def test_sql_field_attributes(self):
#
# test a generic SQLA field type
#
column_1 = schema.Column(types.Unicode(), nullable=False)
fa_1 = EntityAdmin.get_sql_field_attributes([column_1])
self.assertTrue(fa_1['editable'])
self.assertFalse(fa_1['nullable'])
self.assertEqual(fa_1['delegate'], delegates.PlainTextDelegate)
#
# test sql standard types
#
column_2 = schema.Column(types.FLOAT(), nullable=True)
fa_2 = EntityAdmin.get_sql_field_attributes([column_2])
self.assertTrue(fa_2['editable'])
self.assertTrue(fa_2['nullable'])
self.assertEqual(fa_2['delegate'], delegates.FloatDelegate)
#
# test a vendor specific field type
#
from sqlalchemy.dialects import mysql
column_3 = schema.Column(mysql.BIGINT(), default=2)
fa_3 = EntityAdmin.get_sql_field_attributes([column_3])
self.assertTrue(fa_3['default'])
self.assertEqual(fa_3['delegate'], delegates.IntegerDelegate)
def _fixed_lookup_fixture(self):
return [
(sqltypes.String(), sqltypes.VARCHAR()),
(sqltypes.String(1), sqltypes.VARCHAR(1)),
(sqltypes.String(3), sqltypes.VARCHAR(3)),
(sqltypes.Text(), sqltypes.TEXT()),
(sqltypes.Unicode(), sqltypes.VARCHAR()),
(sqltypes.Unicode(1), sqltypes.VARCHAR(1)),
(sqltypes.UnicodeText(), sqltypes.TEXT()),
(sqltypes.CHAR(3), sqltypes.CHAR(3)),
(sqltypes.NUMERIC, sqltypes.NUMERIC()),
(sqltypes.NUMERIC(10, 2), sqltypes.NUMERIC(10, 2)),
(sqltypes.Numeric, sqltypes.NUMERIC()),
(sqltypes.Numeric(10, 2), sqltypes.NUMERIC(10, 2)),
(sqltypes.DECIMAL, sqltypes.DECIMAL()),
(sqltypes.DECIMAL(10, 2), sqltypes.DECIMAL(10, 2)),
(sqltypes.INTEGER, sqltypes.INTEGER()),
(sqltypes.BIGINT, sqltypes.BIGINT()),
(sqltypes.Float, sqltypes.FLOAT()),
(sqltypes.TIMESTAMP, sqltypes.TIMESTAMP()),
(sqltypes.DATETIME, sqltypes.DATETIME()),
(sqltypes.DateTime, sqltypes.DATETIME()),
(sqltypes.DateTime(), sqltypes.DATETIME()),
(sqltypes.DATE, sqltypes.DATE()),
(sqltypes.Date, sqltypes.DATE()),
(sqltypes.TIME, sqltypes.TIME()),
(sqltypes.Time, sqltypes.TIME()),
(sqltypes.BOOLEAN, sqltypes.BOOLEAN()),
(sqltypes.Boolean, sqltypes.BOOLEAN()),
]
def import_data(
df: pd.DataFrame,
engine: engine,
table: str,
):
print("start importing...")
df.replace([np.inf, -np.inf], np.nan,
inplace=True) # 因分母为0除法产生的inf和-inf替换成nan
df["RSP"] = df["RSP"].apply(
lambda x: ",".join(x.tolist())
if type(x) != str and type(x) != float else x) # RSP字段转为字符串存储
df.loc[:,
["POTENTIAL_DOT", "MAT_SALES", "SHARE"]].fillna(0) # 数值字段的空值都替换为0
df.to_sql(
table,
con=engine,
if_exists="replace",
index=False,
dtype={
"HP_ID": t.NVARCHAR(length=10),
"HP_NAME": t.NVARCHAR(length=100),
"HOSPITAL": t.NVARCHAR(length=110),
"PROVINCE": t.NVARCHAR(length=3),
"CITY": t.NVARCHAR(length=30),
"COUNTY": t.NVARCHAR(length=30),
"POTENTIAL_DOT": t.FLOAT(),
"DATA_SOURCE": t.NVARCHAR(length=30),
"HP_TYPE": t.NVARCHAR(length=4),
"DECILE_HP": t.INTEGER(),
"DECILE_CM": t.INTEGER(),
"DECILE": t.INTEGER(),
"DECILE_TOTAL": t.INTEGER(),
"MAT_SALES": t.FLOAT(),
"ANNUAL_TARGET": t.FLOAT(),
"RSP": t.NVARCHAR(length=100),
"BU": t.NVARCHAR(length=3),
"RD": t.NVARCHAR(length=3),
"RM": t.NVARCHAR(length=20),
"AM": t.NVARCHAR(length=20),
"STATUS": t.NVARCHAR(length=7),
"SHARE": t.FLOAT(),
"SHARE_GROUP": t.NVARCHAR(length=11),
},
)
def cast_col(df, output_types):
for field in df.columns:
if isinstance(output_types[field], type(db_types.FLOAT())):
df[field] = df[field].astype(float)
if isinstance(output_types[field], type(db_types.DATE())):
df[field] = df[field].astype('datetime64[ns]')
# if isinstance(output_types[field], type(db_types.INT())):
# df[field] = df[field].astype(int) -> this "if" leads to pb in prod when they are none value + handled properly in prod with string inputs being converted to int.
return df
def get_spark_type(field, required_type):
if isinstance(required_type, type(db_types.DATE())):
return spk_types.StructField(field, spk_types.DateType(), True)
elif isinstance(required_type, type(db_types.DATETIME())):
return spk_types.StructField(field, spk_types.TimestampType(), True)
elif isinstance(required_type, type(db_types.VARCHAR())):
return spk_types.StructField(field, spk_types.StringType(), True)
elif isinstance(required_type, type(db_types.INT())):
return spk_types.StructField(
field, spk_types.LongType(), True
) # db type enforced earlier than spark ones, so spark types needs to be less restrictive than spark ones so needs to choose LongType instead of IntegerType
elif isinstance(required_type, type(db_types.FLOAT())):
return spk_types.StructField(field, spk_types.FloatType(), True)
elif isinstance(required_type, type(db_types.BOOLEAN())):
return spk_types.StructField(field, spk_types.BooleanType(), True)
else:
raise Exception(
"Type not recognized, field={}, required_type={}".format(
field, required_type))
def cast_value(value, required_type, field_name):
# TODO: make it less ugly.. or avoid using pandas to not require this.
try:
if isinstance(required_type, type(db_types.DATE())):
if isinstance(value, str):
return datetime.strptime(value,
"%Y-%m-%d") # assuming iso format
elif isinstance(value, pd.Timestamp): # == datetime
return value.to_pydatetime().date()
elif isinstance(value, date):
return value
elif pd.isnull(value):
return None
else:
return required_type.python_type(value)
if isinstance(required_type, type(db_types.DATETIME())):
if isinstance(value, str):
return datetime.strptime(
value, "%Y-%m-%d %H:%M:%S") # assuming iso format
elif isinstance(value, pd.Timestamp):
return value.to_pydatetime()
elif pd.isnull(value):
return None
else:
return required_type.python_type(value)
elif isinstance(required_type, type(db_types.VARCHAR())):
return None if pd.isnull(value) else str(value)
elif isinstance(required_type, type(db_types.INT())):
return None if pd.isnull(value) else int(float(value))
elif isinstance(required_type, type(db_types.BIGINT())):
return None if pd.isnull(value) else long(value)
elif isinstance(required_type, type(db_types.FLOAT())):
return None if pd.isnull(value) else float(value)
else:
return required_type.python_type(value)
except Exception as e:
logger.error(u"cast_value issue: {}, {}, {}, {}, {}.".format(
field_name, value, type(value), required_type, str(e)))
return None
def _fixed_lookup_fixture(self):
return [
(sqltypes.String(), sqltypes.VARCHAR()),
(sqltypes.String(1), sqltypes.VARCHAR(1)),
(sqltypes.String(3), sqltypes.VARCHAR(3)),
(sqltypes.Text(), sqltypes.TEXT()),
(sqltypes.Unicode(), sqltypes.VARCHAR()),
(sqltypes.Unicode(1), sqltypes.VARCHAR(1)),
(sqltypes.UnicodeText(), sqltypes.TEXT()),
(sqltypes.CHAR(3), sqltypes.CHAR(3)),
(sqltypes.NUMERIC, sqltypes.NUMERIC()),
(sqltypes.NUMERIC(10, 2), sqltypes.NUMERIC(10, 2)),
(sqltypes.Numeric, sqltypes.NUMERIC()),
(sqltypes.Numeric(10, 2), sqltypes.NUMERIC(10, 2)),
(sqltypes.DECIMAL, sqltypes.DECIMAL()),
(sqltypes.DECIMAL(10, 2), sqltypes.DECIMAL(10, 2)),
(sqltypes.INTEGER, sqltypes.INTEGER()),
(sqltypes.BIGINT, sqltypes.BIGINT()),
(sqltypes.Float, sqltypes.FLOAT()),
(sqltypes.TIMESTAMP, sqltypes.TIMESTAMP()),
(sqltypes.DATETIME, sqltypes.DATETIME()),
(sqltypes.DateTime, sqltypes.DATETIME()),
(sqltypes.DateTime(), sqltypes.DATETIME()),
(sqltypes.DATE, sqltypes.DATE()),
(sqltypes.Date, sqltypes.DATE()),
(sqltypes.TIME, sqltypes.TIME()),
(sqltypes.Time, sqltypes.TIME()),
(sqltypes.BOOLEAN, sqltypes.BOOLEAN()),
(sqltypes.Boolean, sqltypes.BOOLEAN()),
(sqlite.DATE(storage_format="%(year)04d%(month)02d%(day)02d", ),
sqltypes.DATE()),
(sqlite.TIME(
storage_format="%(hour)02d%(minute)02d%(second)02d", ),
sqltypes.TIME()),
(sqlite.DATETIME(storage_format="%(year)04d%(month)02d%(day)02d"
"%(hour)02d%(minute)02d%(second)02d", ),
sqltypes.DATETIME()),
]
def setFrameSql(server, database, dataframe, username, password):
'''
Actualiza la tabla de SQL a partir de un Dataframe
'''
# engine = create_engine('mssql+pymssql://' + server + '/' + database)
engine = create_engine('mssql+pymssql://' + username + ':' + password +
'@' + server + '/' + database)
con = engine.connect()
dataframe.to_sql('RF_Cupones',
con,
if_exists='replace',
index=False,
schema='dbo',
dtype={
'Instrumento': types.VARCHAR(length=100),
'moneda': types.VARCHAR(length=100),
'Fecha_corte': types.DATE(),
'Cantidad_corte': types.FLOAT(),
'Vencimiento': types.DATE()
})
con.close()
def setUpClass(cls):
col1 = {
'name': 'col1',
'type': types.VARCHAR(length=11),
'nullable': True,
'default': None
}
col2 = {
'name': 'col2',
'type': types.DATE(),
'nullable': False,
'default': None
}
col3 = {
'name': 'col3',
'type': types.INTEGER(),
'nullable': True,
'default': None
}
col4 = {
'name': 'col4',
'type': types.FLOAT(),
'nullable': True,
'default': None
}
col5 = {
'name': 'col5',
'type': types.CHAR(length=10, collation='Latin1_General_CI_AS'),
'nullable': True,
'default': None
}
cols = [col1, col2, col3, col4, col5]
cls.info = I2FVG.Info(name='tbl_prova',
unique=['pk1', 'pk2'],
keys=[],
foreign=[],
columns=cols)
def column(self,tablename,mode=1,quotechar=False):
cur = self.conn.cursor()
if mode==1:
sql = f" select COLUMN_NAME from user_tab_columns where table_name='{tablename.upper()}'"
cur.execute(sql)
columns = []
for row in cur.fetchall():
columns.append(row[0])
if quotechar:
columns = list(map(lambda x: f"'{x}'", columns))
elif mode==2:
from sqlalchemy import types
sql = f" select COLUMN_NAME,DATA_TYPE,DATA_LENGTH,DATA_PRECISION,DATA_SCALE from user_tab_columns where table_name='{tablename.upper()}'"
cur.execute(sql)
columns={}
for COLUMN_NAME,DATA_TYPE,DATA_LENGTH,DATA_PRECISION,DATA_SCALE in cur.fetchall():
if DATA_TYPE=='VARCHAR2':
columns[COLUMN_NAME]=types.VARCHAR(DATA_LENGTH)
elif DATA_TYPE=='NVARCHAR2':
columns[COLUMN_NAME] = types.NVARCHAR()
elif DATA_TYPE=='CHAR':
columns[COLUMN_NAME] = types.CHAR
elif DATA_TYPE=='DATE':
columns[COLUMN_NAME] = types.DateTime()
elif DATA_TYPE=='NUMBER':
if DATA_SCALE is not None:
columns[COLUMN_NAME]=types.FLOAT(DATA_SCALE)
else :
columns[COLUMN_NAME] = types.INT
else:
#TODO 抛出异常，不识别的类型，需要继续补充if判断
pass
cur.close()
return columns
def dtype_to_sqldtype(self, df):
"""
Creates a dict of SQL-dtypes to pass to the pd.to_sql() command.
Args:
df (DataFrame): Dataframe with data to be converted.
Returns:
sqldtype_dict (dict): Dict with SQL-dtype per column.
"""
sqldtype_dict = {}
for i, j in zip(df.columns, df.dtypes):
if "object" in str(j):
sqldtype_dict.update({i: sql_dtype.VARCHAR(12)})
if "datetime" in str(j):
sqldtype_dict.update({i: sql_dtype.DateTime()})
if "float" in str(j):
sqldtype_dict.update({i: sql_dtype.FLOAT(precision=12)})
if "int" in str(j):
sqldtype_dict.update({i: sql_dtype.INT()})
return sqldtype_dict
unquoted_types = (
(
"BINARY",
types.LargeBinary(),
),
(
"BOOLEAN",
types.Boolean(),
),
(
"DECIMAL",
types.DECIMAL(),
),
(
"FLOAT",
types.FLOAT(),
),
(
"INTEGER",
types.INTEGER(),
),
(
"BIGINT",
types.BIGINT(),
),
(
"NUMERIC",
types.NUMERIC(),
),
)
"HP_ID": t.NVARCHAR(length=10), "HP_NAME": t.NVARCHAR(length=100), "HOSPITAL": t.NVARCHAR(length=110), "STORE_ID": t.NVARCHAR(length=10), "STORE_NAME": t.NVARCHAR(length=100), "STORE": t.NVARCHAR(length=110), "PROVINCE": t.NVARCHAR(length=3), "CITY": t.NVARCHAR(length=30), "COUNTY": t.NVARCHAR(length=30), "LEVEL": t.NVARCHAR(length=4), "IF_COMMUNITY": t.Boolean(), "IF_DUALCALL": t.Boolean(), "PRODUCT": t.NVARCHAR(length=10), "STRENGTH": t.NVARCHAR(length=10), "TAG": t.NVARCHAR(length=4), "VOLUME": t.FLOAT(), "VOLUME_STD": t.FLOAT(), "VALUE": t.FLOAT(), "BU": t.NVARCHAR(length=10), "RD": t.NVARCHAR(length=10), "RM": t.NVARCHAR(length=20), "RM_POS_NAME": t.NVARCHAR(length=40), "DSM": t.NVARCHAR(length=20), "DSM_POS_NAME": t.NVARCHAR(length=40), "RSP": t.NVARCHAR(length=20), "RSP_POS_NAME": t.NVARCHAR(length=40), "PRODUCT_GROUP_RM": t.NVARCHAR(length=10), "PRODUCT_GROUP_DSM": t.NVARCHAR(length=10), "PRODUCT_GROUP_RSP": t.NVARCHAR(length=10), "RM_ID": t.NVARCHAR(length=20), "RM_NAME": t.NVARCHAR(length=20),
df2 = df_volume[df_volume['TEMP'].str.isnumeric() == False]
df1['TEMP'] = df1['TEMP'].apply(np.int64)
df1['AMOUNT'] = df1['AMOUNT'] * df1['TEMP']
df_volume = pd.concat([df1, df2])
df_volume.drop('TEMP', axis=1, inplace=True)
df_combined = pd.concat([df, df_volume])
print(df_combined)
print('start importing...')
df_combined.to_sql('data',
con=engine,
if_exists='replace',
index=False,
dtype={
'DATE': t.DateTime(),
'AMOUNT': t.FLOAT(),
'TC I': t.NVARCHAR(length=200),
'TC II': t.NVARCHAR(length=200),
'TC III': t.NVARCHAR(length=200),
'TC IV': t.NVARCHAR(length=200),
'MOLECULE': t.NVARCHAR(length=200),
'PRODUCT': t.NVARCHAR(length=200),
'PACKAGE': t.NVARCHAR(length=200),
'CORPORATION': t.NVARCHAR(length=200),
'MANUF_TYPE': t.NVARCHAR(length=20),
'FORMULATION': t.NVARCHAR(length=50),
'STRENGTH': t.NVARCHAR(length=20),
'UNIT': t.NVARCHAR(length=25),
'PERIOD': t.NVARCHAR(length=3),
'MOLECULE_TC': t.NVARCHAR(length=255),
'PRODUCT_CORP': t.NVARCHAR(length=255),
from sqlalchemy import MetaData, Table, Column
from sqlalchemy import types
from sqlalchemy.schema import CreateTable, DropTable
TYPE_MAP = {
'bigint': types.BigInteger(),
'boolean': types.Boolean(),
'date': types.DATE(),
'timestamp': types.TIMESTAMP(),
'datetime': types.TIMESTAMP(),
'double precision': types.FLOAT(),
'enum': types.String(length=256),
'guid': types.String(length=256),
'int': types.Integer(),
'integer': types.Integer(),
'text': types.Text(),
}
def ddl_from_json(schema_json):
"""
This function takes the schema definition in JSON format that's returned by
the Canvas Data API and returns SQL DDL statements that can be used to create
all of the tables necessary to hold the archived data.
"""
metadata = MetaData()
create_ddl = []
drop_ddl = []
for artifact in schema_json:
table_name = schema_json[artifact]['tableName']
json_columns = schema_json[artifact]['columns']
def float(**kwargs):
return _vary(types.FLOAT(), float_map.copy(), kwargs)
df1["TEMP"] = df1["TEMP"].apply(np.int64)
df1["AMOUNT"] = df1["AMOUNT"] * df1["TEMP"]
df_volume = pd.concat([df1, df2])
df_volume.drop("TEMP", axis=1, inplace=True)
df_combined = pd.concat([df, df_volume])
print(df_combined)
print("start importing...")
df_combined.to_sql(
"data",
con=engine,
if_exists="replace",
index=False,
dtype={
"DATE": t.DateTime(),
"AMOUNT": t.FLOAT(),
"TC I": t.NVARCHAR(length=200),
"TC II": t.NVARCHAR(length=200),
"TC III": t.NVARCHAR(length=200),
"TC IV": t.NVARCHAR(length=200),
"MOLECULE": t.NVARCHAR(length=250),
"PRODUCT": t.NVARCHAR(length=250),
"PACKAGE": t.NVARCHAR(length=250),
"CORPORATION": t.NVARCHAR(length=250),
"MANUF_TYPE": t.NVARCHAR(length=20),
"FORMULATION": t.NVARCHAR(length=50),
"STRENGTH": t.NVARCHAR(length=20),
"UNIT": t.NVARCHAR(length=25),
"PERIOD": t.NVARCHAR(length=3),
"MOLECULE_TC": t.NVARCHAR(length=255),
"PRODUCT_CORP": t.NVARCHAR(length=255),
class PrestoEngineSpec(BaseEngineSpec): # pylint: disable=too-many-public-methods
engine = "presto"
engine_name = "Presto"
allows_alias_to_source_column = False
_time_grain_expressions = {
None:
"{col}",
"PT1S":
"date_trunc('second', CAST({col} AS TIMESTAMP))",
"PT1M":
"date_trunc('minute', CAST({col} AS TIMESTAMP))",
"PT1H":
"date_trunc('hour', CAST({col} AS TIMESTAMP))",
"P1D":
"date_trunc('day', CAST({col} AS TIMESTAMP))",
"P1W":
"date_trunc('week', CAST({col} AS TIMESTAMP))",
"P1M":
"date_trunc('month', CAST({col} AS TIMESTAMP))",
"P0.25Y":
"date_trunc('quarter', CAST({col} AS TIMESTAMP))",
"P1Y":
"date_trunc('year', CAST({col} AS TIMESTAMP))",
"P1W/1970-01-03T00:00:00Z":
"date_add('day', 5, date_trunc('week', "
"date_add('day', 1, CAST({col} AS TIMESTAMP))))",
"1969-12-28T00:00:00Z/P1W":
"date_add('day', -1, date_trunc('week', "
"date_add('day', 1, CAST({col} AS TIMESTAMP))))",
}
@classmethod
def get_allow_cost_estimate(cls, extra: Dict[str, Any]) -> bool:
version = extra.get("version")
return version is not None and StrictVersion(version) >= StrictVersion(
"0.319")
@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 string
:param impersonate_user: Flag indicating if impersonation is enabled
:param username: Effective username
:return: None
"""
url = make_url(uri)
backend_name = url.get_backend_name()
# Must be Presto connection, enable impersonation, and set optional param
# auth=LDAP|KERBEROS
# Set principal_username=$effective_username
if backend_name == "presto" and username is not None:
connect_args["principal_username"] = username
@classmethod
def get_table_names(cls, database: "Database", inspector: Inspector,
schema: Optional[str]) -> List[str]:
tables = super().get_table_names(database, inspector, schema)
if not is_feature_enabled("PRESTO_SPLIT_VIEWS_FROM_TABLES"):
return tables
views = set(cls.get_view_names(database, inspector, schema))
actual_tables = set(tables) - views
return list(actual_tables)
@classmethod
def get_view_names(cls, database: "Database", inspector: Inspector,
schema: Optional[str]) -> List[str]:
"""Returns an empty list
get_table_names() function returns all table names and view names,
and get_view_names() is not implemented in sqlalchemy_presto.py
https://github.com/dropbox/PyHive/blob/e25fc8440a0686bbb7a5db5de7cb1a77bdb4167a/pyhive/sqlalchemy_presto.py
"""
if not is_feature_enabled("PRESTO_SPLIT_VIEWS_FROM_TABLES"):
return []
if schema:
sql = ("SELECT table_name FROM information_schema.views "
"WHERE table_schema=%(schema)s")
params = {"schema": schema}
else:
sql = "SELECT table_name FROM information_schema.views"
params = {}
engine = cls.get_engine(database, schema=schema)
with closing(engine.raw_connection()) as conn:
cursor = conn.cursor()
cursor.execute(sql, params)
results = cursor.fetchall()
return [row[0] for row in results]
@classmethod
def _create_column_info(cls, name: str,
data_type: types.TypeEngine) -> Dict[str, Any]:
"""
Create column info object
:param name: column name
:param data_type: column data type
:return: column info object
"""
return {"name": name, "type": f"{data_type}"}
@classmethod
def _get_full_name(cls, names: List[Tuple[str, str]]) -> str:
"""
Get the full column name
:param names: list of all individual column names
:return: full column name
"""
return ".".join(column[0] for column in names if column[0])
@classmethod
def _has_nested_data_types(cls, component_type: str) -> bool:
"""
Check if string contains a data type. We determine if there is a data type by
whitespace or multiple data types by commas
:param component_type: data type
:return: boolean
"""
comma_regex = r",(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
white_space_regex = r"\s(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
return (re.search(comma_regex, component_type) is not None
or re.search(white_space_regex, component_type) is not None)
@classmethod
def _split_data_type(cls, data_type: str, delimiter: str) -> List[str]:
"""
Split data type based on given delimiter. Do not split the string if the
delimiter is enclosed in quotes
:param data_type: data type
:param delimiter: string separator (i.e. open parenthesis, closed parenthesis,
comma, whitespace)
:return: list of strings after breaking it by the delimiter
"""
return re.split(
r"{}(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)".format(delimiter), data_type)
@classmethod
def _parse_structural_column( # pylint: disable=too-many-locals,too-many-branches
cls,
parent_column_name: str,
parent_data_type: str,
result: List[Dict[str, Any]],
) -> None:
"""
Parse a row or array column
:param result: list tracking the results
"""
formatted_parent_column_name = parent_column_name
# Quote the column name if there is a space
if " " in parent_column_name:
formatted_parent_column_name = f'"{parent_column_name}"'
full_data_type = f"{formatted_parent_column_name} {parent_data_type}"
original_result_len = len(result)
# split on open parenthesis ( to get the structural
# data type and its component types
data_types = cls._split_data_type(full_data_type, r"\(")
stack: List[Tuple[str, str]] = []
for data_type in data_types:
# split on closed parenthesis ) to track which component
# types belong to what structural data type
inner_types = cls._split_data_type(data_type, r"\)")
for inner_type in inner_types:
# We have finished parsing multiple structural data types
if not inner_type and stack:
stack.pop()
elif cls._has_nested_data_types(inner_type):
# split on comma , to get individual data types
single_fields = cls._split_data_type(inner_type, ",")
for single_field in single_fields:
single_field = single_field.strip()
# If component type starts with a comma, the first single field
# will be an empty string. Disregard this empty string.
if not single_field:
continue
# split on whitespace to get field name and data type
field_info = cls._split_data_type(single_field, r"\s")
# check if there is a structural data type within
# overall structural data type
column_spec = cls.get_column_spec(field_info[1])
column_type = column_spec.sqla_type if column_spec else None
if column_type is None:
column_type = types.String()
logger.info(
"Did not recognize type %s of column %s",
field_info[1],
field_info[0],
)
if field_info[1] == "array" or field_info[1] == "row":
stack.append((field_info[0], field_info[1]))
full_parent_path = cls._get_full_name(stack)
result.append(
cls._create_column_info(
full_parent_path, column_type))
else: # otherwise this field is a basic data type
full_parent_path = cls._get_full_name(stack)
column_name = "{}.{}".format(
full_parent_path, field_info[0])
result.append(
cls._create_column_info(
column_name, column_type))
# If the component type ends with a structural data type, do not pop
# the stack. We have run across a structural data type within the
# overall structural data type. Otherwise, we have completely parsed
# through the entire structural data type and can move on.
if not (inner_type.endswith("array")
or inner_type.endswith("row")):
stack.pop()
# We have an array of row objects (i.e. array(row(...)))
elif inner_type in ("array", "row"):
# Push a dummy object to represent the structural data type
stack.append(("", inner_type))
# We have an array of a basic data types(i.e. array(varchar)).
elif stack:
# Because it is an array of a basic data type. We have finished
# parsing the structural data type and can move on.
stack.pop()
# Unquote the column name if necessary
if formatted_parent_column_name != parent_column_name:
for index in range(original_result_len, len(result)):
result[index]["name"] = result[index]["name"].replace(
formatted_parent_column_name, parent_column_name)
@classmethod
def _show_columns(cls, inspector: Inspector, table_name: str,
schema: Optional[str]) -> List[RowProxy]:
"""
Show presto column names
:param inspector: object that performs database schema inspection
:param table_name: table name
:param schema: schema name
:return: list of column objects
"""
quote = inspector.engine.dialect.identifier_preparer.quote_identifier
full_table = quote(table_name)
if schema:
full_table = "{}.{}".format(quote(schema), full_table)
columns = inspector.bind.execute(
"SHOW COLUMNS FROM {}".format(full_table))
return columns
column_type_mappings = (
(
re.compile(r"^boolean.*", re.IGNORECASE),
types.BOOLEAN,
utils.GenericDataType.BOOLEAN,
),
(
re.compile(r"^tinyint.*", re.IGNORECASE),
TinyInteger(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^smallint.*", re.IGNORECASE),
types.SMALLINT(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^integer.*", re.IGNORECASE),
types.INTEGER(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^bigint.*", re.IGNORECASE),
types.BIGINT(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^real.*", re.IGNORECASE),
types.FLOAT(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^double.*", re.IGNORECASE),
types.FLOAT(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^decimal.*", re.IGNORECASE),
types.DECIMAL(),
utils.GenericDataType.NUMERIC,
),
(
re.compile(r"^varchar(\((\d+)\))*$", re.IGNORECASE),
lambda match: types.VARCHAR(int(match[2]))
if match[2] else types.String(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^char(\((\d+)\))*$", re.IGNORECASE),
lambda match: types.CHAR(int(match[2]))
if match[2] else types.CHAR(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^varbinary.*", re.IGNORECASE),
types.VARBINARY(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^json.*", re.IGNORECASE),
types.JSON(),
utils.GenericDataType.STRING,
),
(
re.compile(r"^date.*", re.IGNORECASE),
types.DATE(),
utils.GenericDataType.TEMPORAL,
),
(
re.compile(r"^timestamp.*", re.IGNORECASE),
types.TIMESTAMP(),
utils.GenericDataType.TEMPORAL,
),
(
re.compile(r"^interval.*", re.IGNORECASE),
Interval(),
utils.GenericDataType.TEMPORAL,
),
(
re.compile(r"^time.*", re.IGNORECASE),
types.Time(),
utils.GenericDataType.TEMPORAL,
),
(re.compile(r"^array.*",
re.IGNORECASE), Array(), utils.GenericDataType.STRING),
(re.compile(r"^map.*",
re.IGNORECASE), Map(), utils.GenericDataType.STRING),
(re.compile(r"^row.*",
re.IGNORECASE), Row(), utils.GenericDataType.STRING),
)
@classmethod
def get_columns(cls, inspector: Inspector, table_name: str,
schema: Optional[str]) -> List[Dict[str, Any]]:
"""
Get columns from a Presto data source. This includes handling row and
array data types
:param inspector: object that performs database schema inspection
:param table_name: table name
:param schema: schema name
:return: a list of results that contain column info
(i.e. column name and data type)
"""
columns = cls._show_columns(inspector, table_name, schema)
result: List[Dict[str, Any]] = []
for column in columns:
# parse column if it is a row or array
if is_feature_enabled("PRESTO_EXPAND_DATA") and (
"array" in column.Type or "row" in column.Type):
structural_column_index = len(result)
cls._parse_structural_column(column.Column, column.Type,
result)
result[structural_column_index]["nullable"] = getattr(
column, "Null", True)
result[structural_column_index]["default"] = None
continue
# otherwise column is a basic data type
column_spec = cls.get_column_spec(column.Type)
column_type = column_spec.sqla_type if column_spec else None
if column_type is None:
column_type = types.String()
logger.info(
"Did not recognize type %s of column %s",
str(column.Type),
str(column.Column),
)
column_info = cls._create_column_info(column.Column, column_type)
column_info["nullable"] = getattr(column, "Null", True)
column_info["default"] = None
result.append(column_info)
return result
@classmethod
def _is_column_name_quoted(cls, column_name: str) -> bool:
"""
Check if column name is in quotes
:param column_name: column name
:return: boolean
"""
return column_name.startswith('"') and column_name.endswith('"')
@classmethod
def _get_fields(cls, cols: List[Dict[str, Any]]) -> List[ColumnClause]:
"""
Format column clauses where names are in quotes and labels are specified
:param cols: columns
:return: column clauses
"""
column_clauses = []
# Column names are separated by periods. This regex will find periods in a
# string if they are not enclosed in quotes because if a period is enclosed in
# quotes, then that period is part of a column name.
dot_pattern = r"""\. # split on period
(?= # look ahead
(?: # create non-capture group
[^\"]*\"[^\"]*\" # two quotes
)*[^\"]*$) # end regex"""
dot_regex = re.compile(dot_pattern, re.VERBOSE)
for col in cols:
# get individual column names
col_names = re.split(dot_regex, col["name"])
# quote each column name if it is not already quoted
for index, col_name in enumerate(col_names):
if not cls._is_column_name_quoted(col_name):
col_names[index] = '"{}"'.format(col_name)
quoted_col_name = ".".join(
col_name if cls._is_column_name_quoted(col_name
) else f'"{col_name}"'
for col_name in col_names)
# create column clause in the format "name"."name" AS "name.name"
column_clause = literal_column(quoted_col_name).label(col["name"])
column_clauses.append(column_clause)
return column_clauses
@classmethod
def select_star( # pylint: disable=too-many-arguments
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:
"""
Include selecting properties of row objects. We cannot easily break arrays into
rows, so render the whole array in its own row and skip columns that correspond
to an array's contents.
"""
cols = cols or []
presto_cols = cols
if is_feature_enabled("PRESTO_EXPAND_DATA") and show_cols:
dot_regex = r"\.(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
presto_cols = [
col for col in presto_cols
if not re.search(dot_regex, col["name"])
]
return super().select_star(
database,
table_name,
engine,
schema,
limit,
show_cols,
indent,
latest_partition,
presto_cols,
)
@classmethod
def estimate_statement_cost( # pylint: disable=too-many-locals
cls, statement: str, cursor: Any) -> Dict[str, Any]:
"""
Run a SQL query that estimates the cost of a given statement.
:param statement: A single SQL statement
:param database: Database instance
:param cursor: Cursor instance
:param username: Effective username
:return: JSON response from Presto
"""
sql = f"EXPLAIN (TYPE IO, FORMAT JSON) {statement}"
cursor.execute(sql)
# the output from Presto is a single column and a single row containing
# JSON:
#
# {
# ...
# "estimate" : {
# "outputRowCount" : 8.73265878E8,
# "outputSizeInBytes" : 3.41425774958E11,
# "cpuCost" : 3.41425774958E11,
# "maxMemory" : 0.0,
# "networkCost" : 3.41425774958E11
# }
# }
result = json.loads(cursor.fetchone()[0])
return result
@classmethod
def query_cost_formatter(
cls, raw_cost: List[Dict[str, Any]]) -> List[Dict[str, str]]:
"""
Format cost estimate.
:param raw_cost: JSON estimate from Presto
:return: Human readable cost estimate
"""
def humanize(value: Any, suffix: str) -> str:
try:
value = int(value)
except ValueError:
return str(value)
prefixes = ["K", "M", "G", "T", "P", "E", "Z", "Y"]
prefix = ""
to_next_prefix = 1000
while value > to_next_prefix and prefixes:
prefix = prefixes.pop(0)
value //= to_next_prefix
return f"{value} {prefix}{suffix}"
cost = []
columns = [
("outputRowCount", "Output count", " rows"),
("outputSizeInBytes", "Output size", "B"),
("cpuCost", "CPU cost", ""),
("maxMemory", "Max memory", "B"),
("networkCost", "Network cost", ""),
]
for row in raw_cost:
estimate: Dict[str, float] = row.get("estimate", {})
statement_cost = {}
for key, label, suffix in columns:
if key in estimate:
statement_cost[label] = humanize(estimate[key],
suffix).strip()
cost.append(statement_cost)
return cost
@classmethod
def adjust_database_uri(cls,
uri: URL,
selected_schema: Optional[str] = None) -> None:
database = uri.database
if selected_schema and database:
selected_schema = parse.quote(selected_schema, safe="")
if "/" in database:
database = database.split("/")[0] + "/" + selected_schema
else:
database += "/" + selected_schema
uri.database = database
@classmethod
def convert_dttm(cls, target_type: str, dttm: datetime) -> Optional[str]:
tt = target_type.upper()
if tt == utils.TemporalType.DATE:
return f"""from_iso8601_date('{dttm.date().isoformat()}')"""
if tt == utils.TemporalType.TIMESTAMP:
return f"""from_iso8601_timestamp('{dttm.isoformat(timespec="microseconds")}')""" # pylint: disable=line-too-long
return None
@classmethod
def epoch_to_dttm(cls) -> str:
return "from_unixtime({col})"
@classmethod
def get_all_datasource_names(
cls, database: "Database",
datasource_type: str) -> List[utils.DatasourceName]:
datasource_df = database.get_df(
"SELECT table_schema, table_name FROM INFORMATION_SCHEMA.{}S "
"ORDER BY concat(table_schema, '.', table_name)".format(
datasource_type.upper()),
None,
)
datasource_names: List[utils.DatasourceName] = []
for _unused, row in datasource_df.iterrows():
datasource_names.append(
utils.DatasourceName(schema=row["table_schema"],
table=row["table_name"]))
return datasource_names
@classmethod
def expand_data( # pylint: disable=too-many-locals,too-many-branches
cls, columns: List[Dict[Any, Any]],
data: List[Dict[Any, Any]]) -> Tuple[List[Dict[Any, Any]], List[Dict[
Any, Any]], List[Dict[Any, Any]]]:
"""
We do not immediately display rows and arrays clearly in the data grid. This
method separates out nested fields and data values to help clearly display
structural columns.
Example: ColumnA is a row(nested_obj varchar) and ColumnB is an array(int)
Original data set = [
{'ColumnA': ['a1'], 'ColumnB': [1, 2]},
{'ColumnA': ['a2'], 'ColumnB': [3, 4]},
]
Expanded data set = [
{'ColumnA': ['a1'], 'ColumnA.nested_obj': 'a1', 'ColumnB': 1},
{'ColumnA': '', 'ColumnA.nested_obj': '', 'ColumnB': 2},
{'ColumnA': ['a2'], 'ColumnA.nested_obj': 'a2', 'ColumnB': 3},
{'ColumnA': '', 'ColumnA.nested_obj': '', 'ColumnB': 4},
]
: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
"""
if not is_feature_enabled("PRESTO_EXPAND_DATA"):
return columns, data, []
# process each column, unnesting ARRAY types and
# expanding ROW types into new columns
to_process = deque((column, 0) for column in columns)
all_columns: List[Dict[str, Any]] = []
expanded_columns = []
current_array_level = None
while to_process:
column, level = to_process.popleft()
if column["name"] not in [
column["name"] for column in all_columns
]:
all_columns.append(column)
# When unnesting arrays we need to keep track of how many extra rows
# were added, for each original row. This is necessary when we expand
# multiple arrays, so that the arrays after the first reuse the rows
# added by the first. every time we change a level in the nested arrays
# we reinitialize this.
if level != current_array_level:
unnested_rows: Dict[int, int] = defaultdict(int)
current_array_level = level
name = column["name"]
values: Optional[Union[str, List[Any]]]
if column["type"].startswith("ARRAY("):
# keep processing array children; we append to the right so that
# multiple nested arrays are processed breadth-first
to_process.append((get_children(column)[0], level + 1))
# unnest array objects data into new rows
i = 0
while i < len(data):
row = data[i]
values = row.get(name)
if isinstance(values, str):
row[name] = values = destringify(values)
if values:
# how many extra rows we need to unnest the data?
extra_rows = len(values) - 1
# how many rows were already added for this row?
current_unnested_rows = unnested_rows[i]
# add any necessary rows
missing = extra_rows - current_unnested_rows
for _ in range(missing):
data.insert(i + current_unnested_rows + 1, {})
unnested_rows[i] += 1
# unnest array into rows
for j, value in enumerate(values):
data[i + j][name] = value
# skip newly unnested rows
i += unnested_rows[i]
i += 1
if column["type"].startswith("ROW("):
# expand columns; we append them to the left so they are added
# immediately after the parent
expanded = get_children(column)
to_process.extendleft(
(column, level) for column in expanded[::-1])
expanded_columns.extend(expanded)
# expand row objects into new columns
for row in data:
values = row.get(name) or []
if isinstance(values, str):
row[name] = values = cast(List[Any],
destringify(values))
for value, col in zip(values, expanded):
row[col["name"]] = value
data = [{k["name"]: row.get(k["name"], "")
for k in all_columns} for row in data]
return all_columns, data, expanded_columns
@classmethod
def extra_table_metadata(cls, database: "Database", table_name: str,
schema_name: str) -> Dict[str, Any]:
metadata = {}
indexes = database.get_indexes(table_name, schema_name)
if indexes:
cols = indexes[0].get("column_names", [])
full_table_name = table_name
if schema_name and "." not in table_name:
full_table_name = "{}.{}".format(schema_name, table_name)
pql = cls._partition_query(full_table_name, database)
col_names, latest_parts = cls.latest_partition(table_name,
schema_name,
database,
show_first=True)
if not latest_parts:
latest_parts = tuple([None] * len(col_names))
metadata["partitions"] = {
"cols": cols,
"latest": dict(zip(col_names, latest_parts)),
"partitionQuery": pql,
}
# flake8 is not matching `Optional[str]` to `Any` for some reason...
metadata["view"] = cast(
Any, cls.get_create_view(database, schema_name, table_name))
return metadata
@classmethod
def get_create_view(cls, database: "Database", schema: str,
table: str) -> Optional[str]:
"""
Return a CREATE VIEW statement, or `None` if not a view.
:param database: Database instance
:param schema: Schema name
:param table: Table (view) name
"""
from pyhive.exc import DatabaseError
engine = cls.get_engine(database, schema)
with closing(engine.raw_connection()) as conn:
cursor = conn.cursor()
sql = f"SHOW CREATE VIEW {schema}.{table}"
try:
cls.execute(cursor, sql)
polled = cursor.poll()
while polled:
time.sleep(0.2)
polled = cursor.poll()
except DatabaseError: # not a VIEW
return None
rows = cls.fetch_data(cursor, 1)
return rows[0][0]
@classmethod
def handle_cursor(cls, cursor: Any, query: Query,
session: Session) -> None:
"""Updates progress information"""
query_id = query.id
poll_interval = query.database.connect_args.get(
"poll_interval", config["PRESTO_POLL_INTERVAL"])
logger.info("Query %i: Polling the cursor for progress", query_id)
polled = cursor.poll()
# poll returns dict -- JSON status information or ``None``
# if the query is done
# https://github.com/dropbox/PyHive/blob/
# b34bdbf51378b3979eaf5eca9e956f06ddc36ca0/pyhive/presto.py#L178
while polled:
# Update the object and wait for the kill signal.
stats = polled.get("stats", {})
query = session.query(type(query)).filter_by(id=query_id).one()
if query.status in [QueryStatus.STOPPED, QueryStatus.TIMED_OUT]:
cursor.cancel()
break
if stats:
state = stats.get("state")
# if already finished, then stop polling
if state == "FINISHED":
break
completed_splits = float(stats.get("completedSplits"))
total_splits = float(stats.get("totalSplits"))
if total_splits and completed_splits:
progress = 100 * (completed_splits / total_splits)
logger.info("Query {} progress: {} / {} " # pylint: disable=logging-format-interpolation
"splits".format(query_id, completed_splits,
total_splits))
if progress > query.progress:
query.progress = progress
session.commit()
time.sleep(poll_interval)
logger.info("Query %i: Polling the cursor for progress", query_id)
polled = cursor.poll()
@classmethod
def _extract_error_message(cls, ex: Exception) -> str:
if (hasattr(ex, "orig")
and type(ex.orig).__name__ == "DatabaseError" # type: ignore
and isinstance(ex.orig[0], dict) # type: ignore
):
error_dict = ex.orig[0] # type: ignore
return "{} at {}: {}".format(
error_dict.get("errorName"),
error_dict.get("errorLocation"),
error_dict.get("message"),
)
if type(ex).__name__ == "DatabaseError" and hasattr(
ex, "args") and ex.args:
error_dict = ex.args[0]
return error_dict.get("message", _("Unknown Presto Error"))
return utils.error_msg_from_exception(ex)
@classmethod
def _partition_query( # pylint: disable=too-many-arguments,too-many-locals
cls,
table_name: str,
database: "Database",
limit: int = 0,
order_by: Optional[List[Tuple[str, bool]]] = None,
filters: Optional[Dict[Any, Any]] = None,
) -> str:
"""Returns a partition query
:param table_name: the name of the table to get partitions from
:type table_name: str
:param limit: the number of partitions to be returned
:type limit: int
:param order_by: a list of tuples of field name and a boolean
that determines if that field should be sorted in descending
order
:type order_by: list of (str, bool) tuples
:param filters: dict of field name and filter value combinations
"""
limit_clause = "LIMIT {}".format(limit) if limit else ""
order_by_clause = ""
if order_by:
l = []
for field, desc in order_by:
l.append(field + " DESC" if desc else "")
order_by_clause = "ORDER BY " + ", ".join(l)
where_clause = ""
if filters:
l = []
for field, value in filters.items():
l.append(f"{field} = '{value}'")
where_clause = "WHERE " + " AND ".join(l)
presto_version = database.get_extra().get("version")
# Partition select syntax changed in v0.199, so check here.
# Default to the new syntax if version is unset.
partition_select_clause = (
f'SELECT * FROM "{table_name}$partitions"' if not presto_version
or StrictVersion(presto_version) >= StrictVersion("0.199") else
f"SHOW PARTITIONS FROM {table_name}")
sql = textwrap.dedent(f"""\
{partition_select_clause}
{where_clause}
{order_by_clause}
{limit_clause}
""")
return sql
@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]:
try:
col_names, values = cls.latest_partition(table_name,
schema,
database,
show_first=True)
except Exception: # pylint: disable=broad-except
# table is not partitioned
return None
if values is None:
return None
column_names = {column.get("name") for column in columns or []}
for col_name, value in zip(col_names, values):
if col_name in column_names:
query = query.where(Column(col_name) == value)
return query
@classmethod
def _latest_partition_from_df(cls,
df: pd.DataFrame) -> Optional[List[str]]:
if not df.empty:
return df.to_records(index=False)[0].item()
return None
@classmethod
@cache_manager.data_cache.memoize(timeout=60)
def latest_partition(
cls,
table_name: str,
schema: Optional[str],
database: "Database",
show_first: bool = False,
) -> Tuple[List[str], Optional[List[str]]]:
"""Returns col name and the latest (max) partition value for a table
:param table_name: the name of the table
:param schema: schema / database / namespace
:param database: database query will be run against
:type database: models.Database
:param show_first: displays the value for the first partitioning key
if there are many partitioning keys
:type show_first: bool
>>> latest_partition('foo_table')
(['ds'], ('2018-01-01',))
"""
indexes = database.get_indexes(table_name, schema)
if not indexes:
raise SupersetTemplateException(
f"Error getting partition for {schema}.{table_name}. "
"Verify that this table has a partition.")
if len(indexes[0]["column_names"]) < 1:
raise SupersetTemplateException(
"The table should have one partitioned field")
if not show_first and len(indexes[0]["column_names"]) > 1:
raise SupersetTemplateException(
"The table should have a single partitioned field "
"to use this function. You may want to use "
"`presto.latest_sub_partition`")
column_names = indexes[0]["column_names"]
part_fields = [(column_name, True) for column_name in column_names]
sql = cls._partition_query(table_name, database, 1, part_fields)
df = database.get_df(sql, schema)
return column_names, cls._latest_partition_from_df(df)
@classmethod
def latest_sub_partition(cls, table_name: str, schema: Optional[str],
database: "Database", **kwargs: Any) -> Any:
"""Returns the latest (max) partition value for a table
A filtering criteria should be passed for all fields that are
partitioned except for the field to be returned. For example,
if a table is partitioned by (``ds``, ``event_type`` and
``event_category``) and you want the latest ``ds``, you'll want
to provide a filter as keyword arguments for both
``event_type`` and ``event_category`` as in
``latest_sub_partition('my_table',
event_category='page', event_type='click')``
:param table_name: the name of the table, can be just the table
name or a fully qualified table name as ``schema_name.table_name``
:type table_name: str
:param schema: schema / database / namespace
:type schema: str
:param database: database query will be run against
:type database: models.Database
:param kwargs: keyword arguments define the filtering criteria
on the partition list. There can be many of these.
:type kwargs: str
>>> latest_sub_partition('sub_partition_table', event_type='click')
'2018-01-01'
"""
indexes = database.get_indexes(table_name, schema)
part_fields = indexes[0]["column_names"]
for k in kwargs.keys(): # pylint: disable=consider-iterating-dictionary
if k not in k in part_fields: # pylint: disable=comparison-with-itself
msg = "Field [{k}] is not part of the portioning key"
raise SupersetTemplateException(msg)
if len(kwargs.keys()) != len(part_fields) - 1:
msg = ("A filter needs to be specified for {} out of the "
"{} fields.").format(
len(part_fields) - 1, len(part_fields))
raise SupersetTemplateException(msg)
for field in part_fields:
if field not in kwargs.keys():
field_to_return = field
sql = cls._partition_query(table_name, database, 1,
[(field_to_return, True)], kwargs)
df = database.get_df(sql, schema)
if df.empty:
return ""
return df.to_dict()[field_to_return][0]
@classmethod
@cache_manager.data_cache.memoize()
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 database.get_df("SHOW FUNCTIONS")["Function"].tolist()
@classmethod
def extract_errors(cls, ex: Exception) -> List[SupersetError]:
raw_message = cls._extract_error_message(ex)
column_match = re.search(COLUMN_NOT_RESOLVED_ERROR_REGEX, raw_message)
if column_match:
return [
SupersetError(
error_type=SupersetErrorType.COLUMN_DOES_NOT_EXIST_ERROR,
message=__(
'We can\'t seem to resolve the column "%(column_name)s" at '
"line %(location)s.",
column_name=column_match.group(2),
location=column_match.group(1),
),
level=ErrorLevel.ERROR,
extra={"engine_name": cls.engine_name},
)
]
table_match = re.search(TABLE_DOES_NOT_EXIST_ERROR_REGEX, raw_message)
if table_match:
return [
SupersetError(
error_type=SupersetErrorType.TABLE_DOES_NOT_EXIST_ERROR,
message=__(
'The table "%(table_name)s" does not exist. '
"A valid table must be used to run this query.",
table_name=table_match.group(1),
),
level=ErrorLevel.ERROR,
extra={"engine_name": cls.engine_name},
)
]
return super().extract_errors(ex)
@classmethod
def is_readonly_query(cls, parsed_query: ParsedQuery) -> bool:
"""Pessimistic readonly, 100% sure statement won't mutate anything"""
return super().is_readonly_query(
parsed_query) or parsed_query.is_show()
@classmethod
def get_column_spec( # type: ignore
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]:
column_spec = super().get_column_spec(
native_type, column_type_mappings=column_type_mappings)
if column_spec:
return column_spec
return super().get_column_spec(native_type)
class StockBasics():
baseType = {
BASE_STOCK_BASICS: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'industry': dbtype.CHAR(50),
'area': dbtype.CHAR(20),
'pe': dbtype.FLOAT(12),
'outstanding': dbtype.FLOAT(),
'totals': dbtype.FLOAT(),
'totalAssets': dbtype.FLOAT(),
'liquidAssets': dbtype.FLOAT(),
'fixedAssets': dbtype.FLOAT(),
'reserved': dbtype.FLOAT(),
'reservedPerShare': dbtype.FLOAT(),
'esp': dbtype.FLOAT(),
'bvps': dbtype.FLOAT(),
'pb': dbtype.FLOAT(),
'timeToMarket': dbtype.TIMESTAMP('yyyyMMdd'),
'undp': dbtype.FLOAT(),
'perundp': dbtype.FLOAT(),
'perundp': dbtype.FLOAT(),
'rev': dbtype.FLOAT(),
'profit': dbtype.FLOAT(),
'gpr': dbtype.FLOAT(),
'npr': dbtype.FLOAT(),
'holders': dbtype.FLOAT(),
},
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# industry = models.CharField(u'所属行业', max_length=50)
# area = models.CharField(u'地区', max_length=20)
# pe = models.FloatField(u'市盈率')
# outstanding = models.FloatField(u'流通股本(亿)' )
# totals = models.FloatField(u'总股本(亿)' )
# totalAssets = models.FloatField(u'总资产(万)' )
# liquidAssets = models.FloatField(u'流动资产' )
# fixedAssets = models.FloatField(u'固定资产' )
# reserved = models.FloatField(u'公积金' )
# reservedPerShare= models.FloatField(u'每股公积金' )
# esp= models.FloatField(u'每股收益' )
# bvps = models.FloatField(u'每股净资' )
# pb = models.FloatField(u'市净率' )
# timeToMarket = models.CharField(u'上市日期', max_length=20)
# undp = models.FloatField(u'未分利润' )
# perundp = models.FloatField(u'每股未分配' )
# rev = models.FloatField(u'收入同比(%)' )
# profit = models.FloatField(u'利润同比(%)' )
# gpr = models.FloatField(u'毛利率(%)' )
# npr = models.FloatField(u'净利润率(%)' )
# holders = models.FloatField(u'股东人数' )
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
BASE_REPORT_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'esp': dbtype.FLOAT(12),
'eps_yoy': dbtype.FLOAT(12),
'bvps': dbtype.FLOAT(12),
'roe': dbtype.FLOAT(12),
'epcf': dbtype.FLOAT(12),
'net_profits': dbtype.FLOAT(12),
'profits_yoy': dbtype.FLOAT(12),
'distrib': dbtype.CHAR(20),
'report_date': dbtype.CHAR(10),
'date': dbtype.CHAR(10),
},
# # 业绩报告
# class ReportData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# esp = models.FloatField(u'每股收益' )
# eps_yoy = models.FloatField(u'每股收益同比( %)' )
# bvps = models.FloatField(u'每股净资产' )
# roe = models.FloatField(u'净资产收益率( %)' )
# epcf = models.FloatField(u'每股现金流量(元)' )
# net_profits = models.FloatField(u'净利润(万元)' )
# profits_yoy = models.FloatField(u'净利润同比( %)' )
# distrib = models.FloatField(u'分配方案' )
# report_date = models.CharField(u'发布日期', max_length=20)
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
BASE_PROFIT_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'roe': dbtype.FLOAT(12),
'net_profit_ratio': dbtype.FLOAT(12),
'gross_profit_rate': dbtype.FLOAT(12),
'net_profits': dbtype.FLOAT(12),
'esp': dbtype.FLOAT(12),
'business_income': dbtype.FLOAT(12),
'bips': dbtype.FLOAT(12),
'date': dbtype.CHAR(10),
},
# # 盈利能力
# class ProfitData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# roe = models.FloatField(u'净资产收益率(%)' )
# net_profit_ratio = models.FloatField(u'净利率(%)' )
# gross_profit_rate = models.FloatField(u'毛利率(%)' )
# net_profits = models.FloatField(u'净利润(万元)' )
# esp = models.FloatField(u'每股收益' )
# business_income = models.FloatField(u'营业收入(百万元)' )
# bips = models.FloatField(u'每股主营业务收入(元)' )
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
BASE_OPERATION_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'arturnover': dbtype.FLOAT(12),
'arturndays': dbtype.FLOAT(12),
'inventory_turnover': dbtype.FLOAT(12, 4),
'inventory_days': dbtype.FLOAT(12, 4),
'currentasset_turnover': dbtype.FLOAT(12, 4),
'currentasset_days': dbtype.FLOAT(12),
'date': dbtype.CHAR(10),
},
# # 营运能力
# class OperationData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# arturnover = models.FloatField(u'应收账款周转率(次)' )
# arturndays = models.FloatField(u'应收账款周转天数(天)' )
# inventory_turnover = models.FloatField(u'存货周转率(次)' )
# inventory_days = models.FloatField(u'存货周转天数(天)' )
# currentasset_turnover = models.FloatField(u'流动资产周转率(次)' )
# currentasset_days = models.FloatField(u'流动资产周转天数(天)' )
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
BASE_GROWTH_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'mbrg': dbtype.FLOAT(12),
'nprg': dbtype.FLOAT(12),
'nav': dbtype.FLOAT(12),
'targ': dbtype.FLOAT(12),
'epsg': dbtype.FLOAT(12),
'seg': dbtype.FLOAT(12),
'date': dbtype.CHAR(10),
},
# # 成长能力
# class GrowthData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# mbrg = models.FloatField(u'主营业务收入增长率( %)' )
# nprg = models.FloatField(u'净利润增长率( %)' )
# nav = models.FloatField(u'净资产增长率' )
# targ = models.FloatField(u'总资产增长率' )
# epsg = models.FloatField(u'每股收益增长率' )
# seg = models.FloatField(u'股东权益增长率' )
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
BASE_DEBTPAYINT_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'currentratio': dbtype.CHAR(50),
'quickratio': dbtype.CHAR(50),
'cashratio': dbtype.CHAR(50),
'icratio': dbtype.CHAR(50),
'sheqratio': dbtype.CHAR(50),
'adratio': dbtype.CHAR(50),
'date': dbtype.CHAR(10),
},
# # 偿债能力
# class DebtpayingData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# currentratio = models.FloatField(u'流动比率' )
# quickratio = models.FloatField(u'速动比率' )
# cashratio = models.FloatField(u'现金比率' )
# icratio = models.FloatField(u'利息支付倍数' )
# sheqratio = models.FloatField(u'股东权益比率' )
# adratio = models.FloatField(u'股东权益增长率' )
#
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
#
BASE_CASHFLOW_DATA: {
'code': dbtype.CHAR(10),
'name': dbtype.CHAR(50),
'cf_sales': dbtype.FLOAT(12),
'rateofreturn': dbtype.FLOAT(12),
'cf_nm': dbtype.FLOAT(12),
'cf_liabilities': dbtype.FLOAT(12),
'cashflowratio': dbtype.FLOAT(12),
'date': dbtype.CHAR(10),
},
# # 现金流量
# class CashFlowData(models.Model):
# code = models.CharField(u'代码', max_length=50)
# name = models.CharField(u'名称', max_length=50)
# cf_sales = models.FloatField(u'经营现金净流量对销售收入比率' )
# rateofreturn = models.FloatField(u'资产的经营现金流量回报率' )
# cf_nm = models.FloatField(u'经营现金净流量与净利润的比率' )
# cf_liabilities = models.FloatField(u'经营现金净流量对负债比率' )
# cashflowratio = models.FloatField(u'现金流量比率' )
# def __unicode__(self):
# return '%s %s' % (self.code, self.name)
}
