(col[0]) for col in cursor_description] ) # fix cursor descriptor with the deduped names deduped_cursor_desc = [ tuple([column_name, *list(description)[1:]]) for column_name, description in zip(column_names, cursor_description) ] # generate numpy structured array dtype numpy_dtype = [(column_name, "object") for column_name in column_names] # only do expensive recasting if datatype is not standard list of tuples if data and (not isinstance(data, list) or not isinstance(data[0], tuple)): data = [tuple(row) for row in data] array = np.array(data, dtype=numpy_dtype) if array.size > 0: for column in column_names: try: pa_data.append(pa.array(array[column].tolist())) except ( pa.lib.ArrowInvalid, pa.lib.ArrowTypeError, pa.lib.ArrowNotImplementedError, TypeError, # this is super hackey, # https://issues.apache.org/jira/browse/ARROW-7855 ): # attempt serialization of values as strings stringified_arr = stringify_values(array[column]) pa_data.append(pa.array(stringified_arr.tolist())) if pa_data: # pylint: disable=too-many-nested-blocks for i, column in enumerate(column_names): if pa.types.is_nested(pa_data[i].type): # TODO: revisit nested column serialization once nested types # are added as a natively supported column type in Superset # (superset.utils.core.GenericDataType). stringified_arr = stringify_values(array[column]) pa_data[i] = pa.array(stringified_arr.tolist()) elif pa.types.is_temporal(pa_data[i].type): # workaround for bug converting # `psycopg2.tz.FixedOffsetTimezone` tzinfo values. # related: https://issues.apache.org/jira/browse/ARROW-5248 sample = self.first_nonempty(array[column]) if sample and isinstance(sample, datetime.datetime): try: if sample.tzinfo: tz = sample.tzinfo series = pd.Series( array[column], dtype="datetime64[ns]" ) series = pd.to_datetime(series).dt.tz_localize(tz) pa_data[i] = pa.Array.from_pandas( series, type=pa.timestamp("ns", tz=tz) ) except Exception as ex: # pylint: disable=broad-except logger.exception(ex) if not pa_data: column_names = [] self.table = pa.Table.from_arrays(pa_data, names=column_names) self._type_dict: Dict[str, Any] = {} try: # The driver may not be passing a cursor.description self._type_dict = { col: db_engine_spec.get_datatype(deduped_cursor_desc[i][1]) for i, col in enumerate(column_names) if deduped_cursor_desc } except Exception as ex: # pylint: disable=broad-except logger.exception(ex) @staticmethod def convert_pa_dtype(pa_dtype: pa.DataType) -> Optional[str]: if pa.types.is_boolean(pa_dtype): return "BOOL" if pa.types.is_integer(pa_dtype): return "INT" if pa.types.is_floating(pa_dtype): return "FLOAT" if pa.types.is_string(pa_dtype): return "STRING" if pa.types.is_temporal(pa_dtype): return "DATETIME" return None @staticmethod def convert_table_to_df(table: pa.Table) -> pd.DataFrame: try: return table.to_pandas(integer_object_nulls=True) except pa.lib.ArrowInvalid: return table.to_pandas(integer_object_nulls=True, timestamp_as_object=True) @staticmethod def first_nonempty(items: List[Any]) -> Any: return next((i for i in items if i), None) def is_temporal(self, db_type_str: Optional[str]) -> bool: column_spec = self.db_engine_spec.get_column_spec(db_type_str) if column_spec is None: return False return column_spec.is_dttm def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]: """Given a pyarrow data type, Returns a generic database type""" set_type = self._type_dict.get(col_name) if set_type: return set_type mapped_type = self.convert_pa_dtype(pa_dtype) if mapped_type: return mapped_type return None def to_pandas_df(self) -> pd.DataFrame: return self.convert_table_to_df(self.table) @property def pa_table(self) -> pa.Table: return self.table @property def size(self) -> int: return self.table.num_rows @property def columns(self) -> List[ResultSetColumnType]: if not self.table.column_names: return [] columns = [] for col in self.table.schema: db_type_str = self.data_type(col.name, col.type) column: ResultSetColumnType = { "name": col.name, "type": db_type_str, "is_dttm": self.is_temporal(db_type_str), } columns.append(column) return columns