superset/result_set.py
# 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 logging
from typing import Any, Optional
import numpy as np
import pandas as pd
import pyarrow as pa
from numpy.typing import NDArray
from superset.db_engine_specs import BaseEngineSpec
from superset.superset_typing import DbapiDescription, DbapiResult, ResultSetColumnType
from superset.utils import core as utils, json
from superset.utils.core import GenericDataType
logger = logging.getLogger(__name__)
def dedup(l: list[str], suffix: str = "__", case_sensitive: bool = True) -> list[str]: # noqa: E741
"""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=json.json_iso_dttm_ser)
def stringify_values(array: NDArray[Any]) -> NDArray[Any]:
result = np.copy(array)
with np.nditer(result, flags=["refs_ok"], op_flags=[["readwrite"]]) as it:
for obj in it:
if na_obj := pd.isna(obj):
# pandas <NA> type cannot be converted to string
obj[na_obj] = None
else:
try:
# for simple string conversions
# this handles odd character types better
obj[...] = obj.astype(str)
except ValueError:
obj[...] = stringify(obj)
return result
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: NDArray[Any]
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,
ValueError,
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])
series = pd.to_datetime(series)
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: NDArray[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 type_generic(
self, db_type_str: Optional[str]
) -> Optional[utils.GenericDataType]:
column_spec = self.db_engine_spec.get_column_spec(db_type_str)
if column_spec is None:
return None
if column_spec.is_dttm:
return GenericDataType.TEMPORAL
return column_spec.generic_type
def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]:
"""Given a pyarrow data type, Returns a generic database type"""
if set_type := self._type_dict.get(col_name):
return set_type
if mapped_type := self.convert_pa_dtype(pa_dtype):
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 = {
"column_name": col.name,
"name": col.name,
"type": db_type_str,
"type_generic": self.type_generic(db_type_str),
"is_dttm": self.is_temporal(db_type_str) or False,
}
columns.append(column)
return columns