superset/utils/mock_data.py
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# distributed with this work for additional information
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# to you under the Apache License, Version 2.0 (the
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# 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.
import decimal
import logging
import os
import random
import string
import sys
from collections.abc import Iterator
from datetime import date, datetime, time, timedelta
from typing import Any, Callable, cast, Optional, TypedDict
from uuid import uuid4
import sqlalchemy.sql.sqltypes
import sqlalchemy_utils
from flask_appbuilder import Model
from sqlalchemy import Column, inspect, MetaData, Table as DBTable
from sqlalchemy.dialects import postgresql
from sqlalchemy.sql import func
from sqlalchemy.sql.visitors import VisitableType
from superset import db
from superset.sql_parse import Table
from superset.utils import json
logger = logging.getLogger(__name__)
class ColumnInfo(TypedDict):
name: str
type: VisitableType
nullable: bool
default: Optional[Any]
autoincrement: str
primary_key: int
example_column = {
"name": "id",
"type": sqlalchemy.sql.sqltypes.INTEGER(),
"nullable": False,
"default": None,
"autoincrement": "auto",
"primary_key": 1,
}
MINIMUM_DATE = date(1900, 1, 1)
MAXIMUM_DATE = date.today()
days_range = (MAXIMUM_DATE - MINIMUM_DATE).days
def get_type_generator( # pylint: disable=too-many-return-statements,too-many-branches
sqltype: sqlalchemy.sql.sqltypes,
) -> Callable[[], Any]:
if isinstance(sqltype, sqlalchemy.dialects.mysql.types.TINYINT):
return lambda: random.choice([0, 1])
if isinstance(
sqltype, (sqlalchemy.sql.sqltypes.INTEGER, sqlalchemy.sql.sqltypes.Integer)
):
return lambda: random.randrange(2147483647)
if isinstance(sqltype, sqlalchemy.sql.sqltypes.BIGINT):
return lambda: random.randrange(sys.maxsize)
if isinstance(
sqltype, (sqlalchemy.sql.sqltypes.VARCHAR, sqlalchemy.sql.sqltypes.String)
):
length = random.randrange(sqltype.length or 255)
length = max(8, length) # for unique values
length = min(100, length) # for FAB perms
return lambda: "".join(random.choices(string.ascii_letters, k=length))
if isinstance(
sqltype, (sqlalchemy.sql.sqltypes.TEXT, sqlalchemy.sql.sqltypes.Text)
):
length = random.randrange(65535)
# "practicality beats purity"
length = max(length, 2048)
return lambda: "".join(random.choices(string.ascii_letters, k=length))
if isinstance(
sqltype, (sqlalchemy.sql.sqltypes.BOOLEAN, sqlalchemy.sql.sqltypes.Boolean)
):
return lambda: random.choice([True, False])
if isinstance(
sqltype, (sqlalchemy.sql.sqltypes.FLOAT, sqlalchemy.sql.sqltypes.REAL)
):
return lambda: random.uniform(-sys.maxsize, sys.maxsize)
if isinstance(sqltype, sqlalchemy.sql.sqltypes.DATE):
return lambda: MINIMUM_DATE + timedelta(days=random.randrange(days_range))
if isinstance(sqltype, sqlalchemy.sql.sqltypes.TIME):
return lambda: time(
random.randrange(24),
random.randrange(60),
random.randrange(60),
)
if isinstance(
sqltype,
(
sqlalchemy.sql.sqltypes.TIMESTAMP,
sqlalchemy.sql.sqltypes.DATETIME,
sqlalchemy.sql.sqltypes.DateTime,
),
):
return lambda: datetime.fromordinal(MINIMUM_DATE.toordinal()) + timedelta(
seconds=random.randrange(days_range * 86400)
)
if isinstance(sqltype, sqlalchemy.sql.sqltypes.Numeric):
# since decimal is used in some models to store time, return a value that
# is a reasonable timestamp
return lambda: decimal.Decimal(datetime.now().timestamp() * 1000)
if isinstance(sqltype, sqlalchemy.sql.sqltypes.JSON):
return lambda: {
"".join(random.choices(string.ascii_letters, k=8)): random.randrange(65535)
for _ in range(10)
}
if isinstance(
sqltype,
(
sqlalchemy.sql.sqltypes.BINARY,
sqlalchemy_utils.types.encrypted.encrypted_type.EncryptedType,
),
):
length = random.randrange(sqltype.length or 255)
return lambda: os.urandom(length)
if isinstance(sqltype, sqlalchemy_utils.types.uuid.UUIDType):
return uuid4
if isinstance(sqltype, postgresql.base.UUID):
return lambda: str(uuid4())
if isinstance(sqltype, sqlalchemy.sql.sqltypes.BLOB):
length = random.randrange(sqltype.length or 255)
return lambda: os.urandom(length)
logger.warning(
"Unknown type %s. Please add it to `get_type_generator`.", type(sqltype)
)
return lambda: b"UNKNOWN TYPE"
def add_data(
columns: Optional[list[ColumnInfo]],
num_rows: int,
table_name: str,
append: bool = True,
) -> None:
"""
Generate synthetic data for testing migrations and features.
If the table already exists `columns` can be `None`.
:param Optional[List[ColumnInfo]] columns: list of column names and types to create
:param int num_rows: how many rows to generate and insert
:param str table_name: name of table, will be created if it doesn't exist
:param bool append: if the table already exists, append data or replace?
"""
# pylint: disable=import-outside-toplevel
from superset.utils.database import get_example_database
database = get_example_database()
table_exists = database.has_table(Table(table_name))
with database.get_sqla_engine() as engine:
if columns is None:
if not table_exists:
raise Exception( # pylint: disable=broad-exception-raised
f"The table {table_name} does not exist. To create it you need to "
"pass a list of column names and types."
)
inspector = inspect(engine)
columns = inspector.get_columns(table_name)
# create table if needed
column_objects = get_column_objects(columns)
metadata = MetaData()
table = DBTable(table_name, metadata, *column_objects)
metadata.create_all(engine)
if not append:
engine.execute(table.delete())
data = generate_data(columns, num_rows)
engine.execute(table.insert(), data)
def get_column_objects(columns: list[ColumnInfo]) -> list[Column]:
out = []
for column in columns:
kwargs = cast(dict[str, Any], column.copy())
kwargs["type_"] = kwargs.pop("type")
out.append(Column(**kwargs))
return out
def generate_data(columns: list[ColumnInfo], num_rows: int) -> list[dict[str, Any]]:
keys = [column["name"] for column in columns]
return [
dict(zip(keys, row))
for row in zip(*[generate_column_data(column, num_rows) for column in columns])
]
def generate_column_data(column: ColumnInfo, num_rows: int) -> list[Any]:
gen = get_type_generator(column["type"])
return [gen() for _ in range(num_rows)]
def add_sample_rows(model: type[Model], count: int) -> Iterator[Model]:
"""
Add entities of a given model.
:param Model model: a Superset/FAB model
:param int count: how many entities to generate and insert
"""
inspector = inspect(model)
# select samples to copy relationship values
relationships = inspector.relationships.items()
samples = db.session.query(model).limit(count).all() if relationships else []
max_primary_key: Optional[int] = None
for i in range(count):
sample = samples[i % len(samples)] if samples else None
kwargs = {}
for column in inspector.columns.values():
# for primary keys, keep incrementing
if column.primary_key:
if max_primary_key is None:
max_primary_key = (
db.session.query(func.max(getattr(model, column.name))).scalar()
or 0
)
max_primary_key += 1
kwargs[column.name] = max_primary_key
# if the column has a foreign key, copy the value from an existing entity
elif column.foreign_keys:
if sample:
kwargs[column.name] = getattr(sample, column.name)
else:
kwargs[column.name] = get_valid_foreign_key(column)
# should be an enum but it's not
elif column.name == "datasource_type":
kwargs[column.name] = "table"
# otherwise, generate a random value based on the type
else:
kwargs[column.name] = generate_value(column)
entity = model(**kwargs)
yield entity
def get_valid_foreign_key(column: Column) -> Any:
foreign_key = list(column.foreign_keys)[0]
table_name, column_name = foreign_key.target_fullname.split(".", 1)
return db.engine.execute(f"SELECT {column_name} FROM {table_name} LIMIT 1").scalar()
def generate_value(column: Column) -> Any:
if hasattr(column.type, "enums"):
return random.choice(column.type.enums)
json_as_string = "json" in column.name.lower() and isinstance(
column.type, sqlalchemy.sql.sqltypes.Text
)
type_ = sqlalchemy.sql.sqltypes.JSON() if json_as_string else column.type
value = get_type_generator(type_)()
if json_as_string:
value = json.dumps(value)
return value