def test_accessors() -> None:
func = FunctionCall(
String("f_name"),
(
FunctionCall(String("f"), (Column(None, String("my_col")), )),
Param(
"second_function",
FunctionCall(Param("second_function_name", Any(str)), None),
),
),
)
result = func.match(
FunctionCallExpr(
"irrelevant",
"f_name",
(
FunctionCallExpr(None, "f",
(ColumnExpr(None, None, "my_col"), )),
FunctionCallExpr(None, "second_name", tuple()),
),
))
assert result is not None
assert result.expression("second_function") == FunctionCallExpr(
None, "second_name", tuple())
assert result.scalar("second_function_name") == "second_name"
def get_time_range_expressions(
conditions: Sequence[Expression],
timestamp_field: str,
table_name: Optional[str] = None,
) -> Tuple[Optional[Tuple[datetime, FunctionCallExpr]], Optional[Tuple[
datetime, FunctionCallExpr]], ]:
max_lower_bound: Optional[Tuple[datetime, FunctionCallExpr]] = None
min_upper_bound: Optional[Tuple[datetime, FunctionCallExpr]] = None
table_match = String(table_name) if table_name else None
for c in conditions:
match = FunctionCall(
Param(
"operator",
Or([
String(OPERATOR_TO_FUNCTION[">="]),
String(OPERATOR_TO_FUNCTION["<"]),
]),
),
(
Column(table_match, String(timestamp_field)),
Literal(Param("timestamp", Any(datetime))),
),
).match(c)
if match is not None:
timestamp = cast(datetime, match.scalar("timestamp"))
assert isinstance(c, FunctionCallExpr)
if match.string("operator") == OPERATOR_TO_FUNCTION[">="]:
if not max_lower_bound or timestamp > max_lower_bound[0]:
max_lower_bound = (timestamp, c)
else:
if not min_upper_bound or timestamp < min_upper_bound[0]:
min_upper_bound = (timestamp, c)
return (max_lower_bound, min_upper_bound)
def __init__(self, uuid_columns: Set[str]) -> None:
self.__unique_uuid_columns = uuid_columns
self.__uuid_column_match = Or([String(u_col) for u_col in uuid_columns])
self.uuid_in_condition = FunctionCallMatch(
Or((String(ConditionFunctions.IN), String(ConditionFunctions.NOT_IN))),
(
self.formatted_uuid_pattern(),
Param("params", FunctionCallMatch(String("tuple"), None)),
),
)
self.uuid_condition = FunctionCallMatch(
Or(
[
String(op)
for op in FUNCTION_TO_OPERATOR
if op not in (ConditionFunctions.IN, ConditionFunctions.NOT_IN)
]
),
(
Or(
(
Param("literal_0", LiteralMatch(AnyOptionalString())),
self.formatted_uuid_pattern("_0"),
)
),
Or(
(
Param("literal_1", LiteralMatch(AnyOptionalString())),
self.formatted_uuid_pattern("_1"),
)
),
),
)
self.formatted: Optional[str] = None
def build_match(
col: str, ops: Sequence[str], param_type: Any, alias: Optional[str] = None
) -> Or[Expression]:
# The IN condition has to be checked separately since each parameter
# has to be checked individually.
alias_match = AnyOptionalString() if alias is None else String(alias)
column_match = Param("column", ColumnPattern(alias_match, String(col)))
return Or(
[
FunctionCallPattern(
Or([String(op) for op in ops]),
(column_match, Param("rhs", LiteralPattern(AnyPattern(param_type)))),
),
FunctionCallPattern(
String(ConditionFunctions.IN),
(
column_match,
Param(
"rhs",
FunctionCallPattern(
Or([String("array"), String("tuple")]),
all_parameters=LiteralPattern(AnyPattern(param_type)),
),
),
),
),
]
)
def __set_condition_pattern(lhs: Pattern[Expression],
operator: str) -> FunctionCallPattern:
return FunctionCallPattern(
String(operator),
(
Param("lhs", lhs),
Param("tuple", FunctionCallPattern(String("tuple"), None)),
),
)
def extract_granularity_from_query(query: Query, column: str) -> Optional[int]:
"""
This extracts the `granularity` from the `groupby` statement of the query.
The matches are essentially the reverse of `TimeSeriesProcessor.__group_time_function`.
"""
groupby = query.get_groupby()
column_match = ColumnMatch(None, String(column))
fn_match = FunctionCallMatch(
Param(
"time_fn",
Or(
[
String("toStartOfHour"),
String("toStartOfMinute"),
String("toStartOfDay"),
String("toDate"),
]
),
),
(column_match,),
with_optionals=True,
)
expr_match = FunctionCallMatch(
String("toDateTime"),
(
FunctionCallMatch(
String("multiply"),
(
FunctionCallMatch(
String("intDiv"),
(
FunctionCallMatch(String("toUInt32"), (column_match,)),
LiteralMatch(Param("granularity", Any(int))),
),
),
LiteralMatch(Param("granularity", Any(int))),
),
),
LiteralMatch(Any(str)),
),
)
for top_expr in groupby:
for expr in top_expr:
result = fn_match.match(expr)
if result is not None:
return GRANULARITY_MAPPING[result.string("time_fn")]
result = expr_match.match(expr)
if result is not None:
return result.integer("granularity")
return None
def get_project_ids_in_condition(
condition: Expression) -> Optional[Set[int]]:
"""
Extract project ids from an expression. Returns None if no project
if condition is found. It returns an empty set of conflicting project_id
conditions are found.
"""
match = FunctionCall(
None,
String(ConditionFunctions.EQ),
(
Column(column_name=String(project_column)),
Literal(value=Param("project_id", Any(int))),
),
).match(condition)
if match is not None:
return {match.integer("project_id")}
match = is_in_condition_pattern(
Column(column_name=String(project_column))).match(condition)
if match is not None:
projects = match.expression("tuple")
assert isinstance(projects, FunctionCallExpr)
return {
l.value
for l in projects.parameters
if isinstance(l, LiteralExpr) and isinstance(l.value, int)
}
match = FunctionCall(
None,
Param(
"operator",
Or([String(BooleanFunctions.AND),
String(BooleanFunctions.OR)]),
),
(Param("lhs", AnyExpression()), Param("rhs", AnyExpression())),
).match(condition)
if match is not None:
lhs_projects = get_project_ids_in_condition(
match.expression("lhs"))
rhs_projects = get_project_ids_in_condition(
match.expression("rhs"))
if lhs_projects is None:
return rhs_projects
elif rhs_projects is None:
return lhs_projects
else:
return (lhs_projects & rhs_projects if match.string("operator")
== BooleanFunctions.AND else lhs_projects
| rhs_projects)
return None
def __init__(self, column_name: str, hash_map_name: str, killswitch: str) -> None:
self.__column_name = column_name
self.__hash_map_name = hash_map_name
self.__killswitch = killswitch
# TODO: Add the support for IN conditions.
self.__optimizable_pattern = FunctionCall(
function_name=String("equals"),
parameters=(
Or(
[
mapping_pattern,
FunctionCall(
function_name=String("ifNull"),
parameters=(mapping_pattern, Literal(String(""))),
),
]
),
Param("right_hand_side", Literal(Any(str))),
),
)
self.__tag_exists_patterns = [
FunctionCall(
function_name=String("notEquals"),
parameters=(
Or(
[
mapping_pattern,
FunctionCall(
function_name=String("ifNull"),
parameters=(mapping_pattern, Literal(String(""))),
),
]
),
Param("right_hand_side", Literal(Any(str))),
),
),
FunctionCall(
function_name=String("has"),
parameters=(
ColumnMatcher(
Param(TABLE_MAPPING_PARAM, AnyOptionalString()),
Param(VALUE_COL_MAPPING_PARAM, String(f"{column_name}.key")),
),
Literal(Param(KEY_MAPPING_PARAM, Any(str))),
),
),
]
def _get_date_range(query: Query) -> Optional[int]:
"""
Best guess to find the time range for the query.
We pick the first column that is compared with a datetime Literal.
"""
pattern = FunctionCall(
Or([String(ConditionFunctions.GT),
String(ConditionFunctions.GTE)]),
(Column(None, Param("col_name", Any(str))), Literal(Any(datetime))),
)
condition = query.get_condition_from_ast()
if condition is None:
return None
for exp in condition:
result = pattern.match(exp)
if result is not None:
from_date, to_date = get_time_range(query,
result.string("col_name"))
if from_date is None or to_date is None:
return None
else:
return (to_date - from_date).days
return None
def get_time_range_estimate(
query: ProcessableQuery[Table],
) -> Tuple[Optional[datetime], Optional[datetime]]:
"""
Best guess to find the time range for the query.
We pick the first column that is compared with a datetime Literal.
"""
pattern = FunctionCall(
Or([String(ConditionFunctions.GT),
String(ConditionFunctions.GTE)]),
(Column(None, Param("col_name", Any(str))), Literal(Any(datetime))),
)
from_date, to_date = None, None
condition = query.get_condition()
if condition is None:
return None, None
for exp in condition:
result = pattern.match(exp)
if result is not None:
from_date, to_date = get_time_range(query,
result.string("col_name"))
break
return from_date, to_date
def process_query(self, query: Query,
query_settings: QuerySettings) -> None:
having_clause = query.get_having()
if not having_clause:
return None
selected_columns = query.get_selected_columns()
uniq_matcher = Param("function", FunctionCallMatch(String("uniq")))
found_functions = []
for exp in having_clause:
match = uniq_matcher.match(exp)
if match is not None:
found_functions.append(match.expression("function"))
if found_functions is not None:
matcher = _ExpressionOrAliasMatcher(found_functions)
for col in selected_columns:
col.expression.accept(matcher)
if not all(matcher.found_expressions):
should_throw = get_config("throw_on_uniq_select_and_having",
False)
error = MismatchedAggregationException(
"Aggregation is in HAVING clause but not SELECT",
query=str(query))
if should_throw:
raise error
else:
logging.warning(
"Aggregation is in HAVING clause but not SELECT",
exc_info=True,
extra=cast(Dict[str, Any], error.to_dict()),
)
def replace_condition(expression: Expression) -> Expression:
match = FunctionCall(
String(OPERATOR_TO_FUNCTION[operator]),
(Param("column", Column(None, String(field))), AnyExpression()),
).match(expression)
return (expression if match is None else replace(
expression, parameters=(match.expression("column"), new_operand)))
def get_time_range(
query: Query,
timestamp_field: str) -> Tuple[Optional[datetime], Optional[datetime]]:
"""
Finds the minimal time range for this query. Which means, it finds
the >= timestamp condition with the highest datetime literal and
the < timestamp condition with the smallest and returns the interval
in the form of a tuple of Literals. It only looks into first level
AND conditions since, if the timestamp is nested in an OR we cannot
say anything on how that compares to the other timestamp conditions.
"""
condition_clause = query.get_condition_from_ast()
if not condition_clause:
return (None, None)
max_lower_bound = None
min_upper_bound = None
for c in get_first_level_and_conditions(condition_clause):
match = FunctionCall(
None,
Param(
"operator",
Or([
String(OPERATOR_TO_FUNCTION[">="]),
String(OPERATOR_TO_FUNCTION["<"]),
]),
),
(
Column(None, None, String(timestamp_field)),
Literal(None, Param("timestamp", Any(datetime))),
),
).match(c)
if match is not None:
timestamp = cast(datetime, match.scalar("timestamp"))
if match.string("operator") == OPERATOR_TO_FUNCTION[">="]:
if not max_lower_bound or timestamp > max_lower_bound:
max_lower_bound = timestamp
else:
if not min_upper_bound or timestamp < min_upper_bound:
min_upper_bound = timestamp
return (max_lower_bound, min_upper_bound)
def extractor(condition: Expression) -> Set[str]:
match = FunctionCall(
String(ConditionFunctions.EQ),
(key_pattern, Literal(Param("key", Any(str)))),
).match(condition)
if match is None:
return set()
return {match.string("key")}
def build_match(
col: str,
ops: Sequence[str],
param_type: Any,
alias: Optional[str] = None,
key: Optional[str] = None,
) -> Or[Expression]:
# The IN condition has to be checked separately since each parameter
# has to be checked individually.
alias_match = AnyOptionalString() if alias is None else String(alias)
pattern: Union[ColumnPattern, SubscriptableReferencePattern]
if key is not None:
pattern = SubscriptableReferencePattern(table_name=alias_match,
column_name=String(col),
key=String(key))
else:
pattern = ColumnPattern(table_name=alias_match,
column_name=String(col))
column_match = Param("column", pattern)
return Or([
FunctionCallPattern(
Or([String(op) for op in ops]),
(column_match, Param("rhs", LiteralPattern(
AnyPattern(param_type)))),
),
FunctionCallPattern(
String(ConditionFunctions.IN),
(
column_match,
Param(
"rhs",
FunctionCallPattern(
Or([String("array"), String("tuple")]),
all_parameters=LiteralPattern(AnyPattern(param_type)),
),
),
),
),
])
def test_pattern_replacer_format_expressions(
unprocessed: Query, expected: Query
) -> None:
def transform(match: MatchResult, exp: Expression) -> Expression:
assert isinstance(exp, Column) # mypy
return FunctionCall(
None, "nullIf", (Column(None, None, exp.column_name), Literal(None, ""),)
)
PatternReplacer(
Param("column", ColumnMatch(None, StringMatch("column1"))), transform,
).process_query(unprocessed, HTTPRequestSettings())
assert expected.get_selected_columns() == unprocessed.get_selected_columns()
def __init__(
self,
column_name: str,
key_names: Sequence[str],
val_names: Sequence[str],
):
super().__init__(column_name, key_names, val_names)
self.__array_join_pattern = FunctionCall(
String("arrayJoin"),
(Column(column_name=Param(
"col",
Or([String(column) for column in self.all_columns]),
), ), ),
)
def formatted_uuid_pattern(self, suffix: str = "") -> FunctionCallMatch:
return FunctionCallMatch(
String("replaceAll"),
(
FunctionCallMatch(
String("toString"),
(
Param(
"formatted_uuid_column" + suffix,
ColumnMatch(None, self.__uuid_column_match),
),
),
),
),
with_optionals=True,
)
def _get_mapping_keys_in_condition(
condition: Expression, column_name: str
) -> Optional[Set[str]]:
"""
Finds the top level conditions that include filter based on the arrayJoin.
This is meant to be used to find the keys the query is filtering the arrayJoin
on.
We can only apply the arrayFilter optimization to arrayJoin conditions
that are not in OR with other columns. To simplify the problem, we only
consider those conditions that are included in the first level of the query:
[['tagskey' '=' 'a'],['col' '=' 'b'],['col2' '=' 'c']] works
[[['tagskey' '=' 'a'], ['col2' '=' 'b']], ['tagskey' '=' 'c']] does not
If we encounter an OR condition we return None, which means we cannot
safely apply the optimization. Empty set means we did not find any
suitable arrayJoin for optimization in this condition but that does
not disqualify the whole query in the way the OR condition does.
"""
keys_found = set()
conditions = get_first_level_and_conditions(condition)
for c in conditions:
if is_binary_condition(c, BooleanFunctions.OR):
return None
match = FunctionCall(
None,
String(ConditionFunctions.EQ),
(array_join_pattern(column_name), Literal(None, Param("key", Any(str)))),
).match(c)
if match is not None:
keys_found.add(match.string("key"))
match = is_in_condition_pattern(array_join_pattern(column_name)).match(c)
if match is not None:
function = match.expression("tuple")
assert isinstance(function, FunctionCallExpr)
keys_found |= {
lit.value
for lit in function.parameters
if isinstance(lit, LiteralExpr) and isinstance(lit.value, str)
}
return keys_found
def __init__(self, column_name: str, hash_map_name: str,
killswitch: str) -> None:
self.__column_name = column_name
self.__hash_map_name = hash_map_name
self.__killswitch = killswitch
# TODO: Add the support for IN connditions.
self.__optimizable_pattern = FunctionCall(
function_name=String("equals"),
parameters=(
Or([
mapping_pattern,
FunctionCall(
function_name=String("ifNull"),
parameters=(mapping_pattern, Literal(String(""))),
),
]),
Param("right_hand_side", Literal(Any(str))),
),
)
def __init__(self, array_columns: Sequence[str]):
self.__array_has_pattern = FunctionCall(
String("equals"),
(
Param(
"has",
FunctionCall(
String("has"),
(
Column(
column_name=Or(
[String(column) for column in array_columns]
)
),
Literal(Any(str)),
),
),
),
Literal(Integer(1)),
),
)
def extractor(condition: Expression) -> Set[Tuple[str, ...]]:
match = FunctionCall(
String(ConditionFunctions.EQ),
(key_pattern, Param("tuple", FunctionCall(String("tuple"), None))),
).match(condition)
if match is None:
return set()
function = match.expression("tuple")
if (
not isinstance(function, FunctionCallExpr)
or function.function_name != "tuple"
):
return set()
parameters = tuple(
param.value
for param in function.parameters
if isinstance(param, LiteralExpr) and isinstance(param.value, str)
)
return {parameters}
def __init__(self, columns: Set[str]):
self.columns = columns
column_match = Or([String(col) for col in columns])
literal = Param("literal", LiteralMatch(AnyMatch(str)))
operator = Param(
"operator",
Or(
[
String(op)
for op in FUNCTION_TO_OPERATOR
if op not in (ConditionFunctions.IN, ConditionFunctions.NOT_IN)
]
),
)
in_operators = Param(
"operator",
Or((String(ConditionFunctions.IN), String(ConditionFunctions.NOT_IN))),
)
col = Param("col", ColumnMatch(None, column_match))
self.__condition_matcher = Or(
[
FunctionCallMatch(operator, (literal, col)),
FunctionCallMatch(operator, (col, literal)),
FunctionCallMatch(Param("operator", String("has")), (col, literal)),
]
)
self.__in_condition_matcher = FunctionCallMatch(
in_operators,
(
col,
Param(
"tuple",
FunctionCallMatch(String("tuple"), all_parameters=LiteralMatch()),
),
),
)
def process_query(self, query: Query,
request_settings: RequestSettings) -> None:
arrayjoin_pattern = FunctionCall(
String("arrayJoin"),
(Column(column_name=Param(
"col",
Or([
String(key_column(self.__column_name)),
String(val_column(self.__column_name)),
]),
), ), ),
)
arrayjoins_in_query = set()
for e in query.get_all_expressions():
match = arrayjoin_pattern.match(e)
if match is not None:
arrayjoins_in_query.add(match.string("col"))
filtered_keys = [
LiteralExpr(None, key)
for key in get_filtered_mapping_keys(query, self.__column_name)
]
# Ensures the alias we apply to the arrayJoin is not already taken.
used_aliases = {exp.alias for exp in query.get_all_expressions()}
pair_alias_root = f"snuba_all_{self.__column_name}"
pair_alias = pair_alias_root
index = 0
while pair_alias in used_aliases:
index += 1
pair_alias = f"{pair_alias_root}_{index}"
def replace_expression(expr: Expression) -> Expression:
"""
Applies the appropriate optimization on a single arrayJoin expression.
"""
match = arrayjoin_pattern.match(expr)
if match is None:
return expr
if arrayjoins_in_query == {
key_column(self.__column_name),
val_column(self.__column_name),
}:
# Both arrayJoin(col.key) and arrayJoin(col.value) expressions
# present int the query. Do the arrayJoin on key-value pairs
# instead of independent arrayjoin for keys and values.
array_index = (LiteralExpr(
None, 1) if match.string("col") == key_column(
self.__column_name) else LiteralExpr(None, 2))
if not filtered_keys:
return _unfiltered_mapping_pairs(expr.alias,
self.__column_name,
pair_alias, array_index)
else:
return _filtered_mapping_pairs(
expr.alias,
self.__column_name,
pair_alias,
filtered_keys,
array_index,
)
elif filtered_keys:
# Only one between arrayJoin(col.key) and arrayJoin(col.value)
# is present, and it is arrayJoin(col.key) since we found
# filtered keys.
return _filtered_mapping_keys(expr.alias, self.__column_name,
filtered_keys)
else:
# No viable optimization
return expr
query.transform_expressions(replace_expression)
LiteralExpr(None, None),
),
)
TABLE_MAPPING_PARAM = "table_name"
VALUE_COL_MAPPING_PARAM = "value_column"
KEY_COL_MAPPING_PARAM = "key_column"
KEY_MAPPING_PARAM = "key"
mapping_pattern = FunctionCall(
None,
String("arrayElement"),
(
Column(
None,
Param(TABLE_MAPPING_PARAM, AnyOptionalString()),
Param(VALUE_COL_MAPPING_PARAM, Any(str)),
),
FunctionCall(
None,
String("indexOf"),
(
Column(None, None, Param(KEY_COL_MAPPING_PARAM, Any(str))),
Literal(None, Param(KEY_MAPPING_PARAM, Any(str))),
),
),
),
)
# TODO: build more of these mappers.
# since SnQL will require an entity to always be specified by the user.
def select_entity(self, query: Query) -> EntityKey:
selected_entity = match_query_to_entity(query, EVENTS_COLUMNS,
TRANSACTIONS_COLUMNS)
track_bad_query(query, selected_entity, EVENTS_COLUMNS,
TRANSACTIONS_COLUMNS)
return selected_entity
metrics = MetricsWrapper(environment.metrics, "api.discover")
logger = logging.getLogger(__name__)
EVENT_CONDITION = FunctionCallMatch(
Param("function", Or([StringMatch(op) for op in BINARY_OPERATORS])),
(
Or([ColumnMatch(None, StringMatch("type")),
LiteralMatch(None)]),
Param("event_type", Or([ColumnMatch(), LiteralMatch()])),
),
)
TRANSACTION_FUNCTIONS = FunctionCallMatch(
Or([StringMatch("apdex"),
StringMatch("failure_rate")]), None)
EVENT_FUNCTIONS = FunctionCallMatch(
Or([StringMatch("isHandled"),
StringMatch("notHandled")]), None)
),
(
"Single node match",
Column(OptionalString("table"), String("test_col")),
ColumnExpr("alias_we_don't_care_of", "table", "test_col"),
MatchResult(),
),
(
"Single node no match",
Column(None, String("test_col")),
ColumnExpr(None, None, "not_a_test_col"),
None,
),
(
"Matches a None table name",
Column(Param("table_name", AnyOptionalString()), None),
ColumnExpr(None, None, "not_a_test_col"),
MatchResult({"table_name": None}),
),
(
"Matches None as table name",
Column(Param("table_name", OptionalString(None)), None),
ColumnExpr(None, None, "not_a_test_col"),
MatchResult({"table_name": None}),
),
(
"Not matching a non None table",
Column(Param("table_name", OptionalString(None)), None),
ColumnExpr(None, "not None", "not_a_test_col"),
None,
),
condition_pattern,
get_first_level_and_conditions,
)
from snuba.query.matchers import Any
from snuba.query.matchers import Column as ColumnPattern
from snuba.query.matchers import FunctionCall as FunctionCallPattern
from snuba.query.matchers import Literal as LiteralPattern
from snuba.query.matchers import Or, Param, Pattern, String
from snuba.request.request_settings import RequestSettings
from snuba.state import get_config
logger = logging.getLogger(__name__)
EQ_CONDITION_PATTERN = condition_pattern(
{ConditionFunctions.EQ},
ColumnPattern(None, Param("lhs", Any(str))),
LiteralPattern(Any(int)),
commutative=True,
)
FULL_CONDITION_PATTERN = Or([
EQ_CONDITION_PATTERN,
FunctionCallPattern(
String(ConditionFunctions.IN),
(
ColumnPattern(None, Param("lhs", Any(str))),
FunctionCallPattern(Or([String("tuple"),
String("array")]), None),
),
),
], )
def __init__(self, time_group_columns: Mapping[str, str],
time_parse_columns: Sequence[str]) -> None:
# Column names that should be mapped to different columns.
self.__time_replace_columns = time_group_columns
# time_parse_columns is a list of columns that, if used in a condition, should be compared with datetimes.
# The columns here might overlap with the columns that get replaced, so we have to search for transformed
# columns.
column_match = ColumnMatch(
None,
Param(
"column_name",
Or([String(tc) for tc in time_parse_columns]),
),
)
self.condition_match = FunctionCallMatch(
Or([
String(ConditionFunctions.GT),
String(ConditionFunctions.GTE),
String(ConditionFunctions.LT),
String(ConditionFunctions.LTE),
String(ConditionFunctions.EQ),
String(ConditionFunctions.NEQ),
]),
(
Or([
column_match,
FunctionCallMatch(
Or([
String("toStartOfHour"),
String("toStartOfMinute"),
String("toStartOfDay"),
String("toDate"),
]),
(column_match, ),
with_optionals=True,
),
FunctionCallMatch(
String("toDateTime"),
(
FunctionCallMatch(
String("multiply"),
(
FunctionCallMatch(
String("intDiv"),
(
FunctionCallMatch(
String("toUInt32"),
(column_match, ),
),
LiteralMatch(Any(int)),
),
),
LiteralMatch(Any(int)),
),
),
LiteralMatch(Any(str)),
),
),
]),
Param("literal", LiteralMatch(Any(str))),
),
)
def validate(self, expression: Expression, schema: ColumnSet) -> None:
raise NotImplementedError
class Any(ParamType):
def validate(self, expression: Expression, schema: ColumnSet) -> None:
return
def __str__(self) -> str:
return "Any"
COLUMN_PATTERN = ColumnMatcher(
alias=None,
table_name=None,
column_name=Param("column_name", AnyMatcher(str)),
)
LITERAL_PATTERN = LiteralMatcher()
AllowedTypes = Union[Type[Array], Type[String], Type[UUID], Type[IPv4],
Type[IPv6], Type[FixedString], Type[UInt], Type[Float],
Type[Date], Type[DateTime], ]
AllowedScalarTypes = Union[Type[None], Type[bool], Type[str], Type[float],
Type[int], Type[date], Type[datetime], ]
class Column(ParamType):
"""
Validates that the type of a Column expression is in a set of