def test_querying_table(metadata):
"""
Create an object for test table.
"""
# When using pytest-xdist, we don't want concurrent table creations
# across test processes so we assign a unique name for table based on
# the current worker id.
worker_id = os.environ.get('PYTEST_XDIST_WORKER', 'master')
return Table(
'test_querying_table_' + worker_id,
metadata,
Column('id', types.Integer, autoincrement=True, primary_key=True),
Column('t_string', types.String(60), onupdate='updated'),
Column('t_list', types.ARRAY(types.String(60))),
Column('t_enum', types.Enum(MyEnum)),
Column('t_int_enum', types.Enum(MyIntEnum)),
Column('t_datetime', types.DateTime()),
Column('t_date', types.DateTime()),
Column('t_interval', types.Interval()),
Column('uniq_uuid',
PG_UUID,
nullable=False,
unique=True,
default=uuid4),
)
class BaseEngineSpec: # pylint: disable=too-many-public-methods
"""Abstract class for database engine specific configurations"""
engine = "base" # str as defined in sqlalchemy.engine.engine
engine_aliases: Optional[Tuple[str]] = None
engine_name: Optional[
str
] = None # used for user messages, overridden in child classes
_date_trunc_functions: Dict[str, str] = {}
_time_grain_expressions: Dict[Optional[str], str] = {}
column_type_mappings: Tuple[
Tuple[
Pattern[str],
Union[TypeEngine, Callable[[Match[str]], TypeEngine]],
GenericDataType,
],
...,
] = (
(
re.compile(r"^smallint", re.IGNORECASE),
types.SmallInteger(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^int.*", re.IGNORECASE),
types.Integer(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^bigint", re.IGNORECASE),
types.BigInteger(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^decimal", re.IGNORECASE),
types.Numeric(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^numeric", re.IGNORECASE),
types.Numeric(),
GenericDataType.NUMERIC,
),
(re.compile(r"^real", re.IGNORECASE), types.REAL, GenericDataType.NUMERIC,),
(
re.compile(r"^smallserial", re.IGNORECASE),
types.SmallInteger(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^serial", re.IGNORECASE),
types.Integer(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^bigserial", re.IGNORECASE),
types.BigInteger(),
GenericDataType.NUMERIC,
),
(
re.compile(r"^string", re.IGNORECASE),
types.String(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^N((VAR)?CHAR|TEXT)", re.IGNORECASE),
UnicodeText(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^((VAR)?CHAR|TEXT|STRING)", re.IGNORECASE),
String(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^datetime", re.IGNORECASE),
types.DateTime(),
GenericDataType.TEMPORAL,
),
(re.compile(r"^date", re.IGNORECASE), types.Date(), GenericDataType.TEMPORAL,),
(
re.compile(r"^timestamp", re.IGNORECASE),
types.TIMESTAMP(),
GenericDataType.TEMPORAL,
),
(
re.compile(r"^interval", re.IGNORECASE),
types.Interval(),
GenericDataType.TEMPORAL,
),
(re.compile(r"^time", re.IGNORECASE), types.Time(), GenericDataType.TEMPORAL,),
(
re.compile(r"^bool.*", re.IGNORECASE),
types.Boolean(),
GenericDataType.BOOLEAN,
),
)
time_groupby_inline = False
limit_method = LimitMethod.FORCE_LIMIT
time_secondary_columns = False
allows_joins = True
allows_subqueries = True
allows_alias_in_select = True
allows_alias_in_orderby = True
allows_sql_comments = True
force_column_alias_quotes = False
arraysize = 0
max_column_name_length = 0
try_remove_schema_from_table_name = True # pylint: disable=invalid-name
run_multiple_statements_as_one = False
@classmethod
def get_dbapi_exception_mapping(cls) -> Dict[Type[Exception], Type[Exception]]:
"""
Each engine can implement and converge its own specific exceptions into
Superset DBAPI exceptions
Note: On python 3.9 this method can be changed to a classmethod property
without the need of implementing a metaclass type
:return: A map of driver specific exception to superset custom exceptions
"""
return {}
@classmethod
def get_dbapi_mapped_exception(cls, exception: Exception) -> Exception:
"""
Get a superset custom DBAPI exception from the driver specific exception.
Override if the engine needs to perform extra changes to the exception, for
example change the exception message or implement custom more complex logic
:param exception: The driver specific exception
:return: Superset custom DBAPI exception
"""
new_exception = cls.get_dbapi_exception_mapping().get(type(exception))
if not new_exception:
return exception
return new_exception(str(exception))
@classmethod
def get_allow_cost_estimate(cls, extra: Dict[str, Any]) -> bool:
return False
@classmethod
def get_engine(
cls,
database: "Database",
schema: Optional[str] = None,
source: Optional[str] = None,
) -> Engine:
user_name = utils.get_username()
return database.get_sqla_engine(
schema=schema, nullpool=True, user_name=user_name, source=source
)
@classmethod
def get_timestamp_expr(
cls,
col: ColumnClause,
pdf: Optional[str],
time_grain: Optional[str],
type_: Optional[str] = None,
) -> TimestampExpression:
"""
Construct a TimestampExpression to be used in a SQLAlchemy query.
:param col: Target column for the TimestampExpression
:param pdf: date format (seconds or milliseconds)
:param time_grain: time grain, e.g. P1Y for 1 year
:param type_: the source column type
:return: TimestampExpression object
"""
if time_grain:
time_expr = cls.get_time_grain_expressions().get(time_grain)
if not time_expr:
raise NotImplementedError(
f"No grain spec for {time_grain} for database {cls.engine}"
)
if type_ and "{func}" in time_expr:
date_trunc_function = cls._date_trunc_functions.get(type_)
if date_trunc_function:
time_expr = time_expr.replace("{func}", date_trunc_function)
if type_ and "{type}" in time_expr:
date_trunc_function = cls._date_trunc_functions.get(type_)
if date_trunc_function:
time_expr = time_expr.replace("{type}", type_)
else:
time_expr = "{col}"
# if epoch, translate to DATE using db specific conf
if pdf == "epoch_s":
time_expr = time_expr.replace("{col}", cls.epoch_to_dttm())
elif pdf == "epoch_ms":
time_expr = time_expr.replace("{col}", cls.epoch_ms_to_dttm())
return TimestampExpression(time_expr, col, type_=DateTime)
@classmethod
def get_time_grains(cls) -> Tuple[TimeGrain, ...]:
"""
Generate a tuple of supported time grains.
:return: All time grains supported by the engine
"""
ret_list = []
time_grains = builtin_time_grains.copy()
time_grains.update(config["TIME_GRAIN_ADDONS"])
for duration, func in cls.get_time_grain_expressions().items():
if duration in time_grains:
name = time_grains[duration]
ret_list.append(TimeGrain(name, _(name), func, duration))
return tuple(ret_list)
@classmethod
def _sort_time_grains(
cls, val: Tuple[Optional[str], str], index: int
) -> Union[float, int, str]:
"""
Return an ordered time-based value of a portion of a time grain
for sorting
Values are expected to be either None or start with P or PT
Have a numerical value in the middle and end with
a value for the time interval
It can also start or end with epoch start time denoting a range
i.e, week beginning or ending with a day
"""
pos = {
"FIRST": 0,
"SECOND": 1,
"THIRD": 2,
"LAST": 3,
}
if val[0] is None:
return pos["FIRST"]
prog = re.compile(r"(.*\/)?(P|PT)([0-9\.]+)(S|M|H|D|W|M|Y)(\/.*)?")
result = prog.match(val[0])
# for any time grains that don't match the format, put them at the end
if result is None:
return pos["LAST"]
second_minute_hour = ["S", "M", "H"]
day_week_month_year = ["D", "W", "M", "Y"]
is_less_than_day = result.group(2) == "PT"
interval = result.group(4)
epoch_time_start_string = result.group(1) or result.group(5)
has_starting_or_ending = bool(len(epoch_time_start_string or ""))
def sort_day_week() -> int:
if has_starting_or_ending:
return pos["LAST"]
if is_less_than_day:
return pos["SECOND"]
return pos["THIRD"]
def sort_interval() -> float:
if is_less_than_day:
return second_minute_hour.index(interval)
return day_week_month_year.index(interval)
# 0: all "PT" values should come before "P" values (i.e, PT10M)
# 1: order values within the above arrays ("D" before "W")
# 2: sort by numeric value (PT10M before PT15M)
# 3: sort by any week starting/ending values
plist = {
0: sort_day_week(),
1: pos["SECOND"] if is_less_than_day else pos["THIRD"],
2: sort_interval(),
3: float(result.group(3)),
}
return plist.get(index, 0)
@classmethod
def get_time_grain_expressions(cls) -> Dict[Optional[str], str]:
"""
Return a dict of all supported time grains including any potential added grains
but excluding any potentially disabled grains in the config file.
:return: All time grain expressions supported by the engine
"""
# TODO: use @memoize decorator or similar to avoid recomputation on every call
time_grain_expressions = cls._time_grain_expressions.copy()
grain_addon_expressions = config["TIME_GRAIN_ADDON_EXPRESSIONS"]
time_grain_expressions.update(grain_addon_expressions.get(cls.engine, {}))
denylist: List[str] = config["TIME_GRAIN_DENYLIST"]
for key in denylist:
time_grain_expressions.pop(key)
return dict(
sorted(
time_grain_expressions.items(),
key=lambda x: (
cls._sort_time_grains(x, 0),
cls._sort_time_grains(x, 1),
cls._sort_time_grains(x, 2),
cls._sort_time_grains(x, 3),
),
)
)
@classmethod
def make_select_compatible(
cls, groupby_exprs: Dict[str, ColumnElement], select_exprs: List[ColumnElement]
) -> List[ColumnElement]:
"""
Some databases will just return the group-by field into the select, but don't
allow the group-by field to be put into the select list.
:param groupby_exprs: mapping between column name and column object
:param select_exprs: all columns in the select clause
:return: columns to be included in the final select clause
"""
return select_exprs
@classmethod
def fetch_data(
cls, cursor: Any, limit: Optional[int] = None
) -> List[Tuple[Any, ...]]:
"""
:param cursor: Cursor instance
:param limit: Maximum number of rows to be returned by the cursor
:return: Result of query
"""
if cls.arraysize:
cursor.arraysize = cls.arraysize
try:
if cls.limit_method == LimitMethod.FETCH_MANY and limit:
return cursor.fetchmany(limit)
return cursor.fetchall()
except Exception as ex:
raise cls.get_dbapi_mapped_exception(ex)
@classmethod
def expand_data(
cls, columns: List[Dict[Any, Any]], data: List[Dict[Any, Any]]
) -> Tuple[List[Dict[Any, Any]], List[Dict[Any, Any]], List[Dict[Any, Any]]]:
"""
Some engines support expanding nested fields. See implementation in Presto
spec for details.
:param columns: columns selected in the query
:param data: original data set
:return: list of all columns(selected columns and their nested fields),
expanded data set, listed of nested fields
"""
return columns, data, []
@classmethod
def alter_new_orm_column(cls, orm_col: "TableColumn") -> None:
"""Allow altering default column attributes when first detected/added
For instance special column like `__time` for Druid can be
set to is_dttm=True. Note that this only gets called when new
columns are detected/created"""
# TODO: Fix circular import caused by importing TableColumn
@classmethod
def epoch_to_dttm(cls) -> str:
"""
SQL expression that converts epoch (seconds) to datetime that can be used in a
query. The reference column should be denoted as `{col}` in the return
expression, e.g. "FROM_UNIXTIME({col})"
:return: SQL Expression
"""
raise NotImplementedError()
@classmethod
def epoch_ms_to_dttm(cls) -> str:
"""
SQL expression that converts epoch (milliseconds) to datetime that can be used
in a query.
:return: SQL Expression
"""
return cls.epoch_to_dttm().replace("{col}", "({col}/1000)")
@classmethod
def get_datatype(cls, type_code: Any) -> Optional[str]:
"""
Change column type code from cursor description to string representation.
:param type_code: Type code from cursor description
:return: String representation of type code
"""
if isinstance(type_code, str) and type_code != "":
return type_code.upper()
return None
@classmethod
def normalize_indexes(cls, indexes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Normalizes indexes for more consistency across db engines
noop by default
:param indexes: Raw indexes as returned by SQLAlchemy
:return: cleaner, more aligned index definition
"""
return indexes
@classmethod
def extra_table_metadata(
cls, database: "Database", table_name: str, schema_name: str
) -> Dict[str, Any]:
"""
Returns engine-specific table metadata
:param database: Database instance
:param table_name: Table name
:param schema_name: Schema name
:return: Engine-specific table metadata
"""
# TODO: Fix circular import caused by importing Database
return {}
@classmethod
def apply_limit_to_sql(cls, sql: str, limit: int, database: "Database") -> str:
"""
Alters the SQL statement to apply a LIMIT clause
:param sql: SQL query
:param limit: Maximum number of rows to be returned by the query
:param database: Database instance
:return: SQL query with limit clause
"""
# TODO: Fix circular import caused by importing Database
if cls.limit_method == LimitMethod.WRAP_SQL:
sql = sql.strip("\t\n ;")
qry = (
select("*")
.select_from(TextAsFrom(text(sql), ["*"]).alias("inner_qry"))
.limit(limit)
)
return database.compile_sqla_query(qry)
if cls.limit_method == LimitMethod.FORCE_LIMIT:
parsed_query = sql_parse.ParsedQuery(sql)
sql = parsed_query.set_or_update_query_limit(limit)
return sql
@classmethod
def get_limit_from_sql(cls, sql: str) -> Optional[int]:
"""
Extract limit from SQL query
:param sql: SQL query
:return: Value of limit clause in query
"""
parsed_query = sql_parse.ParsedQuery(sql)
return parsed_query.limit
@classmethod
def set_or_update_query_limit(cls, sql: str, limit: int) -> str:
"""
Create a query based on original query but with new limit clause
:param sql: SQL query
:param limit: New limit to insert/replace into query
:return: Query with new limit
"""
parsed_query = sql_parse.ParsedQuery(sql)
return parsed_query.set_or_update_query_limit(limit)
@staticmethod
def csv_to_df(**kwargs: Any) -> pd.DataFrame:
"""Read csv into Pandas DataFrame
:param kwargs: params to be passed to DataFrame.read_csv
:return: Pandas DataFrame containing data from csv
"""
kwargs["encoding"] = "utf-8"
kwargs["iterator"] = True
chunks = pd.read_csv(**kwargs)
df = pd.concat(chunk for chunk in chunks)
return df
@classmethod
def df_to_sql(cls, df: pd.DataFrame, **kwargs: Any) -> None:
"""Upload data from a Pandas DataFrame to a database. For
regular engines this calls the DataFrame.to_sql() method. Can be
overridden for engines that don't work well with to_sql(), e.g.
BigQuery.
:param df: Dataframe with data to be uploaded
:param kwargs: kwargs to be passed to to_sql() method
"""
df.to_sql(**kwargs)
@classmethod
def create_table_from_csv( # pylint: disable=too-many-arguments
cls,
filename: str,
table: Table,
database: "Database",
csv_to_df_kwargs: Dict[str, Any],
df_to_sql_kwargs: Dict[str, Any],
) -> None:
"""
Create table from contents of a csv. Note: this method does not create
metadata for the table.
"""
df = cls.csv_to_df(filepath_or_buffer=filename, **csv_to_df_kwargs)
engine = cls.get_engine(database)
if table.schema:
# only add schema when it is preset and non empty
df_to_sql_kwargs["schema"] = table.schema
if engine.dialect.supports_multivalues_insert:
df_to_sql_kwargs["method"] = "multi"
cls.df_to_sql(df=df, con=engine, **df_to_sql_kwargs)
@classmethod
def convert_dttm(cls, target_type: str, dttm: datetime) -> Optional[str]:
"""
Convert Python datetime object to a SQL expression
:param target_type: The target type of expression
:param dttm: The datetime object
:return: The SQL expression
"""
return None
@classmethod
def create_table_from_excel( # pylint: disable=too-many-arguments
cls,
filename: str,
table: Table,
database: "Database",
excel_to_df_kwargs: Dict[str, Any],
df_to_sql_kwargs: Dict[str, Any],
) -> None:
"""
Create table from contents of a excel. Note: this method does not create
metadata for the table.
"""
df = pd.read_excel(io=filename, **excel_to_df_kwargs)
engine = cls.get_engine(database)
if table.schema:
# only add schema when it is preset and non empty
df_to_sql_kwargs["schema"] = table.schema
if engine.dialect.supports_multivalues_insert:
df_to_sql_kwargs["method"] = "multi"
cls.df_to_sql(df=df, con=engine, **df_to_sql_kwargs)
@classmethod
def get_all_datasource_names(
cls, database: "Database", datasource_type: str
) -> List[utils.DatasourceName]:
"""Returns a list of all tables or views in database.
:param database: Database instance
:param datasource_type: Datasource_type can be 'table' or 'view'
:return: List of all datasources in database or schema
"""
# TODO: Fix circular import caused by importing Database
schemas = database.get_all_schema_names(
cache=database.schema_cache_enabled,
cache_timeout=database.schema_cache_timeout,
force=True,
)
all_datasources: List[utils.DatasourceName] = []
for schema in schemas:
if datasource_type == "table":
all_datasources += database.get_all_table_names_in_schema(
schema=schema,
force=True,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout,
)
elif datasource_type == "view":
all_datasources += database.get_all_view_names_in_schema(
schema=schema,
force=True,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout,
)
else:
raise Exception(f"Unsupported datasource_type: {datasource_type}")
return all_datasources
@classmethod
def handle_cursor(cls, cursor: Any, query: Query, session: Session) -> None:
"""Handle a live cursor between the execute and fetchall calls
The flow works without this method doing anything, but it allows
for handling the cursor and updating progress information in the
query object"""
# TODO: Fix circular import error caused by importing sql_lab.Query
@classmethod
def extract_error_message(cls, ex: Exception) -> str:
return f"{cls.engine} error: {cls._extract_error_message(ex)}"
@classmethod
def _extract_error_message(cls, ex: Exception) -> str:
"""Extract error message for queries"""
return utils.error_msg_from_exception(ex)
@classmethod
def extract_errors(cls, ex: Exception) -> List[Dict[str, Any]]:
return [
dataclasses.asdict(
SupersetError(
error_type=SupersetErrorType.GENERIC_DB_ENGINE_ERROR,
message=cls._extract_error_message(ex),
level=ErrorLevel.ERROR,
extra={"engine_name": cls.engine_name},
)
)
]
@classmethod
def adjust_database_uri(cls, uri: URL, selected_schema: Optional[str]) -> None:
"""
Mutate the database component of the SQLAlchemy URI.
The URI here represents the URI as entered when saving the database,
``selected_schema`` is the schema currently active presumably in
the SQL Lab dropdown. Based on that, for some database engine,
we can return a new altered URI that connects straight to the
active schema, meaning the users won't have to prefix the object
names by the schema name.
Some databases engines have 2 level of namespacing: database and
schema (postgres, oracle, mssql, ...)
For those it's probably better to not alter the database
component of the URI with the schema name, it won't work.
Some database drivers like presto accept '{catalog}/{schema}' in
the database component of the URL, that can be handled here.
"""
@classmethod
def patch(cls) -> None:
"""
TODO: Improve docstring and refactor implementation in Hive
"""
@classmethod
def get_schema_names(cls, inspector: Inspector) -> List[str]:
"""
Get all schemas from database
:param inspector: SqlAlchemy inspector
:return: All schemas in the database
"""
return sorted(inspector.get_schema_names())
@classmethod
def get_table_names(
cls, database: "Database", inspector: Inspector, schema: Optional[str]
) -> List[str]:
"""
Get all tables from schema
:param inspector: SqlAlchemy inspector
:param schema: Schema to inspect. If omitted, uses default schema for database
:return: All tables in schema
"""
tables = inspector.get_table_names(schema)
if schema and cls.try_remove_schema_from_table_name:
tables = [re.sub(f"^{schema}\\.", "", table) for table in tables]
return sorted(tables)
@classmethod
def get_view_names(
cls, database: "Database", inspector: Inspector, schema: Optional[str]
) -> List[str]:
"""
Get all views from schema
:param inspector: SqlAlchemy inspector
:param schema: Schema name. If omitted, uses default schema for database
:return: All views in schema
"""
views = inspector.get_view_names(schema)
if schema and cls.try_remove_schema_from_table_name:
views = [re.sub(f"^{schema}\\.", "", view) for view in views]
return sorted(views)
@classmethod
def get_table_comment(
cls, inspector: Inspector, table_name: str, schema: Optional[str]
) -> Optional[str]:
"""
Get comment of table from a given schema and table
:param inspector: SqlAlchemy Inspector instance
:param table_name: Table name
:param schema: Schema name. If omitted, uses default schema for database
:return: comment of table
"""
comment = None
try:
comment = inspector.get_table_comment(table_name, schema)
comment = comment.get("text") if isinstance(comment, dict) else None
except NotImplementedError:
# It's expected that some dialects don't implement the comment method
pass
except Exception as ex: # pylint: disable=broad-except
logger.error("Unexpected error while fetching table comment")
logger.exception(ex)
return comment
@classmethod
def get_columns(
cls, inspector: Inspector, table_name: str, schema: Optional[str]
) -> List[Dict[str, Any]]:
"""
Get all columns from a given schema and table
:param inspector: SqlAlchemy Inspector instance
:param table_name: Table name
:param schema: Schema name. If omitted, uses default schema for database
:return: All columns in table
"""
return inspector.get_columns(table_name, schema)
@classmethod
def where_latest_partition( # pylint: disable=too-many-arguments
cls,
table_name: str,
schema: Optional[str],
database: "Database",
query: Select,
columns: Optional[List[Dict[str, str]]] = None,
) -> Optional[Select]:
"""
Add a where clause to a query to reference only the most recent partition
:param table_name: Table name
:param schema: Schema name
:param database: Database instance
:param query: SqlAlchemy query
:param columns: List of TableColumns
:return: SqlAlchemy query with additional where clause referencing latest
partition
"""
# TODO: Fix circular import caused by importing Database, TableColumn
return None
@classmethod
def _get_fields(cls, cols: List[Dict[str, Any]]) -> List[Any]:
return [column(c["name"]) for c in cols]
@classmethod
def select_star( # pylint: disable=too-many-arguments,too-many-locals
cls,
database: "Database",
table_name: str,
engine: Engine,
schema: Optional[str] = None,
limit: int = 100,
show_cols: bool = False,
indent: bool = True,
latest_partition: bool = True,
cols: Optional[List[Dict[str, Any]]] = None,
) -> str:
"""
Generate a "SELECT * from [schema.]table_name" query with appropriate limit.
WARNING: expects only unquoted table and schema names.
:param database: Database instance
:param table_name: Table name, unquoted
:param engine: SqlALchemy Engine instance
:param schema: Schema, unquoted
:param limit: limit to impose on query
:param show_cols: Show columns in query; otherwise use "*"
:param indent: Add indentation to query
:param latest_partition: Only query latest partition
:param cols: Columns to include in query
:return: SQL query
"""
fields: Union[str, List[Any]] = "*"
cols = cols or []
if (show_cols or latest_partition) and not cols:
cols = database.get_columns(table_name, schema)
if show_cols:
fields = cls._get_fields(cols)
quote = engine.dialect.identifier_preparer.quote
if schema:
full_table_name = quote(schema) + "." + quote(table_name)
else:
full_table_name = quote(table_name)
qry = select(fields).select_from(text(full_table_name))
if limit:
qry = qry.limit(limit)
if latest_partition:
partition_query = cls.where_latest_partition(
table_name, schema, database, qry, columns=cols
)
if partition_query is not None:
qry = partition_query
sql = database.compile_sqla_query(qry)
if indent:
sql = sqlparse.format(sql, reindent=True)
return sql
@classmethod
def estimate_statement_cost(cls, statement: str, cursor: Any,) -> Dict[str, Any]:
"""
Generate a SQL query that estimates the cost of a given statement.
:param statement: A single SQL statement
:param cursor: Cursor instance
:return: Dictionary with different costs
"""
raise Exception("Database does not support cost estimation")
@classmethod
def query_cost_formatter(
cls, raw_cost: List[Dict[str, Any]]
) -> List[Dict[str, str]]:
"""
Format cost estimate.
:param raw_cost: Raw estimate from `estimate_query_cost`
:return: Human readable cost estimate
"""
raise Exception("Database does not support cost estimation")
@classmethod
def process_statement(
cls, statement: str, database: "Database", user_name: str
) -> str:
"""
Process a SQL statement by stripping and mutating it.
:param statement: A single SQL statement
:param database: Database instance
:param username: Effective username
:return: Dictionary with different costs
"""
parsed_query = ParsedQuery(statement)
sql = parsed_query.stripped()
sql_query_mutator = config["SQL_QUERY_MUTATOR"]
if sql_query_mutator:
sql = sql_query_mutator(sql, user_name, security_manager, database)
return sql
@classmethod
def estimate_query_cost(
cls, database: "Database", schema: str, sql: str, source: Optional[str] = None
) -> List[Dict[str, Any]]:
"""
Estimate the cost of a multiple statement SQL query.
:param database: Database instance
:param schema: Database schema
:param sql: SQL query with possibly multiple statements
:param source: Source of the query (eg, "sql_lab")
"""
extra = database.get_extra() or {}
if not cls.get_allow_cost_estimate(extra):
raise Exception("Database does not support cost estimation")
user_name = g.user.username if g.user else None
parsed_query = sql_parse.ParsedQuery(sql)
statements = parsed_query.get_statements()
engine = cls.get_engine(database, schema=schema, source=source)
costs = []
with closing(engine.raw_connection()) as conn:
cursor = conn.cursor()
for statement in statements:
processed_statement = cls.process_statement(
statement, database, user_name
)
costs.append(cls.estimate_statement_cost(processed_statement, cursor))
return costs
@classmethod
def modify_url_for_impersonation(
cls, url: URL, impersonate_user: bool, username: Optional[str]
) -> None:
"""
Modify the SQL Alchemy URL object with the user to impersonate if applicable.
:param url: SQLAlchemy URL object
:param impersonate_user: Flag indicating if impersonation is enabled
:param username: Effective username
"""
if impersonate_user and username is not None:
url.username = username
@classmethod
def update_impersonation_config(
cls, connect_args: Dict[str, Any], uri: str, username: Optional[str],
) -> None:
"""
Update a configuration dictionary
that can set the correct properties for impersonating users
:param connect_args: config to be updated
:param uri: URI
:param impersonate_user: Flag indicating if impersonation is enabled
:param username: Effective username
:return: None
"""
@classmethod
def execute(cls, cursor: Any, query: str, **kwargs: Any) -> None:
"""
Execute a SQL query
:param cursor: Cursor instance
:param query: Query to execute
:param kwargs: kwargs to be passed to cursor.execute()
:return:
"""
if not cls.allows_sql_comments:
query = sql_parse.strip_comments_from_sql(query)
if cls.arraysize:
cursor.arraysize = cls.arraysize
try:
cursor.execute(query)
except Exception as ex:
raise cls.get_dbapi_mapped_exception(ex)
@classmethod
def make_label_compatible(cls, label: str) -> Union[str, quoted_name]:
"""
Conditionally mutate and/or quote a sqlalchemy expression label. If
force_column_alias_quotes is set to True, return the label as a
sqlalchemy.sql.elements.quoted_name object to ensure that the select query
and query results have same case. Otherwise return the mutated label as a
regular string. If maxmimum supported column name length is exceeded,
generate a truncated label by calling truncate_label().
:param label: expected expression label/alias
:return: conditionally mutated label supported by the db engine
"""
label_mutated = cls._mutate_label(label)
if (
cls.max_column_name_length
and len(label_mutated) > cls.max_column_name_length
):
label_mutated = cls._truncate_label(label)
if cls.force_column_alias_quotes:
label_mutated = quoted_name(label_mutated, True)
return label_mutated
@classmethod
def get_sqla_column_type(
cls,
column_type: Optional[str],
column_type_mappings: Tuple[
Tuple[
Pattern[str],
Union[TypeEngine, Callable[[Match[str]], TypeEngine]],
GenericDataType,
],
...,
] = column_type_mappings,
) -> Union[Tuple[TypeEngine, GenericDataType], None]:
"""
Return a sqlalchemy native column type that corresponds to the column type
defined in the data source (return None to use default type inferred by
SQLAlchemy). Override `column_type_mappings` for specific needs
(see MSSQL for example of NCHAR/NVARCHAR handling).
:param column_type: Column type returned by inspector
:return: SqlAlchemy column type
"""
if not column_type:
return None
for regex, sqla_type, generic_type in column_type_mappings:
match = regex.match(column_type)
if match:
if callable(sqla_type):
return sqla_type(match), generic_type
return sqla_type, generic_type
return None
@staticmethod
def _mutate_label(label: str) -> str:
"""
Most engines support mixed case aliases that can include numbers
and special characters, like commas, parentheses etc. For engines that
have restrictions on what types of aliases are supported, this method
can be overridden to ensure that labels conform to the engine's
limitations. Mutated labels should be deterministic (input label A always
yields output label X) and unique (input labels A and B don't yield the same
output label X).
:param label: Preferred expression label
:return: Conditionally mutated label
"""
return label
@classmethod
def _truncate_label(cls, label: str) -> str:
"""
In the case that a label exceeds the max length supported by the engine,
this method is used to construct a deterministic and unique label based on
the original label. By default this returns an md5 hash of the original label,
conditionally truncated if the length of the hash exceeds the max column length
of the engine.
:param label: Expected expression label
:return: Truncated label
"""
label = hashlib.md5(label.encode("utf-8")).hexdigest()
# truncate hash if it exceeds max length
if cls.max_column_name_length and len(label) > cls.max_column_name_length:
label = label[: cls.max_column_name_length]
return label
@classmethod
def column_datatype_to_string(
cls, sqla_column_type: TypeEngine, dialect: Dialect
) -> str:
"""
Convert sqlalchemy column type to string representation.
By default removes collation and character encoding info to avoid unnecessarily
long datatypes.
:param sqla_column_type: SqlAlchemy column type
:param dialect: Sqlalchemy dialect
:return: Compiled column type
"""
sqla_column_type = sqla_column_type.copy()
if hasattr(sqla_column_type, "collation"):
sqla_column_type.collation = None
if hasattr(sqla_column_type, "charset"):
sqla_column_type.charset = None
return sqla_column_type.compile(dialect=dialect).upper()
@classmethod
def get_function_names(cls, database: "Database") -> List[str]:
"""
Get a list of function names that are able to be called on the database.
Used for SQL Lab autocomplete.
:param database: The database to get functions for
:return: A list of function names useable in the database
"""
return []
@staticmethod
def pyodbc_rows_to_tuples(data: List[Any]) -> List[Tuple[Any, ...]]:
"""
Convert pyodbc.Row objects from `fetch_data` to tuples.
:param data: List of tuples or pyodbc.Row objects
:return: List of tuples
"""
if data and type(data[0]).__name__ == "Row":
data = [tuple(row) for row in data]
return data
@staticmethod
def mutate_db_for_connection_test(database: "Database") -> None:
"""
Some databases require passing additional parameters for validating database
connections. This method makes it possible to mutate the database instance prior
to testing if a connection is ok.
:param database: instance to be mutated
"""
return None
@staticmethod
def get_extra_params(database: "Database") -> Dict[str, Any]:
"""
Some databases require adding elements to connection parameters,
like passing certificates to `extra`. This can be done here.
:param database: database instance from which to extract extras
:raises CertificateException: If certificate is not valid/unparseable
"""
extra: Dict[str, Any] = {}
if database.extra:
try:
extra = json.loads(database.extra)
except json.JSONDecodeError as ex:
logger.error(ex)
raise ex
return extra
@classmethod
def is_readonly_query(cls, parsed_query: ParsedQuery) -> bool:
"""Pessimistic readonly, 100% sure statement won't mutate anything"""
return (
parsed_query.is_select()
or parsed_query.is_explain()
or parsed_query.is_show()
)
@classmethod
def get_column_spec(
cls,
native_type: Optional[str],
source: utils.ColumnTypeSource = utils.ColumnTypeSource.GET_TABLE,
column_type_mappings: Tuple[
Tuple[
Pattern[str],
Union[TypeEngine, Callable[[Match[str]], TypeEngine]],
GenericDataType,
],
...,
] = column_type_mappings,
) -> Union[ColumnSpec, None]:
"""
Converts native database type to sqlalchemy column type.
:param native_type: Native database typee
:param source: Type coming from the database table or cursor description
:return: ColumnSpec object
"""
col_types = cls.get_sqla_column_type(
native_type, column_type_mappings=column_type_mappings
)
if col_types:
column_type, generic_type = col_types
# wrap temporal types in custom type that supports literal binding
# using datetimes
if generic_type == GenericDataType.TEMPORAL:
column_type = literal_dttm_type_factory(
type(column_type), cls, native_type or ""
)
is_dttm = generic_type == GenericDataType.TEMPORAL
return ColumnSpec(
sqla_type=column_type, generic_type=generic_type, is_dttm=is_dttm
)
return None
default=t_enum_default),
Column('t_int_enum',
types.Enum(MyIntEnum),
nullable=False,
default=t_int_enum_default),
Column('t_datetime',
types.DateTime(),
nullable=False,
default=t_datetime_default),
Column('t_date', types.DateTime(), nullable=False, default=t_date_default),
Column('t_date_2',
types.DateTime(),
nullable=False,
default=t_date_2_default),
Column('t_interval',
types.Interval(),
nullable=False,
default=t_interval_default),
Column('t_boolean', types.Boolean(), nullable=False, default=True),
Column('version', PG_UUID, default=uuid4, onupdate=uuid4))
def test_insert_query_defaults():
query = users.insert()
new_query, new_params = connection.compile_query(query)
serial_default = query.parameters.get('serial')
assert serial_default.name == 'nextval'
assert serial_default.clause_expr.element.clauses[0].value == 'serial_seq'
assert query.parameters.get('name') == name_default
assert query.parameters.get('t_list') == t_list_default
assert query.parameters.get('t_enum') == t_enum_default