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superset/tests/integration_tests/sqla_models_tests.py

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# 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.
# isort:skip_file
import re
from datetime import datetime
from typing import Any, NamedTuple, Optional, Union
from re import Pattern
from unittest.mock import patch
import pytest
import numpy as np
import pandas as pd
from flask import Flask
from pytest_mock import MockFixture
from sqlalchemy.sql import text
from sqlalchemy.sql.elements import TextClause
from superset import db
from superset.connectors.sqla.models import SqlaTable, TableColumn, SqlMetric
from superset.constants import EMPTY_STRING, NULL_STRING
from superset.db_engine_specs.bigquery import BigQueryEngineSpec
from superset.db_engine_specs.druid import DruidEngineSpec
from superset.exceptions import QueryObjectValidationError, SupersetSecurityException
from superset.models.core import Database
from superset.utils.core import (
AdhocMetricExpressionType,
FilterOperator,
GenericDataType,
)
from superset.utils.database import get_example_database
from tests.integration_tests.fixtures.birth_names_dashboard import (
load_birth_names_dashboard_with_slices,
load_birth_names_data,
)
from tests.integration_tests.test_app import app
from .base_tests import SupersetTestCase
from .conftest import only_postgresql
VIRTUAL_TABLE_INT_TYPES: dict[str, Pattern[str]] = {
"hive": re.compile(r"^INT_TYPE$"),
"mysql": re.compile("^LONGLONG$"),
"postgresql": re.compile(r"^INTEGER$"),
"presto": re.compile(r"^INTEGER$"),
"sqlite": re.compile(r"^INT$"),
}
VIRTUAL_TABLE_STRING_TYPES: dict[str, Pattern[str]] = {
"hive": re.compile(r"^STRING_TYPE$"),
"mysql": re.compile(r"^VAR_STRING$"),
"postgresql": re.compile(r"^STRING$"),
"presto": re.compile(r"^VARCHAR*"),
"sqlite": re.compile(r"^STRING$"),
}
class FilterTestCase(NamedTuple):
column: str
operator: str
value: Union[float, int, list[Any], str]
expected: Union[str, list[str]]
class TestDatabaseModel(SupersetTestCase):
def test_is_time_druid_time_col(self):
"""Druid has a special __time column"""
database = Database(database_name="druid_db", sqlalchemy_uri="druid://db")
tbl = SqlaTable(table_name="druid_tbl", database=database)
col = TableColumn(column_name="__time", type="INTEGER", table=tbl)
self.assertEqual(col.is_dttm, None)
DruidEngineSpec.alter_new_orm_column(col)
self.assertEqual(col.is_dttm, True)
col = TableColumn(column_name="__not_time", type="INTEGER", table=tbl)
self.assertEqual(col.is_temporal, False)
def test_temporal_varchar(self):
"""Ensure a column with is_dttm set to true evaluates to is_temporal == True"""
database = get_example_database()
tbl = SqlaTable(table_name="test_tbl", database=database)
col = TableColumn(column_name="ds", type="VARCHAR", table=tbl)
# by default, VARCHAR should not be assumed to be temporal
assert col.is_temporal is False
# changing to `is_dttm = True`, calling `is_temporal` should return True
col.is_dttm = True
assert col.is_temporal is True
def test_db_column_types(self):
test_cases: dict[str, GenericDataType] = {
# string
"CHAR": GenericDataType.STRING,
"VARCHAR": GenericDataType.STRING,
"NVARCHAR": GenericDataType.STRING,
"STRING": GenericDataType.STRING,
"TEXT": GenericDataType.STRING,
"NTEXT": GenericDataType.STRING,
# numeric
"INTEGER": GenericDataType.NUMERIC,
"BIGINT": GenericDataType.NUMERIC,
"DECIMAL": GenericDataType.NUMERIC,
# temporal
"DATE": GenericDataType.TEMPORAL,
"DATETIME": GenericDataType.TEMPORAL,
"TIME": GenericDataType.TEMPORAL,
"TIMESTAMP": GenericDataType.TEMPORAL,
}
tbl = SqlaTable(table_name="col_type_test_tbl", database=get_example_database())
for str_type, db_col_type in test_cases.items():
col = TableColumn(column_name="foo", type=str_type, table=tbl)
self.assertEqual(col.is_temporal, db_col_type == GenericDataType.TEMPORAL)
self.assertEqual(col.is_numeric, db_col_type == GenericDataType.NUMERIC)
self.assertEqual(col.is_string, db_col_type == GenericDataType.STRING)
for str_type, db_col_type in test_cases.items():
col = TableColumn(column_name="foo", type=str_type, table=tbl, is_dttm=True)
self.assertTrue(col.is_temporal)
@patch("superset.jinja_context.get_user_id", return_value=1)
@patch("superset.jinja_context.get_username", return_value="abc")
@patch("superset.jinja_context.get_user_email", return_value="abc@test.com")
def test_extra_cache_keys(self, mock_user_email, mock_username, mock_user_id):
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["id", "username", "email"],
"metrics": [],
"is_timeseries": False,
"filter": [],
}
# Table with Jinja callable.
table1 = SqlaTable(
table_name="test_has_extra_cache_keys_table",
sql="""
SELECT
'{{ current_user_id() }}' as id,
'{{ current_username() }}' as username,
'{{ current_user_email() }}' as email
""",
database=get_example_database(),
)
query_obj = dict(**base_query_obj, extras={})
extra_cache_keys = table1.get_extra_cache_keys(query_obj)
self.assertTrue(table1.has_extra_cache_key_calls(query_obj))
assert extra_cache_keys == [1, "abc", "abc@test.com"]
# Table with Jinja callable disabled.
table2 = SqlaTable(
table_name="test_has_extra_cache_keys_disabled_table",
sql="""
SELECT
'{{ current_user_id(False) }}' as id,
'{{ current_username(False) }}' as username,
'{{ current_user_email(False) }}' as email,
""",
database=get_example_database(),
)
query_obj = dict(**base_query_obj, extras={})
extra_cache_keys = table2.get_extra_cache_keys(query_obj)
self.assertTrue(table2.has_extra_cache_key_calls(query_obj))
self.assertListEqual(extra_cache_keys, [])
# Table with no Jinja callable.
query = "SELECT 'abc' as user"
table3 = SqlaTable(
table_name="test_has_no_extra_cache_keys_table",
sql=query,
database=get_example_database(),
)
query_obj = dict(**base_query_obj, extras={"where": "(user != 'abc')"})
extra_cache_keys = table3.get_extra_cache_keys(query_obj)
self.assertFalse(table3.has_extra_cache_key_calls(query_obj))
self.assertListEqual(extra_cache_keys, [])
# With Jinja callable in SQL expression.
query_obj = dict(
**base_query_obj, extras={"where": "(user != '{{ current_username() }}')"}
)
extra_cache_keys = table3.get_extra_cache_keys(query_obj)
self.assertTrue(table3.has_extra_cache_key_calls(query_obj))
assert extra_cache_keys == ["abc"]
@patch("superset.jinja_context.get_username", return_value="abc")
def test_jinja_metrics_and_calc_columns(self, mock_username):
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"columns": [
"user",
"expr",
{
"hasCustomLabel": True,
"label": "adhoc_column",
"sqlExpression": "'{{ 'foo_' + time_grain }}'",
},
],
"metrics": [
{
"hasCustomLabel": True,
"label": "adhoc_metric",
"expressionType": AdhocMetricExpressionType.SQL,
"sqlExpression": "SUM(case when user = '{{ 'user_' + "
"current_username() }}' then 1 else 0 end)",
},
"count_timegrain",
],
"is_timeseries": False,
"filter": [],
"extras": {"time_grain_sqla": "P1D"},
}
table = SqlaTable(
table_name="test_has_jinja_metric_and_expr",
sql="SELECT '{{ 'user_' + current_username() }}' as user, "
"'{{ 'xyz_' + time_grain }}' as time_grain",
database=get_example_database(),
)
TableColumn(
column_name="expr",
expression="case when '{{ current_username() }}' = 'abc' "
"then 'yes' else 'no' end",
type="VARCHAR(100)",
table=table,
)
SqlMetric(
metric_name="count_timegrain",
expression="count('{{ 'bar_' + time_grain }}')",
table=table,
)
db.session.commit()
sqla_query = table.get_sqla_query(**base_query_obj)
query = table.database.compile_sqla_query(sqla_query.sqla_query)
# assert virtual dataset
assert "SELECT\n 'user_abc' AS user,\n 'xyz_P1D' AS time_grain" in query
# assert dataset calculated column
assert "case when 'abc' = 'abc' then 'yes' else 'no' end" in query
# assert adhoc column
assert "'foo_P1D'" in query
# assert dataset saved metric
assert "count('bar_P1D')" in query
# assert adhoc metric
assert "SUM(case when user = 'user_abc' then 1 else 0 end)" in query
# Cleanup
db.session.delete(table)
db.session.commit()
@patch("superset.views.utils.get_form_data")
def test_jinja_metric_macro(self, mock_form_data_context):
self.login(username="admin")
table = self.get_table(name="birth_names")
metric = SqlMetric(
metric_name="count_jinja_metric", expression="count(*)", table=table
)
db.session.commit()
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"columns": [],
"metrics": [
{
"hasCustomLabel": True,
"label": "Metric using Jinja macro",
"expressionType": AdhocMetricExpressionType.SQL,
"sqlExpression": "{{ metric('count_jinja_metric') }}",
},
{
"hasCustomLabel": True,
"label": "Same but different",
"expressionType": AdhocMetricExpressionType.SQL,
"sqlExpression": "{{ metric('count_jinja_metric', "
+ str(table.id)
+ ") }}",
},
],
"is_timeseries": False,
"filter": [],
"extras": {"time_grain_sqla": "P1D"},
}
mock_form_data_context.return_value = [
{
"url_params": {
"datasource_id": table.id,
}
},
None,
]
sqla_query = table.get_sqla_query(**base_query_obj)
query = table.database.compile_sqla_query(sqla_query.sqla_query)
database = table.database
with database.get_sqla_engine_with_context() as engine:
quote = engine.dialect.identifier_preparer.quote_identifier
for metric_label in {"metric using jinja macro", "same but different"}:
assert f"count(*) as {quote(metric_label)}" in query.lower()
db.session.delete(metric)
db.session.commit()
def test_adhoc_metrics_and_calc_columns(self):
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["user", "expr"],
"metrics": [
{
"expressionType": AdhocMetricExpressionType.SQL,
"sqlExpression": "(SELECT (SELECT * from birth_names) "
"from test_validate_adhoc_sql)",
"label": "adhoc_metrics",
}
],
"is_timeseries": False,
"filter": [],
}
table = SqlaTable(
table_name="test_validate_adhoc_sql", database=get_example_database()
)
db.session.commit()
with pytest.raises(QueryObjectValidationError):
table.get_sqla_query(**base_query_obj)
# Cleanup
db.session.delete(table)
db.session.commit()
@pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
def test_where_operators(self):
filters: tuple[FilterTestCase, ...] = (
FilterTestCase("num", FilterOperator.IS_NULL, "", "IS NULL"),
FilterTestCase("num", FilterOperator.IS_NOT_NULL, "", "IS NOT NULL"),
# Some db backends translate true/false to 1/0
FilterTestCase("num", FilterOperator.IS_TRUE, "", ["IS 1", "IS true"]),
FilterTestCase("num", FilterOperator.IS_FALSE, "", ["IS 0", "IS false"]),
FilterTestCase("num", FilterOperator.GREATER_THAN, 0, "> 0"),
FilterTestCase("num", FilterOperator.GREATER_THAN_OR_EQUALS, 0, ">= 0"),
FilterTestCase("num", FilterOperator.LESS_THAN, 0, "< 0"),
FilterTestCase("num", FilterOperator.LESS_THAN_OR_EQUALS, 0, "<= 0"),
FilterTestCase("num", FilterOperator.EQUALS, 0, "= 0"),
FilterTestCase("num", FilterOperator.NOT_EQUALS, 0, "!= 0"),
FilterTestCase("num", FilterOperator.IN, ["1", "2"], "IN (1, 2)"),
FilterTestCase("num", FilterOperator.NOT_IN, ["1", "2"], "NOT IN (1, 2)"),
FilterTestCase(
"ds", FilterOperator.TEMPORAL_RANGE, "2020 : 2021", "2020-01-01"
),
)
table = self.get_table(name="birth_names")
for filter_ in filters:
query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["gender"],
"metrics": ["count"],
"is_timeseries": False,
"filter": [
{
"col": filter_.column,
"op": filter_.operator,
"val": filter_.value,
}
],
"extras": {},
}
sqla_query = table.get_sqla_query(**query_obj)
sql = table.database.compile_sqla_query(sqla_query.sqla_query)
if isinstance(filter_.expected, list):
self.assertTrue(
any([candidate in sql for candidate in filter_.expected])
)
else:
self.assertIn(filter_.expected, sql)
@pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
def test_boolean_type_where_operators(self):
table = self.get_table(name="birth_names")
db.session.add(
TableColumn(
column_name="boolean_gender",
expression="case when gender = 'boy' then True else False end",
type="BOOLEAN",
table=table,
)
)
query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["boolean_gender"],
"metrics": ["count"],
"is_timeseries": False,
"filter": [
{
"col": "boolean_gender",
"op": FilterOperator.IN,
"val": ["true", "false"],
}
],
"extras": {},
}
sqla_query = table.get_sqla_query(**query_obj)
sql = table.database.compile_sqla_query(sqla_query.sqla_query)
dialect = table.database.get_dialect()
operand = "(true, false)"
# override native_boolean=False behavior in MySQLCompiler
# https://github.com/sqlalchemy/sqlalchemy/blob/master/lib/sqlalchemy/dialects/mysql/base.py
if not dialect.supports_native_boolean and dialect.name != "mysql":
operand = "(1, 0)"
self.assertIn(f"IN {operand}", sql)
def test_incorrect_jinja_syntax_raises_correct_exception(self):
query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["user"],
"metrics": [],
"is_timeseries": False,
"filter": [],
"extras": {},
}
# Table with Jinja callable.
table = SqlaTable(
table_name="test_table",
sql="SELECT '{{ abcd xyz + 1 ASDF }}' as user",
database=get_example_database(),
)
# TODO(villebro): make it work with presto
if get_example_database().backend != "presto":
with pytest.raises(QueryObjectValidationError):
table.get_sqla_query(**query_obj)
def test_query_format_strip_trailing_semicolon(self):
query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["user"],
"metrics": [],
"is_timeseries": False,
"filter": [],
"extras": {},
}
table = SqlaTable(
table_name="another_test_table",
sql="SELECT * from test_table;",
database=get_example_database(),
)
sqlaq = table.get_sqla_query(**query_obj)
sql = table.database.compile_sqla_query(sqlaq.sqla_query)
assert sql[-1] != ";"
def test_multiple_sql_statements_raises_exception(self):
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["grp"],
"metrics": [],
"is_timeseries": False,
"filter": [],
}
table = SqlaTable(
table_name="test_multiple_sql_statements",
sql="SELECT 'foo' as grp, 1 as num; SELECT 'bar' as grp, 2 as num",
database=get_example_database(),
)
query_obj = dict(**base_query_obj, extras={})
with pytest.raises(QueryObjectValidationError):
table.get_sqla_query(**query_obj)
def test_dml_statement_raises_exception(self):
base_query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["grp"],
"metrics": [],
"is_timeseries": False,
"filter": [],
}
table = SqlaTable(
table_name="test_dml_statement",
sql="DELETE FROM foo",
database=get_example_database(),
)
query_obj = dict(**base_query_obj, extras={})
with pytest.raises(QueryObjectValidationError):
table.get_sqla_query(**query_obj)
def test_fetch_metadata_for_updated_virtual_table(self):
table = SqlaTable(
table_name="updated_sql_table",
database=get_example_database(),
sql="select 123 as intcol, 'abc' as strcol, 'abc' as mycase",
)
TableColumn(column_name="intcol", type="FLOAT", table=table)
TableColumn(column_name="oldcol", type="INT", table=table)
TableColumn(
column_name="expr",
expression="case when 1 then 1 else 0 end",
type="INT",
table=table,
)
TableColumn(
column_name="mycase",
expression="case when 1 then 1 else 0 end",
type="INT",
table=table,
)
# make sure the columns have been mapped properly
assert len(table.columns) == 4
with db.session.no_autoflush:
table.fetch_metadata(commit=False)
# assert that the removed column has been dropped and
# the physical and calculated columns are present
assert {col.column_name for col in table.columns} == {
"intcol",
"strcol",
"mycase",
"expr",
}
cols: dict[str, TableColumn] = {col.column_name: col for col in table.columns}
# assert that the type for intcol has been updated (asserting CI types)
backend = table.database.backend
assert VIRTUAL_TABLE_INT_TYPES[backend].match(cols["intcol"].type)
# assert that the expression has been replaced with the new physical column
assert cols["mycase"].expression == ""
assert VIRTUAL_TABLE_STRING_TYPES[backend].match(cols["mycase"].type)
assert cols["expr"].expression == "case when 1 then 1 else 0 end"
@patch("superset.models.core.Database.db_engine_spec", BigQueryEngineSpec)
def test_labels_expected_on_mutated_query(self):
query_obj = {
"granularity": None,
"from_dttm": None,
"to_dttm": None,
"groupby": ["user"],
"metrics": [
{
"expressionType": "SIMPLE",
"column": {"column_name": "user"},
"aggregate": "COUNT_DISTINCT",
"label": "COUNT_DISTINCT(user)",
}
],
"is_timeseries": False,
"filter": [],
"extras": {},
}
database = Database(database_name="testdb", sqlalchemy_uri="sqlite://")
table = SqlaTable(table_name="bq_table", database=database)
db.session.add(database)
db.session.add(table)
db.session.commit()
sqlaq = table.get_sqla_query(**query_obj)
assert sqlaq.labels_expected == ["user", "COUNT_DISTINCT(user)"]
sql = table.database.compile_sqla_query(sqlaq.sqla_query)
assert "COUNT_DISTINCT_user__00db1" in sql
db.session.delete(table)
db.session.delete(database)
db.session.commit()
@pytest.fixture
def text_column_table():
with app.app_context():
table = SqlaTable(
table_name="text_column_table",
sql=(
"SELECT 'foo' as foo "
"UNION SELECT '' "
"UNION SELECT NULL "
"UNION SELECT 'null' "
"UNION SELECT '\"text in double quotes\"' "
"UNION SELECT '''text in single quotes''' "
"UNION SELECT 'double quotes \" in text' "
"UNION SELECT 'single quotes '' in text' "
),
database=get_example_database(),
)
TableColumn(column_name="foo", type="VARCHAR(255)", table=table)
SqlMetric(metric_name="count", expression="count(*)", table=table)
yield table
def test_values_for_column_on_text_column(text_column_table):
# null value, empty string and text should be retrieved
with_null = text_column_table.values_for_column(column_name="foo", limit=10000)
assert None in with_null
assert len(with_null) == 8
def test_filter_on_text_column(text_column_table):
table = text_column_table
# null value should be replaced
result_object = table.query(
{
"metrics": ["count"],
"filter": [{"col": "foo", "val": [NULL_STRING], "op": "IN"}],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# also accept None value
result_object = table.query(
{
"metrics": ["count"],
"filter": [{"col": "foo", "val": [None], "op": "IN"}],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# empty string should be replaced
result_object = table.query(
{
"metrics": ["count"],
"filter": [{"col": "foo", "val": [EMPTY_STRING], "op": "IN"}],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# also accept "" string
result_object = table.query(
{
"metrics": ["count"],
"filter": [{"col": "foo", "val": [""], "op": "IN"}],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# both replaced
result_object = table.query(
{
"metrics": ["count"],
"filter": [
{
"col": "foo",
"val": [EMPTY_STRING, NULL_STRING, "null", "foo"],
"op": "IN",
}
],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 4
# should filter text in double quotes
result_object = table.query(
{
"metrics": ["count"],
"filter": [
{
"col": "foo",
"val": ['"text in double quotes"'],
"op": "IN",
}
],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# should filter text in single quotes
result_object = table.query(
{
"metrics": ["count"],
"filter": [
{
"col": "foo",
"val": ["'text in single quotes'"],
"op": "IN",
}
],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# should filter text with double quote
result_object = table.query(
{
"metrics": ["count"],
"filter": [
{
"col": "foo",
"val": ['double quotes " in text'],
"op": "IN",
}
],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
# should filter text with single quote
result_object = table.query(
{
"metrics": ["count"],
"filter": [
{
"col": "foo",
"val": ["single quotes ' in text"],
"op": "IN",
}
],
"is_timeseries": False,
}
)
assert result_object.df["count"][0] == 1
@only_postgresql
def test_should_generate_closed_and_open_time_filter_range(login_as_admin):
table = SqlaTable(
table_name="temporal_column_table",
sql=(
"SELECT '2021-12-31'::timestamp as datetime_col "
"UNION SELECT '2022-01-01'::timestamp "
"UNION SELECT '2022-03-10'::timestamp "
"UNION SELECT '2023-01-01'::timestamp "
"UNION SELECT '2023-03-10'::timestamp "
),
database=get_example_database(),
)
TableColumn(
column_name="datetime_col",
type="TIMESTAMP",
table=table,
is_dttm=True,
)
SqlMetric(metric_name="count", expression="count(*)", table=table)
result_object = table.query(
{
"metrics": ["count"],
"is_timeseries": False,
"filter": [],
"from_dttm": datetime(2022, 1, 1),
"to_dttm": datetime(2023, 1, 1),
"granularity": "datetime_col",
}
)
""" >>> result_object.query
SELECT count(*) AS count
FROM
(SELECT '2021-12-31'::timestamp as datetime_col
UNION SELECT '2022-01-01'::timestamp
UNION SELECT '2022-03-10'::timestamp
UNION SELECT '2023-01-01'::timestamp
UNION SELECT '2023-03-10'::timestamp) AS virtual_table
WHERE datetime_col >= TO_TIMESTAMP('2022-01-01 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
AND datetime_col < TO_TIMESTAMP('2023-01-01 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
"""
assert result_object.df.iloc[0]["count"] == 2
def test_none_operand_in_filter(login_as_admin, physical_dataset):
expected_results = [
{
"operator": FilterOperator.EQUALS.value,
"count": 10,
"sql_should_contain": "COL4 IS NULL",
},
{
"operator": FilterOperator.NOT_EQUALS.value,
"count": 0,
"sql_should_contain": "NOT COL4 IS NULL",
},
]
for expected in expected_results:
result = physical_dataset.query(
{
"metrics": ["count"],
"filter": [{"col": "col4", "val": None, "op": expected["operator"]}],
"is_timeseries": False,
}
)
assert result.df["count"][0] == expected["count"]
assert expected["sql_should_contain"] in result.query.upper()
with pytest.raises(QueryObjectValidationError):
for flt in [
FilterOperator.GREATER_THAN,
FilterOperator.LESS_THAN,
FilterOperator.GREATER_THAN_OR_EQUALS,
FilterOperator.LESS_THAN_OR_EQUALS,
FilterOperator.LIKE,
FilterOperator.ILIKE,
]:
physical_dataset.query(
{
"metrics": ["count"],
"filter": [{"col": "col4", "val": None, "op": flt.value}],
"is_timeseries": False,
}
)
@pytest.mark.parametrize(
"row,dimension,result",
[
(pd.Series({"foo": "abc"}), "foo", "abc"),
(pd.Series({"bar": True}), "bar", True),
(pd.Series({"baz": 123}), "baz", 123),
(pd.Series({"baz": np.int16(123)}), "baz", 123),
(pd.Series({"baz": np.uint32(123)}), "baz", 123),
(pd.Series({"baz": np.int64(123)}), "baz", 123),
(pd.Series({"qux": 123.456}), "qux", 123.456),
(pd.Series({"qux": np.float32(123.456)}), "qux", 123.45600128173828),
(pd.Series({"qux": np.float64(123.456)}), "qux", 123.456),
(pd.Series({"quux": "2021-01-01"}), "quux", "2021-01-01"),
(
pd.Series({"quuz": "2021-01-01T00:00:00"}),
"quuz",
text("TIME_PARSE('2021-01-01T00:00:00')"),
),
],
)
def test__normalize_prequery_result_type(
app_context: Flask,
mocker: MockFixture,
row: pd.Series,
dimension: str,
result: Any,
) -> None:
def _convert_dttm(
target_type: str, dttm: datetime, db_extra: Optional[dict[str, Any]] = None
) -> Optional[str]:
if target_type.upper() == "TIMESTAMP":
return f"""TIME_PARSE('{dttm.isoformat(timespec="seconds")}')"""
return None
table = SqlaTable(table_name="foobar", database=get_example_database())
mocker.patch.object(table.db_engine_spec, "convert_dttm", new=_convert_dttm)
columns_by_name = {
"foo": TableColumn(
column_name="foo",
is_dttm=False,
table=table,
type="STRING",
),
"bar": TableColumn(
column_name="bar",
is_dttm=False,
table=table,
type="BOOLEAN",
),
"baz": TableColumn(
column_name="baz",
is_dttm=False,
table=table,
type="INTEGER",
),
"qux": TableColumn(
column_name="qux",
is_dttm=False,
table=table,
type="FLOAT",
),
"quux": TableColumn(
column_name="quuz",
is_dttm=True,
table=table,
type="STRING",
),
"quuz": TableColumn(
column_name="quux",
is_dttm=True,
table=table,
type="TIMESTAMP",
),
}
normalized = table._normalize_prequery_result_type(
row,
dimension,
columns_by_name,
)
assert type(normalized) == type(result)
if isinstance(normalized, TextClause):
assert str(normalized) == str(result)
else:
assert normalized == result
def test__temporal_range_operator_in_adhoc_filter(app_context, physical_dataset):
result = physical_dataset.query(
{
"columns": ["col1", "col2"],
"filter": [
{
"col": "col5",
"val": "2000-01-05 : 2000-01-06",
"op": FilterOperator.TEMPORAL_RANGE.value,
},
{
"col": "col6",
"val": "2002-05-11 : 2002-05-12",
"op": FilterOperator.TEMPORAL_RANGE.value,
},
],
"is_timeseries": False,
}
)
df = pd.DataFrame(index=[0], data={"col1": 4, "col2": "e"})
assert df.equals(result.df)
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