partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | check_ownership | Meant to be used in `pre_update` hooks on models to enforce ownership
Admin have all access, and other users need to be referenced on either
the created_by field that comes with the ``AuditMixin``, or in a field
named ``owners`` which is expected to be a one-to-many with the User
model. It is meant to be used in the ModelView's pre_update hook in
which raising will abort the update. | superset/views/base.py | def check_ownership(obj, raise_if_false=True):
"""Meant to be used in `pre_update` hooks on models to enforce ownership
Admin have all access, and other users need to be referenced on either
the created_by field that comes with the ``AuditMixin``, or in a field
named ``owners`` which is expected to be a one-to-many with the User
model. It is meant to be used in the ModelView's pre_update hook in
which raising will abort the update.
"""
if not obj:
return False
security_exception = SupersetSecurityException(
"You don't have the rights to alter [{}]".format(obj))
if g.user.is_anonymous:
if raise_if_false:
raise security_exception
return False
roles = [r.name for r in get_user_roles()]
if 'Admin' in roles:
return True
session = db.create_scoped_session()
orig_obj = session.query(obj.__class__).filter_by(id=obj.id).first()
# Making a list of owners that works across ORM models
owners = []
if hasattr(orig_obj, 'owners'):
owners += orig_obj.owners
if hasattr(orig_obj, 'owner'):
owners += [orig_obj.owner]
if hasattr(orig_obj, 'created_by'):
owners += [orig_obj.created_by]
owner_names = [o.username for o in owners if o]
if (
g.user and hasattr(g.user, 'username') and
g.user.username in owner_names):
return True
if raise_if_false:
raise security_exception
else:
return False | def check_ownership(obj, raise_if_false=True):
"""Meant to be used in `pre_update` hooks on models to enforce ownership
Admin have all access, and other users need to be referenced on either
the created_by field that comes with the ``AuditMixin``, or in a field
named ``owners`` which is expected to be a one-to-many with the User
model. It is meant to be used in the ModelView's pre_update hook in
which raising will abort the update.
"""
if not obj:
return False
security_exception = SupersetSecurityException(
"You don't have the rights to alter [{}]".format(obj))
if g.user.is_anonymous:
if raise_if_false:
raise security_exception
return False
roles = [r.name for r in get_user_roles()]
if 'Admin' in roles:
return True
session = db.create_scoped_session()
orig_obj = session.query(obj.__class__).filter_by(id=obj.id).first()
# Making a list of owners that works across ORM models
owners = []
if hasattr(orig_obj, 'owners'):
owners += orig_obj.owners
if hasattr(orig_obj, 'owner'):
owners += [orig_obj.owner]
if hasattr(orig_obj, 'created_by'):
owners += [orig_obj.created_by]
owner_names = [o.username for o in owners if o]
if (
g.user and hasattr(g.user, 'username') and
g.user.username in owner_names):
return True
if raise_if_false:
raise security_exception
else:
return False | [
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train | bind_field | Customize how fields are bound by stripping all whitespace.
:param form: The form
:param unbound_field: The unbound field
:param options: The field options
:returns: The bound field | superset/views/base.py | def bind_field(
self,
form: DynamicForm,
unbound_field: UnboundField,
options: Dict[Any, Any],
) -> Field:
"""
Customize how fields are bound by stripping all whitespace.
:param form: The form
:param unbound_field: The unbound field
:param options: The field options
:returns: The bound field
"""
filters = unbound_field.kwargs.get('filters', [])
filters.append(lambda x: x.strip() if isinstance(x, str) else x)
return unbound_field.bind(form=form, filters=filters, **options) | def bind_field(
self,
form: DynamicForm,
unbound_field: UnboundField,
options: Dict[Any, Any],
) -> Field:
"""
Customize how fields are bound by stripping all whitespace.
:param form: The form
:param unbound_field: The unbound field
:param options: The field options
:returns: The bound field
"""
filters = unbound_field.kwargs.get('filters', [])
filters.append(lambda x: x.strip() if isinstance(x, str) else x)
return unbound_field.bind(form=form, filters=filters, **options) | [
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train | BaseSupersetView.common_bootsrap_payload | Common data always sent to the client | superset/views/base.py | def common_bootsrap_payload(self):
"""Common data always sent to the client"""
messages = get_flashed_messages(with_categories=True)
locale = str(get_locale())
return {
'flash_messages': messages,
'conf': {k: conf.get(k) for k in FRONTEND_CONF_KEYS},
'locale': locale,
'language_pack': get_language_pack(locale),
'feature_flags': get_feature_flags(),
} | def common_bootsrap_payload(self):
"""Common data always sent to the client"""
messages = get_flashed_messages(with_categories=True)
locale = str(get_locale())
return {
'flash_messages': messages,
'conf': {k: conf.get(k) for k in FRONTEND_CONF_KEYS},
'locale': locale,
'language_pack': get_language_pack(locale),
'feature_flags': get_feature_flags(),
} | [
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train | DeleteMixin._delete | Delete function logic, override to implement diferent logic
deletes the record with primary_key = pk
:param pk:
record primary key to delete | superset/views/base.py | def _delete(self, pk):
"""
Delete function logic, override to implement diferent logic
deletes the record with primary_key = pk
:param pk:
record primary key to delete
"""
item = self.datamodel.get(pk, self._base_filters)
if not item:
abort(404)
try:
self.pre_delete(item)
except Exception as e:
flash(str(e), 'danger')
else:
view_menu = security_manager.find_view_menu(item.get_perm())
pvs = security_manager.get_session.query(
security_manager.permissionview_model).filter_by(
view_menu=view_menu).all()
schema_view_menu = None
if hasattr(item, 'schema_perm'):
schema_view_menu = security_manager.find_view_menu(item.schema_perm)
pvs.extend(security_manager.get_session.query(
security_manager.permissionview_model).filter_by(
view_menu=schema_view_menu).all())
if self.datamodel.delete(item):
self.post_delete(item)
for pv in pvs:
security_manager.get_session.delete(pv)
if view_menu:
security_manager.get_session.delete(view_menu)
if schema_view_menu:
security_manager.get_session.delete(schema_view_menu)
security_manager.get_session.commit()
flash(*self.datamodel.message)
self.update_redirect() | def _delete(self, pk):
"""
Delete function logic, override to implement diferent logic
deletes the record with primary_key = pk
:param pk:
record primary key to delete
"""
item = self.datamodel.get(pk, self._base_filters)
if not item:
abort(404)
try:
self.pre_delete(item)
except Exception as e:
flash(str(e), 'danger')
else:
view_menu = security_manager.find_view_menu(item.get_perm())
pvs = security_manager.get_session.query(
security_manager.permissionview_model).filter_by(
view_menu=view_menu).all()
schema_view_menu = None
if hasattr(item, 'schema_perm'):
schema_view_menu = security_manager.find_view_menu(item.schema_perm)
pvs.extend(security_manager.get_session.query(
security_manager.permissionview_model).filter_by(
view_menu=schema_view_menu).all())
if self.datamodel.delete(item):
self.post_delete(item)
for pv in pvs:
security_manager.get_session.delete(pv)
if view_menu:
security_manager.get_session.delete(view_menu)
if schema_view_menu:
security_manager.get_session.delete(schema_view_menu)
security_manager.get_session.commit()
flash(*self.datamodel.message)
self.update_redirect() | [
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train | SupersetFilter.get_all_permissions | Returns a set of tuples with the perm name and view menu name | superset/views/base.py | def get_all_permissions(self):
"""Returns a set of tuples with the perm name and view menu name"""
perms = set()
for role in self.get_user_roles():
for perm_view in role.permissions:
t = (perm_view.permission.name, perm_view.view_menu.name)
perms.add(t)
return perms | def get_all_permissions(self):
"""Returns a set of tuples with the perm name and view menu name"""
perms = set()
for role in self.get_user_roles():
for perm_view in role.permissions:
t = (perm_view.permission.name, perm_view.view_menu.name)
perms.add(t)
return perms | [
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train | SupersetFilter.get_view_menus | Returns the details of view_menus for a perm name | superset/views/base.py | def get_view_menus(self, permission_name):
"""Returns the details of view_menus for a perm name"""
vm = set()
for perm_name, vm_name in self.get_all_permissions():
if perm_name == permission_name:
vm.add(vm_name)
return vm | def get_view_menus(self, permission_name):
"""Returns the details of view_menus for a perm name"""
vm = set()
for perm_name, vm_name in self.get_all_permissions():
if perm_name == permission_name:
vm.add(vm_name)
return vm | [
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train | destroy_webdriver | Destroy a driver | superset/tasks/schedules.py | def destroy_webdriver(driver):
"""
Destroy a driver
"""
# This is some very flaky code in selenium. Hence the retries
# and catch-all exceptions
try:
retry_call(driver.close, tries=2)
except Exception:
pass
try:
driver.quit()
except Exception:
pass | def destroy_webdriver(driver):
"""
Destroy a driver
"""
# This is some very flaky code in selenium. Hence the retries
# and catch-all exceptions
try:
retry_call(driver.close, tries=2)
except Exception:
pass
try:
driver.quit()
except Exception:
pass | [
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train | deliver_dashboard | Given a schedule, delivery the dashboard as an email report | superset/tasks/schedules.py | def deliver_dashboard(schedule):
"""
Given a schedule, delivery the dashboard as an email report
"""
dashboard = schedule.dashboard
dashboard_url = _get_url_path(
'Superset.dashboard',
dashboard_id=dashboard.id,
)
# Create a driver, fetch the page, wait for the page to render
driver = create_webdriver()
window = config.get('WEBDRIVER_WINDOW')['dashboard']
driver.set_window_size(*window)
driver.get(dashboard_url)
time.sleep(PAGE_RENDER_WAIT)
# Set up a function to retry once for the element.
# This is buggy in certain selenium versions with firefox driver
get_element = getattr(driver, 'find_element_by_class_name')
element = retry_call(
get_element,
fargs=['grid-container'],
tries=2,
delay=PAGE_RENDER_WAIT,
)
try:
screenshot = element.screenshot_as_png
except WebDriverException:
# Some webdrivers do not support screenshots for elements.
# In such cases, take a screenshot of the entire page.
screenshot = driver.screenshot() # pylint: disable=no-member
finally:
destroy_webdriver(driver)
# Generate the email body and attachments
email = _generate_mail_content(
schedule,
screenshot,
dashboard.dashboard_title,
dashboard_url,
)
subject = __(
'%(prefix)s %(title)s',
prefix=config.get('EMAIL_REPORTS_SUBJECT_PREFIX'),
title=dashboard.dashboard_title,
)
_deliver_email(schedule, subject, email) | def deliver_dashboard(schedule):
"""
Given a schedule, delivery the dashboard as an email report
"""
dashboard = schedule.dashboard
dashboard_url = _get_url_path(
'Superset.dashboard',
dashboard_id=dashboard.id,
)
# Create a driver, fetch the page, wait for the page to render
driver = create_webdriver()
window = config.get('WEBDRIVER_WINDOW')['dashboard']
driver.set_window_size(*window)
driver.get(dashboard_url)
time.sleep(PAGE_RENDER_WAIT)
# Set up a function to retry once for the element.
# This is buggy in certain selenium versions with firefox driver
get_element = getattr(driver, 'find_element_by_class_name')
element = retry_call(
get_element,
fargs=['grid-container'],
tries=2,
delay=PAGE_RENDER_WAIT,
)
try:
screenshot = element.screenshot_as_png
except WebDriverException:
# Some webdrivers do not support screenshots for elements.
# In such cases, take a screenshot of the entire page.
screenshot = driver.screenshot() # pylint: disable=no-member
finally:
destroy_webdriver(driver)
# Generate the email body and attachments
email = _generate_mail_content(
schedule,
screenshot,
dashboard.dashboard_title,
dashboard_url,
)
subject = __(
'%(prefix)s %(title)s',
prefix=config.get('EMAIL_REPORTS_SUBJECT_PREFIX'),
title=dashboard.dashboard_title,
)
_deliver_email(schedule, subject, email) | [
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train | deliver_slice | Given a schedule, delivery the slice as an email report | superset/tasks/schedules.py | def deliver_slice(schedule):
"""
Given a schedule, delivery the slice as an email report
"""
if schedule.email_format == SliceEmailReportFormat.data:
email = _get_slice_data(schedule)
elif schedule.email_format == SliceEmailReportFormat.visualization:
email = _get_slice_visualization(schedule)
else:
raise RuntimeError('Unknown email report format')
subject = __(
'%(prefix)s %(title)s',
prefix=config.get('EMAIL_REPORTS_SUBJECT_PREFIX'),
title=schedule.slice.slice_name,
)
_deliver_email(schedule, subject, email) | def deliver_slice(schedule):
"""
Given a schedule, delivery the slice as an email report
"""
if schedule.email_format == SliceEmailReportFormat.data:
email = _get_slice_data(schedule)
elif schedule.email_format == SliceEmailReportFormat.visualization:
email = _get_slice_visualization(schedule)
else:
raise RuntimeError('Unknown email report format')
subject = __(
'%(prefix)s %(title)s',
prefix=config.get('EMAIL_REPORTS_SUBJECT_PREFIX'),
title=schedule.slice.slice_name,
)
_deliver_email(schedule, subject, email) | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/tasks/schedules.py#L356-L373 | [
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train | schedule_window | Find all active schedules and schedule celery tasks for
each of them with a specific ETA (determined by parsing
the cron schedule for the schedule) | superset/tasks/schedules.py | def schedule_window(report_type, start_at, stop_at, resolution):
"""
Find all active schedules and schedule celery tasks for
each of them with a specific ETA (determined by parsing
the cron schedule for the schedule)
"""
model_cls = get_scheduler_model(report_type)
dbsession = db.create_scoped_session()
schedules = dbsession.query(model_cls).filter(model_cls.active.is_(True))
for schedule in schedules:
args = (
report_type,
schedule.id,
)
# Schedule the job for the specified time window
for eta in next_schedules(schedule.crontab,
start_at,
stop_at,
resolution=resolution):
schedule_email_report.apply_async(args, eta=eta) | def schedule_window(report_type, start_at, stop_at, resolution):
"""
Find all active schedules and schedule celery tasks for
each of them with a specific ETA (determined by parsing
the cron schedule for the schedule)
"""
model_cls = get_scheduler_model(report_type)
dbsession = db.create_scoped_session()
schedules = dbsession.query(model_cls).filter(model_cls.active.is_(True))
for schedule in schedules:
args = (
report_type,
schedule.id,
)
# Schedule the job for the specified time window
for eta in next_schedules(schedule.crontab,
start_at,
stop_at,
resolution=resolution):
schedule_email_report.apply_async(args, eta=eta) | [
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train | schedule_hourly | Celery beat job meant to be invoked hourly | superset/tasks/schedules.py | def schedule_hourly():
""" Celery beat job meant to be invoked hourly """
if not config.get('ENABLE_SCHEDULED_EMAIL_REPORTS'):
logging.info('Scheduled email reports not enabled in config')
return
resolution = config.get('EMAIL_REPORTS_CRON_RESOLUTION', 0) * 60
# Get the top of the hour
start_at = datetime.now(tzlocal()).replace(microsecond=0, second=0, minute=0)
stop_at = start_at + timedelta(seconds=3600)
schedule_window(ScheduleType.dashboard.value, start_at, stop_at, resolution)
schedule_window(ScheduleType.slice.value, start_at, stop_at, resolution) | def schedule_hourly():
""" Celery beat job meant to be invoked hourly """
if not config.get('ENABLE_SCHEDULED_EMAIL_REPORTS'):
logging.info('Scheduled email reports not enabled in config')
return
resolution = config.get('EMAIL_REPORTS_CRON_RESOLUTION', 0) * 60
# Get the top of the hour
start_at = datetime.now(tzlocal()).replace(microsecond=0, second=0, minute=0)
stop_at = start_at + timedelta(seconds=3600)
schedule_window(ScheduleType.dashboard.value, start_at, stop_at, resolution)
schedule_window(ScheduleType.slice.value, start_at, stop_at, resolution) | [
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train | dedup | De-duplicates a list of string by suffixing a counter
Always returns the same number of entries as provided, and always returns
unique values. Case sensitive comparison by default.
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
foo,bar,bar__1,bar__2,Bar
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False)))
foo,bar,bar__1,bar__2,Bar__3 | superset/dataframe.py | def dedup(l, suffix='__', case_sensitive=True):
"""De-duplicates a list of string by suffixing a counter
Always returns the same number of entries as provided, and always returns
unique values. Case sensitive comparison by default.
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
foo,bar,bar__1,bar__2,Bar
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False)))
foo,bar,bar__1,bar__2,Bar__3
"""
new_l = []
seen = {}
for s in l:
s_fixed_case = s if case_sensitive else s.lower()
if s_fixed_case in seen:
seen[s_fixed_case] += 1
s += suffix + str(seen[s_fixed_case])
else:
seen[s_fixed_case] = 0
new_l.append(s)
return new_l | def dedup(l, suffix='__', case_sensitive=True):
"""De-duplicates a list of string by suffixing a counter
Always returns the same number of entries as provided, and always returns
unique values. Case sensitive comparison by default.
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
foo,bar,bar__1,bar__2,Bar
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False)))
foo,bar,bar__1,bar__2,Bar__3
"""
new_l = []
seen = {}
for s in l:
s_fixed_case = s if case_sensitive else s.lower()
if s_fixed_case in seen:
seen[s_fixed_case] += 1
s += suffix + str(seen[s_fixed_case])
else:
seen[s_fixed_case] = 0
new_l.append(s)
return new_l | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/dataframe.py#L39-L60 | [
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train | SupersetDataFrame.db_type | Given a numpy dtype, Returns a generic database type | superset/dataframe.py | def db_type(cls, dtype):
"""Given a numpy dtype, Returns a generic database type"""
if isinstance(dtype, ExtensionDtype):
return cls.type_map.get(dtype.kind)
elif hasattr(dtype, 'char'):
return cls.type_map.get(dtype.char) | def db_type(cls, dtype):
"""Given a numpy dtype, Returns a generic database type"""
if isinstance(dtype, ExtensionDtype):
return cls.type_map.get(dtype.kind)
elif hasattr(dtype, 'char'):
return cls.type_map.get(dtype.char) | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/dataframe.py#L122-L127 | [
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train | SupersetDataFrame.columns | Provides metadata about columns for data visualization.
:return: dict, with the fields name, type, is_date, is_dim and agg. | superset/dataframe.py | def columns(self):
"""Provides metadata about columns for data visualization.
:return: dict, with the fields name, type, is_date, is_dim and agg.
"""
if self.df.empty:
return None
columns = []
sample_size = min(INFER_COL_TYPES_SAMPLE_SIZE, len(self.df.index))
sample = self.df
if sample_size:
sample = self.df.sample(sample_size)
for col in self.df.dtypes.keys():
db_type_str = (
self._type_dict.get(col) or
self.db_type(self.df.dtypes[col])
)
column = {
'name': col,
'agg': self.agg_func(self.df.dtypes[col], col),
'type': db_type_str,
'is_date': self.is_date(self.df.dtypes[col], db_type_str),
'is_dim': self.is_dimension(self.df.dtypes[col], col),
}
if not db_type_str or db_type_str.upper() == 'OBJECT':
v = sample[col].iloc[0] if not sample[col].empty else None
if isinstance(v, str):
column['type'] = 'STRING'
elif isinstance(v, int):
column['type'] = 'INT'
elif isinstance(v, float):
column['type'] = 'FLOAT'
elif isinstance(v, (datetime, date)):
column['type'] = 'DATETIME'
column['is_date'] = True
column['is_dim'] = False
# check if encoded datetime
if (
column['type'] == 'STRING' and
self.datetime_conversion_rate(sample[col]) >
INFER_COL_TYPES_THRESHOLD):
column.update({
'is_date': True,
'is_dim': False,
'agg': None,
})
# 'agg' is optional attribute
if not column['agg']:
column.pop('agg', None)
columns.append(column)
return columns | def columns(self):
"""Provides metadata about columns for data visualization.
:return: dict, with the fields name, type, is_date, is_dim and agg.
"""
if self.df.empty:
return None
columns = []
sample_size = min(INFER_COL_TYPES_SAMPLE_SIZE, len(self.df.index))
sample = self.df
if sample_size:
sample = self.df.sample(sample_size)
for col in self.df.dtypes.keys():
db_type_str = (
self._type_dict.get(col) or
self.db_type(self.df.dtypes[col])
)
column = {
'name': col,
'agg': self.agg_func(self.df.dtypes[col], col),
'type': db_type_str,
'is_date': self.is_date(self.df.dtypes[col], db_type_str),
'is_dim': self.is_dimension(self.df.dtypes[col], col),
}
if not db_type_str or db_type_str.upper() == 'OBJECT':
v = sample[col].iloc[0] if not sample[col].empty else None
if isinstance(v, str):
column['type'] = 'STRING'
elif isinstance(v, int):
column['type'] = 'INT'
elif isinstance(v, float):
column['type'] = 'FLOAT'
elif isinstance(v, (datetime, date)):
column['type'] = 'DATETIME'
column['is_date'] = True
column['is_dim'] = False
# check if encoded datetime
if (
column['type'] == 'STRING' and
self.datetime_conversion_rate(sample[col]) >
INFER_COL_TYPES_THRESHOLD):
column.update({
'is_date': True,
'is_dim': False,
'agg': None,
})
# 'agg' is optional attribute
if not column['agg']:
column.pop('agg', None)
columns.append(column)
return columns | [
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"visualization",
"."
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/dataframe.py#L177-L229 | [
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train | TableColumn.get_timestamp_expression | Getting the time component of the query | superset/connectors/sqla/models.py | def get_timestamp_expression(self, time_grain):
"""Getting the time component of the query"""
label = utils.DTTM_ALIAS
db = self.table.database
pdf = self.python_date_format
is_epoch = pdf in ('epoch_s', 'epoch_ms')
if not self.expression and not time_grain and not is_epoch:
sqla_col = column(self.column_name, type_=DateTime)
return self.table.make_sqla_column_compatible(sqla_col, label)
grain = None
if time_grain:
grain = db.grains_dict().get(time_grain)
if not grain:
raise NotImplementedError(
f'No grain spec for {time_grain} for database {db.database_name}')
col = db.db_engine_spec.get_timestamp_column(self.expression, self.column_name)
expr = db.db_engine_spec.get_time_expr(col, pdf, time_grain, grain)
sqla_col = literal_column(expr, type_=DateTime)
return self.table.make_sqla_column_compatible(sqla_col, label) | def get_timestamp_expression(self, time_grain):
"""Getting the time component of the query"""
label = utils.DTTM_ALIAS
db = self.table.database
pdf = self.python_date_format
is_epoch = pdf in ('epoch_s', 'epoch_ms')
if not self.expression and not time_grain and not is_epoch:
sqla_col = column(self.column_name, type_=DateTime)
return self.table.make_sqla_column_compatible(sqla_col, label)
grain = None
if time_grain:
grain = db.grains_dict().get(time_grain)
if not grain:
raise NotImplementedError(
f'No grain spec for {time_grain} for database {db.database_name}')
col = db.db_engine_spec.get_timestamp_column(self.expression, self.column_name)
expr = db.db_engine_spec.get_time_expr(col, pdf, time_grain, grain)
sqla_col = literal_column(expr, type_=DateTime)
return self.table.make_sqla_column_compatible(sqla_col, label) | [
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train | TableColumn.dttm_sql_literal | Convert datetime object to a SQL expression string
If database_expression is empty, the internal dttm
will be parsed as the string with the pattern that
the user inputted (python_date_format)
If database_expression is not empty, the internal dttm
will be parsed as the sql sentence for the database to convert | superset/connectors/sqla/models.py | def dttm_sql_literal(self, dttm, is_epoch_in_utc):
"""Convert datetime object to a SQL expression string
If database_expression is empty, the internal dttm
will be parsed as the string with the pattern that
the user inputted (python_date_format)
If database_expression is not empty, the internal dttm
will be parsed as the sql sentence for the database to convert
"""
tf = self.python_date_format
if self.database_expression:
return self.database_expression.format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
elif tf:
if is_epoch_in_utc:
seconds_since_epoch = dttm.timestamp()
else:
seconds_since_epoch = (dttm - datetime(1970, 1, 1)).total_seconds()
seconds_since_epoch = int(seconds_since_epoch)
if tf == 'epoch_s':
return str(seconds_since_epoch)
elif tf == 'epoch_ms':
return str(seconds_since_epoch * 1000)
return "'{}'".format(dttm.strftime(tf))
else:
s = self.table.database.db_engine_spec.convert_dttm(
self.type or '', dttm)
return s or "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S.%f')) | def dttm_sql_literal(self, dttm, is_epoch_in_utc):
"""Convert datetime object to a SQL expression string
If database_expression is empty, the internal dttm
will be parsed as the string with the pattern that
the user inputted (python_date_format)
If database_expression is not empty, the internal dttm
will be parsed as the sql sentence for the database to convert
"""
tf = self.python_date_format
if self.database_expression:
return self.database_expression.format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
elif tf:
if is_epoch_in_utc:
seconds_since_epoch = dttm.timestamp()
else:
seconds_since_epoch = (dttm - datetime(1970, 1, 1)).total_seconds()
seconds_since_epoch = int(seconds_since_epoch)
if tf == 'epoch_s':
return str(seconds_since_epoch)
elif tf == 'epoch_ms':
return str(seconds_since_epoch * 1000)
return "'{}'".format(dttm.strftime(tf))
else:
s = self.table.database.db_engine_spec.convert_dttm(
self.type or '', dttm)
return s or "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S.%f')) | [
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train | SqlaTable.make_sqla_column_compatible | Takes a sql alchemy column object and adds label info if supported by engine.
:param sqla_col: sql alchemy column instance
:param label: alias/label that column is expected to have
:return: either a sql alchemy column or label instance if supported by engine | superset/connectors/sqla/models.py | def make_sqla_column_compatible(self, sqla_col, label=None):
"""Takes a sql alchemy column object and adds label info if supported by engine.
:param sqla_col: sql alchemy column instance
:param label: alias/label that column is expected to have
:return: either a sql alchemy column or label instance if supported by engine
"""
label_expected = label or sqla_col.name
db_engine_spec = self.database.db_engine_spec
if db_engine_spec.supports_column_aliases:
label = db_engine_spec.make_label_compatible(label_expected)
sqla_col = sqla_col.label(label)
sqla_col._df_label_expected = label_expected
return sqla_col | def make_sqla_column_compatible(self, sqla_col, label=None):
"""Takes a sql alchemy column object and adds label info if supported by engine.
:param sqla_col: sql alchemy column instance
:param label: alias/label that column is expected to have
:return: either a sql alchemy column or label instance if supported by engine
"""
label_expected = label or sqla_col.name
db_engine_spec = self.database.db_engine_spec
if db_engine_spec.supports_column_aliases:
label = db_engine_spec.make_label_compatible(label_expected)
sqla_col = sqla_col.label(label)
sqla_col._df_label_expected = label_expected
return sqla_col | [
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train | SqlaTable.values_for_column | Runs query against sqla to retrieve some
sample values for the given column. | superset/connectors/sqla/models.py | def values_for_column(self, column_name, limit=10000):
"""Runs query against sqla to retrieve some
sample values for the given column.
"""
cols = {col.column_name: col for col in self.columns}
target_col = cols[column_name]
tp = self.get_template_processor()
qry = (
select([target_col.get_sqla_col()])
.select_from(self.get_from_clause(tp))
.distinct()
)
if limit:
qry = qry.limit(limit)
if self.fetch_values_predicate:
tp = self.get_template_processor()
qry = qry.where(tp.process_template(self.fetch_values_predicate))
engine = self.database.get_sqla_engine()
sql = '{}'.format(
qry.compile(engine, compile_kwargs={'literal_binds': True}),
)
sql = self.mutate_query_from_config(sql)
df = pd.read_sql_query(sql=sql, con=engine)
return [row[0] for row in df.to_records(index=False)] | def values_for_column(self, column_name, limit=10000):
"""Runs query against sqla to retrieve some
sample values for the given column.
"""
cols = {col.column_name: col for col in self.columns}
target_col = cols[column_name]
tp = self.get_template_processor()
qry = (
select([target_col.get_sqla_col()])
.select_from(self.get_from_clause(tp))
.distinct()
)
if limit:
qry = qry.limit(limit)
if self.fetch_values_predicate:
tp = self.get_template_processor()
qry = qry.where(tp.process_template(self.fetch_values_predicate))
engine = self.database.get_sqla_engine()
sql = '{}'.format(
qry.compile(engine, compile_kwargs={'literal_binds': True}),
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sql = self.mutate_query_from_config(sql)
df = pd.read_sql_query(sql=sql, con=engine)
return [row[0] for row in df.to_records(index=False)] | [
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train | SqlaTable.mutate_query_from_config | Apply config's SQL_QUERY_MUTATOR
Typically adds comments to the query with context | superset/connectors/sqla/models.py | def mutate_query_from_config(self, sql):
"""Apply config's SQL_QUERY_MUTATOR
Typically adds comments to the query with context"""
SQL_QUERY_MUTATOR = config.get('SQL_QUERY_MUTATOR')
if SQL_QUERY_MUTATOR:
username = utils.get_username()
sql = SQL_QUERY_MUTATOR(sql, username, security_manager, self.database)
return sql | def mutate_query_from_config(self, sql):
"""Apply config's SQL_QUERY_MUTATOR
Typically adds comments to the query with context"""
SQL_QUERY_MUTATOR = config.get('SQL_QUERY_MUTATOR')
if SQL_QUERY_MUTATOR:
username = utils.get_username()
sql = SQL_QUERY_MUTATOR(sql, username, security_manager, self.database)
return sql | [
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train | SqlaTable.adhoc_metric_to_sqla | Turn an adhoc metric into a sqlalchemy column.
:param dict metric: Adhoc metric definition
:param dict cols: Columns for the current table
:returns: The metric defined as a sqlalchemy column
:rtype: sqlalchemy.sql.column | superset/connectors/sqla/models.py | def adhoc_metric_to_sqla(self, metric, cols):
"""
Turn an adhoc metric into a sqlalchemy column.
:param dict metric: Adhoc metric definition
:param dict cols: Columns for the current table
:returns: The metric defined as a sqlalchemy column
:rtype: sqlalchemy.sql.column
"""
expression_type = metric.get('expressionType')
label = utils.get_metric_name(metric)
if expression_type == utils.ADHOC_METRIC_EXPRESSION_TYPES['SIMPLE']:
column_name = metric.get('column').get('column_name')
table_column = cols.get(column_name)
if table_column:
sqla_column = table_column.get_sqla_col()
else:
sqla_column = column(column_name)
sqla_metric = self.sqla_aggregations[metric.get('aggregate')](sqla_column)
elif expression_type == utils.ADHOC_METRIC_EXPRESSION_TYPES['SQL']:
sqla_metric = literal_column(metric.get('sqlExpression'))
else:
return None
return self.make_sqla_column_compatible(sqla_metric, label) | def adhoc_metric_to_sqla(self, metric, cols):
"""
Turn an adhoc metric into a sqlalchemy column.
:param dict metric: Adhoc metric definition
:param dict cols: Columns for the current table
:returns: The metric defined as a sqlalchemy column
:rtype: sqlalchemy.sql.column
"""
expression_type = metric.get('expressionType')
label = utils.get_metric_name(metric)
if expression_type == utils.ADHOC_METRIC_EXPRESSION_TYPES['SIMPLE']:
column_name = metric.get('column').get('column_name')
table_column = cols.get(column_name)
if table_column:
sqla_column = table_column.get_sqla_col()
else:
sqla_column = column(column_name)
sqla_metric = self.sqla_aggregations[metric.get('aggregate')](sqla_column)
elif expression_type == utils.ADHOC_METRIC_EXPRESSION_TYPES['SQL']:
sqla_metric = literal_column(metric.get('sqlExpression'))
else:
return None
return self.make_sqla_column_compatible(sqla_metric, label) | [
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train | SqlaTable.get_sqla_query | Querying any sqla table from this common interface | superset/connectors/sqla/models.py | def get_sqla_query( # sqla
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=15,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
orderby=None,
extras=None,
columns=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Querying any sqla table from this common interface"""
template_kwargs = {
'from_dttm': from_dttm,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'to_dttm': to_dttm,
'filter': filter,
'columns': {col.column_name: col for col in self.columns},
}
template_kwargs.update(self.template_params_dict)
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols = {col.column_name: col for col in self.columns}
metrics_dict = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise Exception(_(
'Datetime column not provided as part table configuration '
'and is required by this type of chart'))
if not groupby and not metrics and not columns:
raise Exception(_('Empty query?'))
metrics_exprs = []
for m in metrics:
if utils.is_adhoc_metric(m):
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif m in metrics_dict:
metrics_exprs.append(metrics_dict.get(m).get_sqla_col())
else:
raise Exception(_("Metric '{}' is not valid".format(m)))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr, label = literal_column('COUNT(*)'), 'ccount'
main_metric_expr = self.make_sqla_column_compatible(main_metric_expr, label)
select_exprs = []
groupby_exprs_sans_timestamp = OrderedDict()
if groupby:
select_exprs = []
for s in groupby:
if s in cols:
outer = cols[s].get_sqla_col()
else:
outer = literal_column(f'({s})')
outer = self.make_sqla_column_compatible(outer, s)
groupby_exprs_sans_timestamp[outer.name] = outer
select_exprs.append(outer)
elif columns:
for s in columns:
select_exprs.append(
cols[s].get_sqla_col() if s in cols else
self.make_sqla_column_compatible(literal_column(s)))
metrics_exprs = []
groupby_exprs_with_timestamp = OrderedDict(groupby_exprs_sans_timestamp.items())
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get('time_grain_sqla')
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs_with_timestamp[timestamp.name] = timestamp
# Use main dttm column to support index with secondary dttm columns
if db_engine_spec.time_secondary_columns and \
self.main_dttm_col in self.dttm_cols and \
self.main_dttm_col != dttm_col.column_name:
time_filters.append(cols[self.main_dttm_col].
get_time_filter(from_dttm, to_dttm))
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
select_exprs += metrics_exprs
labels_expected = [c._df_label_expected for c in select_exprs]
select_exprs = db_engine_spec.make_select_compatible(
groupby_exprs_with_timestamp.values(),
select_exprs)
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if not columns:
qry = qry.group_by(*groupby_exprs_with_timestamp.values())
where_clause_and = []
having_clause_and = []
for flt in filter:
if not all([flt.get(s) for s in ['col', 'op']]):
continue
col = flt['col']
op = flt['op']
col_obj = cols.get(col)
if col_obj:
is_list_target = op in ('in', 'not in')
eq = self.filter_values_handler(
flt.get('val'),
target_column_is_numeric=col_obj.is_num,
is_list_target=is_list_target)
if op in ('in', 'not in'):
cond = col_obj.get_sqla_col().in_(eq)
if '<NULL>' in eq:
cond = or_(cond, col_obj.get_sqla_col() == None) # noqa
if op == 'not in':
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_num:
eq = utils.string_to_num(flt['val'])
if op == '==':
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == '!=':
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == '>':
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == '<':
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == '>=':
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == '<=':
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == 'LIKE':
where_clause_and.append(col_obj.get_sqla_col().like(eq))
elif op == 'IS NULL':
where_clause_and.append(col_obj.get_sqla_col() == None) # noqa
elif op == 'IS NOT NULL':
where_clause_and.append(
col_obj.get_sqla_col() != None) # noqa
if extras:
where = extras.get('where')
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text('({})'.format(where))]
having = extras.get('having')
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text('({})'.format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and not columns:
orderby = [(main_metric_expr, not order_desc)]
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if is_timeseries and \
timeseries_limit and groupby and not time_groupby_inline:
if self.database.db_engine_spec.inner_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, 'mme_inner__')
inner_groupby_exprs = []
inner_select_exprs = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
inner = self.make_sqla_column_compatible(gby_obj, gby_name + '__')
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs).select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
)
subq = subq.where(and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
ob = self._get_timeseries_orderby(
timeseries_limit_metric,
metrics_dict,
cols,
)
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
# in this case the column name, not the alias, needs to be
# conditionally mutated, as it refers to the column alias in
# the inner query
col_name = db_engine_spec.make_label_compatible(gby_name + '__')
on_clause.append(gby_obj == column(col_name))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
if timeseries_limit_metric:
orderby = [(
self._get_timeseries_orderby(
timeseries_limit_metric,
metrics_dict,
cols,
),
False,
)]
# run subquery to get top groups
subquery_obj = {
'prequeries': prequeries,
'is_prequery': True,
'is_timeseries': False,
'row_limit': timeseries_limit,
'groupby': groupby,
'metrics': metrics,
'granularity': granularity,
'from_dttm': inner_from_dttm or from_dttm,
'to_dttm': inner_to_dttm or to_dttm,
'filter': filter,
'orderby': orderby,
'extras': extras,
'columns': columns,
'order_desc': True,
}
result = self.query(subquery_obj)
dimensions = [
c for c in result.df.columns
if c not in metrics and c in groupby_exprs_sans_timestamp
]
top_groups = self._get_top_groups(result.df,
dimensions,
groupby_exprs_sans_timestamp)
qry = qry.where(top_groups)
return SqlaQuery(sqla_query=qry.select_from(tbl),
labels_expected=labels_expected) | def get_sqla_query( # sqla
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=15,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
orderby=None,
extras=None,
columns=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Querying any sqla table from this common interface"""
template_kwargs = {
'from_dttm': from_dttm,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'to_dttm': to_dttm,
'filter': filter,
'columns': {col.column_name: col for col in self.columns},
}
template_kwargs.update(self.template_params_dict)
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols = {col.column_name: col for col in self.columns}
metrics_dict = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise Exception(_(
'Datetime column not provided as part table configuration '
'and is required by this type of chart'))
if not groupby and not metrics and not columns:
raise Exception(_('Empty query?'))
metrics_exprs = []
for m in metrics:
if utils.is_adhoc_metric(m):
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif m in metrics_dict:
metrics_exprs.append(metrics_dict.get(m).get_sqla_col())
else:
raise Exception(_("Metric '{}' is not valid".format(m)))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr, label = literal_column('COUNT(*)'), 'ccount'
main_metric_expr = self.make_sqla_column_compatible(main_metric_expr, label)
select_exprs = []
groupby_exprs_sans_timestamp = OrderedDict()
if groupby:
select_exprs = []
for s in groupby:
if s in cols:
outer = cols[s].get_sqla_col()
else:
outer = literal_column(f'({s})')
outer = self.make_sqla_column_compatible(outer, s)
groupby_exprs_sans_timestamp[outer.name] = outer
select_exprs.append(outer)
elif columns:
for s in columns:
select_exprs.append(
cols[s].get_sqla_col() if s in cols else
self.make_sqla_column_compatible(literal_column(s)))
metrics_exprs = []
groupby_exprs_with_timestamp = OrderedDict(groupby_exprs_sans_timestamp.items())
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get('time_grain_sqla')
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs_with_timestamp[timestamp.name] = timestamp
# Use main dttm column to support index with secondary dttm columns
if db_engine_spec.time_secondary_columns and \
self.main_dttm_col in self.dttm_cols and \
self.main_dttm_col != dttm_col.column_name:
time_filters.append(cols[self.main_dttm_col].
get_time_filter(from_dttm, to_dttm))
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
select_exprs += metrics_exprs
labels_expected = [c._df_label_expected for c in select_exprs]
select_exprs = db_engine_spec.make_select_compatible(
groupby_exprs_with_timestamp.values(),
select_exprs)
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if not columns:
qry = qry.group_by(*groupby_exprs_with_timestamp.values())
where_clause_and = []
having_clause_and = []
for flt in filter:
if not all([flt.get(s) for s in ['col', 'op']]):
continue
col = flt['col']
op = flt['op']
col_obj = cols.get(col)
if col_obj:
is_list_target = op in ('in', 'not in')
eq = self.filter_values_handler(
flt.get('val'),
target_column_is_numeric=col_obj.is_num,
is_list_target=is_list_target)
if op in ('in', 'not in'):
cond = col_obj.get_sqla_col().in_(eq)
if '<NULL>' in eq:
cond = or_(cond, col_obj.get_sqla_col() == None) # noqa
if op == 'not in':
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_num:
eq = utils.string_to_num(flt['val'])
if op == '==':
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == '!=':
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == '>':
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == '<':
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == '>=':
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == '<=':
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == 'LIKE':
where_clause_and.append(col_obj.get_sqla_col().like(eq))
elif op == 'IS NULL':
where_clause_and.append(col_obj.get_sqla_col() == None) # noqa
elif op == 'IS NOT NULL':
where_clause_and.append(
col_obj.get_sqla_col() != None) # noqa
if extras:
where = extras.get('where')
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text('({})'.format(where))]
having = extras.get('having')
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text('({})'.format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and not columns:
orderby = [(main_metric_expr, not order_desc)]
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if is_timeseries and \
timeseries_limit and groupby and not time_groupby_inline:
if self.database.db_engine_spec.inner_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, 'mme_inner__')
inner_groupby_exprs = []
inner_select_exprs = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
inner = self.make_sqla_column_compatible(gby_obj, gby_name + '__')
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs).select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
)
subq = subq.where(and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
ob = self._get_timeseries_orderby(
timeseries_limit_metric,
metrics_dict,
cols,
)
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
# in this case the column name, not the alias, needs to be
# conditionally mutated, as it refers to the column alias in
# the inner query
col_name = db_engine_spec.make_label_compatible(gby_name + '__')
on_clause.append(gby_obj == column(col_name))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
if timeseries_limit_metric:
orderby = [(
self._get_timeseries_orderby(
timeseries_limit_metric,
metrics_dict,
cols,
),
False,
)]
# run subquery to get top groups
subquery_obj = {
'prequeries': prequeries,
'is_prequery': True,
'is_timeseries': False,
'row_limit': timeseries_limit,
'groupby': groupby,
'metrics': metrics,
'granularity': granularity,
'from_dttm': inner_from_dttm or from_dttm,
'to_dttm': inner_to_dttm or to_dttm,
'filter': filter,
'orderby': orderby,
'extras': extras,
'columns': columns,
'order_desc': True,
}
result = self.query(subquery_obj)
dimensions = [
c for c in result.df.columns
if c not in metrics and c in groupby_exprs_sans_timestamp
]
top_groups = self._get_top_groups(result.df,
dimensions,
groupby_exprs_sans_timestamp)
qry = qry.where(top_groups)
return SqlaQuery(sqla_query=qry.select_from(tbl),
labels_expected=labels_expected) | [
"Querying",
"any",
"sqla",
"table",
"from",
"this",
"common",
"interface"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/sqla/models.py#L536-L808 | [
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train | SqlaTable.fetch_metadata | Fetches the metadata for the table and merges it in | superset/connectors/sqla/models.py | def fetch_metadata(self):
"""Fetches the metadata for the table and merges it in"""
try:
table = self.get_sqla_table_object()
except Exception as e:
logging.exception(e)
raise Exception(_(
"Table [{}] doesn't seem to exist in the specified database, "
"couldn't fetch column information").format(self.table_name))
M = SqlMetric # noqa
metrics = []
any_date_col = None
db_engine_spec = self.database.db_engine_spec
db_dialect = self.database.get_dialect()
dbcols = (
db.session.query(TableColumn)
.filter(TableColumn.table == self)
.filter(or_(TableColumn.column_name == col.name
for col in table.columns)))
dbcols = {dbcol.column_name: dbcol for dbcol in dbcols}
for col in table.columns:
try:
datatype = col.type.compile(dialect=db_dialect).upper()
except Exception as e:
datatype = 'UNKNOWN'
logging.error(
'Unrecognized data type in {}.{}'.format(table, col.name))
logging.exception(e)
dbcol = dbcols.get(col.name, None)
if not dbcol:
dbcol = TableColumn(column_name=col.name, type=datatype)
dbcol.sum = dbcol.is_num
dbcol.avg = dbcol.is_num
dbcol.is_dttm = dbcol.is_time
db_engine_spec.alter_new_orm_column(dbcol)
else:
dbcol.type = datatype
dbcol.groupby = True
dbcol.filterable = True
self.columns.append(dbcol)
if not any_date_col and dbcol.is_time:
any_date_col = col.name
metrics.append(M(
metric_name='count',
verbose_name='COUNT(*)',
metric_type='count',
expression='COUNT(*)',
))
if not self.main_dttm_col:
self.main_dttm_col = any_date_col
self.add_missing_metrics(metrics)
db.session.merge(self)
db.session.commit() | def fetch_metadata(self):
"""Fetches the metadata for the table and merges it in"""
try:
table = self.get_sqla_table_object()
except Exception as e:
logging.exception(e)
raise Exception(_(
"Table [{}] doesn't seem to exist in the specified database, "
"couldn't fetch column information").format(self.table_name))
M = SqlMetric # noqa
metrics = []
any_date_col = None
db_engine_spec = self.database.db_engine_spec
db_dialect = self.database.get_dialect()
dbcols = (
db.session.query(TableColumn)
.filter(TableColumn.table == self)
.filter(or_(TableColumn.column_name == col.name
for col in table.columns)))
dbcols = {dbcol.column_name: dbcol for dbcol in dbcols}
for col in table.columns:
try:
datatype = col.type.compile(dialect=db_dialect).upper()
except Exception as e:
datatype = 'UNKNOWN'
logging.error(
'Unrecognized data type in {}.{}'.format(table, col.name))
logging.exception(e)
dbcol = dbcols.get(col.name, None)
if not dbcol:
dbcol = TableColumn(column_name=col.name, type=datatype)
dbcol.sum = dbcol.is_num
dbcol.avg = dbcol.is_num
dbcol.is_dttm = dbcol.is_time
db_engine_spec.alter_new_orm_column(dbcol)
else:
dbcol.type = datatype
dbcol.groupby = True
dbcol.filterable = True
self.columns.append(dbcol)
if not any_date_col and dbcol.is_time:
any_date_col = col.name
metrics.append(M(
metric_name='count',
verbose_name='COUNT(*)',
metric_type='count',
expression='COUNT(*)',
))
if not self.main_dttm_col:
self.main_dttm_col = any_date_col
self.add_missing_metrics(metrics)
db.session.merge(self)
db.session.commit() | [
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train | SqlaTable.import_obj | Imports the datasource from the object to the database.
Metrics and columns and datasource will be overrided if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over. | superset/connectors/sqla/models.py | def import_obj(cls, i_datasource, import_time=None):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overrided if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over.
"""
def lookup_sqlatable(table):
return db.session.query(SqlaTable).join(Database).filter(
SqlaTable.table_name == table.table_name,
SqlaTable.schema == table.schema,
Database.id == table.database_id,
).first()
def lookup_database(table):
return db.session.query(Database).filter_by(
database_name=table.params_dict['database_name']).one()
return import_datasource.import_datasource(
db.session, i_datasource, lookup_database, lookup_sqlatable,
import_time) | def import_obj(cls, i_datasource, import_time=None):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overrided if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over.
"""
def lookup_sqlatable(table):
return db.session.query(SqlaTable).join(Database).filter(
SqlaTable.table_name == table.table_name,
SqlaTable.schema == table.schema,
Database.id == table.database_id,
).first()
def lookup_database(table):
return db.session.query(Database).filter_by(
database_name=table.params_dict['database_name']).one()
return import_datasource.import_datasource(
db.session, i_datasource, lookup_database, lookup_sqlatable,
import_time) | [
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"database",
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/sqla/models.py#L933-L952 | [
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train | load_long_lat_data | Loading lat/long data from a csv file in the repo | superset/data/long_lat.py | def load_long_lat_data():
"""Loading lat/long data from a csv file in the repo"""
data = get_example_data('san_francisco.csv.gz', make_bytes=True)
pdf = pd.read_csv(data, encoding='utf-8')
start = datetime.datetime.now().replace(
hour=0, minute=0, second=0, microsecond=0)
pdf['datetime'] = [
start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
for i in range(len(pdf))
]
pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
pdf['geohash'] = pdf[['LAT', 'LON']].apply(
lambda x: geohash.encode(*x), axis=1)
pdf['delimited'] = pdf['LAT'].map(str).str.cat(pdf['LON'].map(str), sep=',')
pdf.to_sql( # pylint: disable=no-member
'long_lat',
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'longitude': Float(),
'latitude': Float(),
'number': Float(),
'street': String(100),
'unit': String(10),
'city': String(50),
'district': String(50),
'region': String(50),
'postcode': Float(),
'id': String(100),
'datetime': DateTime(),
'occupancy': Float(),
'radius_miles': Float(),
'geohash': String(12),
'delimited': String(60),
},
index=False)
print('Done loading table!')
print('-' * 80)
print('Creating table reference')
obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
if not obj:
obj = TBL(table_name='long_lat')
obj.main_dttm_col = 'datetime'
obj.database = utils.get_or_create_main_db()
db.session.merge(obj)
db.session.commit()
obj.fetch_metadata()
tbl = obj
slice_data = {
'granularity_sqla': 'day',
'since': '2014-01-01',
'until': 'now',
'where': '',
'viz_type': 'mapbox',
'all_columns_x': 'LON',
'all_columns_y': 'LAT',
'mapbox_style': 'mapbox://styles/mapbox/light-v9',
'all_columns': ['occupancy'],
'row_limit': 500000,
}
print('Creating a slice')
slc = Slice(
slice_name='Mapbox Long/Lat',
viz_type='mapbox',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(slice_data),
)
misc_dash_slices.add(slc.slice_name)
merge_slice(slc) | def load_long_lat_data():
"""Loading lat/long data from a csv file in the repo"""
data = get_example_data('san_francisco.csv.gz', make_bytes=True)
pdf = pd.read_csv(data, encoding='utf-8')
start = datetime.datetime.now().replace(
hour=0, minute=0, second=0, microsecond=0)
pdf['datetime'] = [
start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
for i in range(len(pdf))
]
pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
pdf['geohash'] = pdf[['LAT', 'LON']].apply(
lambda x: geohash.encode(*x), axis=1)
pdf['delimited'] = pdf['LAT'].map(str).str.cat(pdf['LON'].map(str), sep=',')
pdf.to_sql( # pylint: disable=no-member
'long_lat',
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'longitude': Float(),
'latitude': Float(),
'number': Float(),
'street': String(100),
'unit': String(10),
'city': String(50),
'district': String(50),
'region': String(50),
'postcode': Float(),
'id': String(100),
'datetime': DateTime(),
'occupancy': Float(),
'radius_miles': Float(),
'geohash': String(12),
'delimited': String(60),
},
index=False)
print('Done loading table!')
print('-' * 80)
print('Creating table reference')
obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
if not obj:
obj = TBL(table_name='long_lat')
obj.main_dttm_col = 'datetime'
obj.database = utils.get_or_create_main_db()
db.session.merge(obj)
db.session.commit()
obj.fetch_metadata()
tbl = obj
slice_data = {
'granularity_sqla': 'day',
'since': '2014-01-01',
'until': 'now',
'where': '',
'viz_type': 'mapbox',
'all_columns_x': 'LON',
'all_columns_y': 'LAT',
'mapbox_style': 'mapbox://styles/mapbox/light-v9',
'all_columns': ['occupancy'],
'row_limit': 500000,
}
print('Creating a slice')
slc = Slice(
slice_name='Mapbox Long/Lat',
viz_type='mapbox',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(slice_data),
)
misc_dash_slices.add(slc.slice_name)
merge_slice(slc) | [
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"/",
"long",
"data",
"from",
"a",
"csv",
"file",
"in",
"the",
"repo"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/long_lat.py#L36-L110 | [
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train | Datasource.external_metadata | Gets column info from the source system | superset/views/datasource.py | def external_metadata(self, datasource_type=None, datasource_id=None):
"""Gets column info from the source system"""
if datasource_type == 'druid':
datasource = ConnectorRegistry.get_datasource(
datasource_type, datasource_id, db.session)
elif datasource_type == 'table':
database = (
db.session
.query(Database)
.filter_by(id=request.args.get('db_id'))
.one()
)
Table = ConnectorRegistry.sources['table']
datasource = Table(
database=database,
table_name=request.args.get('table_name'),
schema=request.args.get('schema') or None,
)
external_metadata = datasource.external_metadata()
return self.json_response(external_metadata) | def external_metadata(self, datasource_type=None, datasource_id=None):
"""Gets column info from the source system"""
if datasource_type == 'druid':
datasource = ConnectorRegistry.get_datasource(
datasource_type, datasource_id, db.session)
elif datasource_type == 'table':
database = (
db.session
.query(Database)
.filter_by(id=request.args.get('db_id'))
.one()
)
Table = ConnectorRegistry.sources['table']
datasource = Table(
database=database,
table_name=request.args.get('table_name'),
schema=request.args.get('schema') or None,
)
external_metadata = datasource.external_metadata()
return self.json_response(external_metadata) | [
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train | filter_not_empty_values | Returns a list of non empty values or None | superset/forms.py | def filter_not_empty_values(value):
"""Returns a list of non empty values or None"""
if not value:
return None
data = [x for x in value if x]
if not data:
return None
return data | def filter_not_empty_values(value):
"""Returns a list of non empty values or None"""
if not value:
return None
data = [x for x in value if x]
if not data:
return None
return data | [
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train | CsvToDatabaseForm.at_least_one_schema_is_allowed | If the user has access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is able to upload csv without specifying schema name
b) if database supports schema
user is able to upload csv to any schema
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and upload will fail
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload
elif the user does not access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is unable to upload csv
b) if database supports schema
user is unable to upload csv
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and user is unable to upload csv
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload | superset/forms.py | def at_least_one_schema_is_allowed(database):
"""
If the user has access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is able to upload csv without specifying schema name
b) if database supports schema
user is able to upload csv to any schema
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and upload will fail
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload
elif the user does not access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is unable to upload csv
b) if database supports schema
user is unable to upload csv
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and user is unable to upload csv
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload
"""
if (security_manager.database_access(database) or
security_manager.all_datasource_access()):
return True
schemas = database.get_schema_access_for_csv_upload()
if (schemas and
security_manager.schemas_accessible_by_user(
database, schemas, False)):
return True
return False | def at_least_one_schema_is_allowed(database):
"""
If the user has access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is able to upload csv without specifying schema name
b) if database supports schema
user is able to upload csv to any schema
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and upload will fail
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload
elif the user does not access to the database or all datasource
1. if schemas_allowed_for_csv_upload is empty
a) if database does not support schema
user is unable to upload csv
b) if database supports schema
user is unable to upload csv
2. if schemas_allowed_for_csv_upload is not empty
a) if database does not support schema
This situation is impossible and user is unable to upload csv
b) if database supports schema
user is able to upload to schema in schemas_allowed_for_csv_upload
"""
if (security_manager.database_access(database) or
security_manager.all_datasource_access()):
return True
schemas = database.get_schema_access_for_csv_upload()
if (schemas and
security_manager.schemas_accessible_by_user(
database, schemas, False)):
return True
return False | [
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train | QueryFilter.apply | Filter queries to only those owned by current user if
can_only_access_owned_queries permission is set.
:returns: query | superset/views/sql_lab.py | def apply(
self,
query: BaseQuery,
func: Callable) -> BaseQuery:
"""
Filter queries to only those owned by current user if
can_only_access_owned_queries permission is set.
:returns: query
"""
if security_manager.can_only_access_owned_queries():
query = (
query
.filter(Query.user_id == g.user.get_user_id())
)
return query | def apply(
self,
query: BaseQuery,
func: Callable) -> BaseQuery:
"""
Filter queries to only those owned by current user if
can_only_access_owned_queries permission is set.
:returns: query
"""
if security_manager.can_only_access_owned_queries():
query = (
query
.filter(Query.user_id == g.user.get_user_id())
)
return query | [
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train | TableModelView.edit | Simple hack to redirect to explore view after saving | superset/connectors/sqla/views.py | def edit(self, pk):
"""Simple hack to redirect to explore view after saving"""
resp = super(TableModelView, self).edit(pk)
if isinstance(resp, str):
return resp
return redirect('/superset/explore/table/{}/'.format(pk)) | def edit(self, pk):
"""Simple hack to redirect to explore view after saving"""
resp = super(TableModelView, self).edit(pk)
if isinstance(resp, str):
return resp
return redirect('/superset/explore/table/{}/'.format(pk)) | [
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train | get_language_pack | Get/cache a language pack
Returns the langugage pack from cache if it exists, caches otherwise
>>> get_language_pack('fr')['Dashboards']
"Tableaux de bords" | superset/translations/utils.py | def get_language_pack(locale):
"""Get/cache a language pack
Returns the langugage pack from cache if it exists, caches otherwise
>>> get_language_pack('fr')['Dashboards']
"Tableaux de bords"
"""
pack = ALL_LANGUAGE_PACKS.get(locale)
if not pack:
filename = DIR + '/{}/LC_MESSAGES/messages.json'.format(locale)
try:
with open(filename) as f:
pack = json.load(f)
ALL_LANGUAGE_PACKS[locale] = pack
except Exception:
# Assuming english, client side falls back on english
pass
return pack | def get_language_pack(locale):
"""Get/cache a language pack
Returns the langugage pack from cache if it exists, caches otherwise
>>> get_language_pack('fr')['Dashboards']
"Tableaux de bords"
"""
pack = ALL_LANGUAGE_PACKS.get(locale)
if not pack:
filename = DIR + '/{}/LC_MESSAGES/messages.json'.format(locale)
try:
with open(filename) as f:
pack = json.load(f)
ALL_LANGUAGE_PACKS[locale] = pack
except Exception:
# Assuming english, client side falls back on english
pass
return pack | [
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train | get_form_data | Build `form_data` for chart GET request from dashboard's `default_filters`.
When a dashboard has `default_filters` they need to be added as extra
filters in the GET request for charts. | superset/tasks/cache.py | def get_form_data(chart_id, dashboard=None):
"""
Build `form_data` for chart GET request from dashboard's `default_filters`.
When a dashboard has `default_filters` they need to be added as extra
filters in the GET request for charts.
"""
form_data = {'slice_id': chart_id}
if dashboard is None or not dashboard.json_metadata:
return form_data
json_metadata = json.loads(dashboard.json_metadata)
# do not apply filters if chart is immune to them
if chart_id in json_metadata.get('filter_immune_slices', []):
return form_data
default_filters = json.loads(json_metadata.get('default_filters', 'null'))
if not default_filters:
return form_data
# are some of the fields in the chart immune to filters?
filter_immune_slice_fields = json_metadata.get('filter_immune_slice_fields', {})
immune_fields = filter_immune_slice_fields.get(str(chart_id), [])
extra_filters = []
for filters in default_filters.values():
for col, val in filters.items():
if col not in immune_fields:
extra_filters.append({'col': col, 'op': 'in', 'val': val})
if extra_filters:
form_data['extra_filters'] = extra_filters
return form_data | def get_form_data(chart_id, dashboard=None):
"""
Build `form_data` for chart GET request from dashboard's `default_filters`.
When a dashboard has `default_filters` they need to be added as extra
filters in the GET request for charts.
"""
form_data = {'slice_id': chart_id}
if dashboard is None or not dashboard.json_metadata:
return form_data
json_metadata = json.loads(dashboard.json_metadata)
# do not apply filters if chart is immune to them
if chart_id in json_metadata.get('filter_immune_slices', []):
return form_data
default_filters = json.loads(json_metadata.get('default_filters', 'null'))
if not default_filters:
return form_data
# are some of the fields in the chart immune to filters?
filter_immune_slice_fields = json_metadata.get('filter_immune_slice_fields', {})
immune_fields = filter_immune_slice_fields.get(str(chart_id), [])
extra_filters = []
for filters in default_filters.values():
for col, val in filters.items():
if col not in immune_fields:
extra_filters.append({'col': col, 'op': 'in', 'val': val})
if extra_filters:
form_data['extra_filters'] = extra_filters
return form_data | [
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train | get_url | Return external URL for warming up a given chart/table cache. | superset/tasks/cache.py | def get_url(params):
"""Return external URL for warming up a given chart/table cache."""
baseurl = 'http://{SUPERSET_WEBSERVER_ADDRESS}:{SUPERSET_WEBSERVER_PORT}/'.format(
**app.config)
with app.test_request_context():
return urllib.parse.urljoin(
baseurl,
url_for('Superset.explore_json', **params),
) | def get_url(params):
"""Return external URL for warming up a given chart/table cache."""
baseurl = 'http://{SUPERSET_WEBSERVER_ADDRESS}:{SUPERSET_WEBSERVER_PORT}/'.format(
**app.config)
with app.test_request_context():
return urllib.parse.urljoin(
baseurl,
url_for('Superset.explore_json', **params),
) | [
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train | cache_warmup | Warm up cache.
This task periodically hits charts to warm up the cache. | superset/tasks/cache.py | def cache_warmup(strategy_name, *args, **kwargs):
"""
Warm up cache.
This task periodically hits charts to warm up the cache.
"""
logger.info('Loading strategy')
class_ = None
for class_ in strategies:
if class_.name == strategy_name:
break
else:
message = f'No strategy {strategy_name} found!'
logger.error(message)
return message
logger.info(f'Loading {class_.__name__}')
try:
strategy = class_(*args, **kwargs)
logger.info('Success!')
except TypeError:
message = 'Error loading strategy!'
logger.exception(message)
return message
results = {'success': [], 'errors': []}
for url in strategy.get_urls():
try:
logger.info(f'Fetching {url}')
requests.get(url)
results['success'].append(url)
except RequestException:
logger.exception('Error warming up cache!')
results['errors'].append(url)
return results | def cache_warmup(strategy_name, *args, **kwargs):
"""
Warm up cache.
This task periodically hits charts to warm up the cache.
"""
logger.info('Loading strategy')
class_ = None
for class_ in strategies:
if class_.name == strategy_name:
break
else:
message = f'No strategy {strategy_name} found!'
logger.error(message)
return message
logger.info(f'Loading {class_.__name__}')
try:
strategy = class_(*args, **kwargs)
logger.info('Success!')
except TypeError:
message = 'Error loading strategy!'
logger.exception(message)
return message
results = {'success': [], 'errors': []}
for url in strategy.get_urls():
try:
logger.info(f'Fetching {url}')
requests.get(url)
results['success'].append(url)
except RequestException:
logger.exception('Error warming up cache!')
results['errors'].append(url)
return results | [
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train | fetch_logs | Mocked. Retrieve the logs produced by the execution of the query.
Can be called multiple times to fetch the logs produced after
the previous call.
:returns: list<str>
:raises: ``ProgrammingError`` when no query has been started
.. note::
This is not a part of DB-API. | superset/db_engines/hive.py | def fetch_logs(self, max_rows=1024,
orientation=None):
"""Mocked. Retrieve the logs produced by the execution of the query.
Can be called multiple times to fetch the logs produced after
the previous call.
:returns: list<str>
:raises: ``ProgrammingError`` when no query has been started
.. note::
This is not a part of DB-API.
"""
from pyhive import hive
from TCLIService import ttypes
from thrift import Thrift
orientation = orientation or ttypes.TFetchOrientation.FETCH_NEXT
try:
req = ttypes.TGetLogReq(operationHandle=self._operationHandle)
logs = self._connection.client.GetLog(req).log
return logs
# raised if Hive is used
except (ttypes.TApplicationException,
Thrift.TApplicationException):
if self._state == self._STATE_NONE:
raise hive.ProgrammingError('No query yet')
logs = []
while True:
req = ttypes.TFetchResultsReq(
operationHandle=self._operationHandle,
orientation=ttypes.TFetchOrientation.FETCH_NEXT,
maxRows=self.arraysize,
fetchType=1, # 0: results, 1: logs
)
response = self._connection.client.FetchResults(req)
hive._check_status(response)
assert not response.results.rows, \
'expected data in columnar format'
assert len(response.results.columns) == 1, response.results.columns
new_logs = hive._unwrap_column(response.results.columns[0])
logs += new_logs
if not new_logs:
break
return '\n'.join(logs) | def fetch_logs(self, max_rows=1024,
orientation=None):
"""Mocked. Retrieve the logs produced by the execution of the query.
Can be called multiple times to fetch the logs produced after
the previous call.
:returns: list<str>
:raises: ``ProgrammingError`` when no query has been started
.. note::
This is not a part of DB-API.
"""
from pyhive import hive
from TCLIService import ttypes
from thrift import Thrift
orientation = orientation or ttypes.TFetchOrientation.FETCH_NEXT
try:
req = ttypes.TGetLogReq(operationHandle=self._operationHandle)
logs = self._connection.client.GetLog(req).log
return logs
# raised if Hive is used
except (ttypes.TApplicationException,
Thrift.TApplicationException):
if self._state == self._STATE_NONE:
raise hive.ProgrammingError('No query yet')
logs = []
while True:
req = ttypes.TFetchResultsReq(
operationHandle=self._operationHandle,
orientation=ttypes.TFetchOrientation.FETCH_NEXT,
maxRows=self.arraysize,
fetchType=1, # 0: results, 1: logs
)
response = self._connection.client.FetchResults(req)
hive._check_status(response)
assert not response.results.rows, \
'expected data in columnar format'
assert len(response.results.columns) == 1, response.results.columns
new_logs = hive._unwrap_column(response.results.columns[0])
logs += new_logs
if not new_logs:
break
return '\n'.join(logs) | [
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train | DruidCluster.refresh_datasources | Refresh metadata of all datasources in the cluster
If ``datasource_name`` is specified, only that datasource is updated | superset/connectors/druid/models.py | def refresh_datasources(
self,
datasource_name=None,
merge_flag=True,
refreshAll=True):
"""Refresh metadata of all datasources in the cluster
If ``datasource_name`` is specified, only that datasource is updated
"""
ds_list = self.get_datasources()
blacklist = conf.get('DRUID_DATA_SOURCE_BLACKLIST', [])
ds_refresh = []
if not datasource_name:
ds_refresh = list(filter(lambda ds: ds not in blacklist, ds_list))
elif datasource_name not in blacklist and datasource_name in ds_list:
ds_refresh.append(datasource_name)
else:
return
self.refresh(ds_refresh, merge_flag, refreshAll) | def refresh_datasources(
self,
datasource_name=None,
merge_flag=True,
refreshAll=True):
"""Refresh metadata of all datasources in the cluster
If ``datasource_name`` is specified, only that datasource is updated
"""
ds_list = self.get_datasources()
blacklist = conf.get('DRUID_DATA_SOURCE_BLACKLIST', [])
ds_refresh = []
if not datasource_name:
ds_refresh = list(filter(lambda ds: ds not in blacklist, ds_list))
elif datasource_name not in blacklist and datasource_name in ds_list:
ds_refresh.append(datasource_name)
else:
return
self.refresh(ds_refresh, merge_flag, refreshAll) | [
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train | DruidCluster.refresh | Fetches metadata for the specified datasources and
merges to the Superset database | superset/connectors/druid/models.py | def refresh(self, datasource_names, merge_flag, refreshAll):
"""
Fetches metadata for the specified datasources and
merges to the Superset database
"""
session = db.session
ds_list = (
session.query(DruidDatasource)
.filter(DruidDatasource.cluster_name == self.cluster_name)
.filter(DruidDatasource.datasource_name.in_(datasource_names))
)
ds_map = {ds.name: ds for ds in ds_list}
for ds_name in datasource_names:
datasource = ds_map.get(ds_name, None)
if not datasource:
datasource = DruidDatasource(datasource_name=ds_name)
with session.no_autoflush:
session.add(datasource)
flasher(
_('Adding new datasource [{}]').format(ds_name), 'success')
ds_map[ds_name] = datasource
elif refreshAll:
flasher(
_('Refreshing datasource [{}]').format(ds_name), 'info')
else:
del ds_map[ds_name]
continue
datasource.cluster = self
datasource.merge_flag = merge_flag
session.flush()
# Prepare multithreaded executation
pool = ThreadPool()
ds_refresh = list(ds_map.values())
metadata = pool.map(_fetch_metadata_for, ds_refresh)
pool.close()
pool.join()
for i in range(0, len(ds_refresh)):
datasource = ds_refresh[i]
cols = metadata[i]
if cols:
col_objs_list = (
session.query(DruidColumn)
.filter(DruidColumn.datasource_id == datasource.id)
.filter(DruidColumn.column_name.in_(cols.keys()))
)
col_objs = {col.column_name: col for col in col_objs_list}
for col in cols:
if col == '__time': # skip the time column
continue
col_obj = col_objs.get(col)
if not col_obj:
col_obj = DruidColumn(
datasource_id=datasource.id,
column_name=col)
with session.no_autoflush:
session.add(col_obj)
col_obj.type = cols[col]['type']
col_obj.datasource = datasource
if col_obj.type == 'STRING':
col_obj.groupby = True
col_obj.filterable = True
datasource.refresh_metrics()
session.commit() | def refresh(self, datasource_names, merge_flag, refreshAll):
"""
Fetches metadata for the specified datasources and
merges to the Superset database
"""
session = db.session
ds_list = (
session.query(DruidDatasource)
.filter(DruidDatasource.cluster_name == self.cluster_name)
.filter(DruidDatasource.datasource_name.in_(datasource_names))
)
ds_map = {ds.name: ds for ds in ds_list}
for ds_name in datasource_names:
datasource = ds_map.get(ds_name, None)
if not datasource:
datasource = DruidDatasource(datasource_name=ds_name)
with session.no_autoflush:
session.add(datasource)
flasher(
_('Adding new datasource [{}]').format(ds_name), 'success')
ds_map[ds_name] = datasource
elif refreshAll:
flasher(
_('Refreshing datasource [{}]').format(ds_name), 'info')
else:
del ds_map[ds_name]
continue
datasource.cluster = self
datasource.merge_flag = merge_flag
session.flush()
# Prepare multithreaded executation
pool = ThreadPool()
ds_refresh = list(ds_map.values())
metadata = pool.map(_fetch_metadata_for, ds_refresh)
pool.close()
pool.join()
for i in range(0, len(ds_refresh)):
datasource = ds_refresh[i]
cols = metadata[i]
if cols:
col_objs_list = (
session.query(DruidColumn)
.filter(DruidColumn.datasource_id == datasource.id)
.filter(DruidColumn.column_name.in_(cols.keys()))
)
col_objs = {col.column_name: col for col in col_objs_list}
for col in cols:
if col == '__time': # skip the time column
continue
col_obj = col_objs.get(col)
if not col_obj:
col_obj = DruidColumn(
datasource_id=datasource.id,
column_name=col)
with session.no_autoflush:
session.add(col_obj)
col_obj.type = cols[col]['type']
col_obj.datasource = datasource
if col_obj.type == 'STRING':
col_obj.groupby = True
col_obj.filterable = True
datasource.refresh_metrics()
session.commit() | [
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train | DruidColumn.refresh_metrics | Refresh metrics based on the column metadata | superset/connectors/druid/models.py | def refresh_metrics(self):
"""Refresh metrics based on the column metadata"""
metrics = self.get_metrics()
dbmetrics = (
db.session.query(DruidMetric)
.filter(DruidMetric.datasource_id == self.datasource_id)
.filter(DruidMetric.metric_name.in_(metrics.keys()))
)
dbmetrics = {metric.metric_name: metric for metric in dbmetrics}
for metric in metrics.values():
dbmetric = dbmetrics.get(metric.metric_name)
if dbmetric:
for attr in ['json', 'metric_type']:
setattr(dbmetric, attr, getattr(metric, attr))
else:
with db.session.no_autoflush:
metric.datasource_id = self.datasource_id
db.session.add(metric) | def refresh_metrics(self):
"""Refresh metrics based on the column metadata"""
metrics = self.get_metrics()
dbmetrics = (
db.session.query(DruidMetric)
.filter(DruidMetric.datasource_id == self.datasource_id)
.filter(DruidMetric.metric_name.in_(metrics.keys()))
)
dbmetrics = {metric.metric_name: metric for metric in dbmetrics}
for metric in metrics.values():
dbmetric = dbmetrics.get(metric.metric_name)
if dbmetric:
for attr in ['json', 'metric_type']:
setattr(dbmetric, attr, getattr(metric, attr))
else:
with db.session.no_autoflush:
metric.datasource_id = self.datasource_id
db.session.add(metric) | [
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train | DruidDatasource.import_obj | Imports the datasource from the object to the database.
Metrics and columns and datasource will be overridden if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over. | superset/connectors/druid/models.py | def import_obj(cls, i_datasource, import_time=None):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overridden if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over.
"""
def lookup_datasource(d):
return db.session.query(DruidDatasource).filter(
DruidDatasource.datasource_name == d.datasource_name,
DruidCluster.cluster_name == d.cluster_name,
).first()
def lookup_cluster(d):
return db.session.query(DruidCluster).filter_by(
cluster_name=d.cluster_name).one()
return import_datasource.import_datasource(
db.session, i_datasource, lookup_cluster, lookup_datasource,
import_time) | def import_obj(cls, i_datasource, import_time=None):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overridden if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over.
"""
def lookup_datasource(d):
return db.session.query(DruidDatasource).filter(
DruidDatasource.datasource_name == d.datasource_name,
DruidCluster.cluster_name == d.cluster_name,
).first()
def lookup_cluster(d):
return db.session.query(DruidCluster).filter_by(
cluster_name=d.cluster_name).one()
return import_datasource.import_datasource(
db.session, i_datasource, lookup_cluster, lookup_datasource,
import_time) | [
"Imports",
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/druid/models.py#L514-L532 | [
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train | DruidDatasource.sync_to_db_from_config | Merges the ds config from druid_config into one stored in the db. | superset/connectors/druid/models.py | def sync_to_db_from_config(
cls,
druid_config,
user,
cluster,
refresh=True):
"""Merges the ds config from druid_config into one stored in the db."""
session = db.session
datasource = (
session.query(cls)
.filter_by(datasource_name=druid_config['name'])
.first()
)
# Create a new datasource.
if not datasource:
datasource = cls(
datasource_name=druid_config['name'],
cluster=cluster,
owners=[user],
changed_by_fk=user.id,
created_by_fk=user.id,
)
session.add(datasource)
elif not refresh:
return
dimensions = druid_config['dimensions']
col_objs = (
session.query(DruidColumn)
.filter(DruidColumn.datasource_id == datasource.id)
.filter(DruidColumn.column_name.in_(dimensions))
)
col_objs = {col.column_name: col for col in col_objs}
for dim in dimensions:
col_obj = col_objs.get(dim, None)
if not col_obj:
col_obj = DruidColumn(
datasource_id=datasource.id,
column_name=dim,
groupby=True,
filterable=True,
# TODO: fetch type from Hive.
type='STRING',
datasource=datasource,
)
session.add(col_obj)
# Import Druid metrics
metric_objs = (
session.query(DruidMetric)
.filter(DruidMetric.datasource_id == datasource.id)
.filter(DruidMetric.metric_name.in_(
spec['name'] for spec in druid_config['metrics_spec']
))
)
metric_objs = {metric.metric_name: metric for metric in metric_objs}
for metric_spec in druid_config['metrics_spec']:
metric_name = metric_spec['name']
metric_type = metric_spec['type']
metric_json = json.dumps(metric_spec)
if metric_type == 'count':
metric_type = 'longSum'
metric_json = json.dumps({
'type': 'longSum',
'name': metric_name,
'fieldName': metric_name,
})
metric_obj = metric_objs.get(metric_name, None)
if not metric_obj:
metric_obj = DruidMetric(
metric_name=metric_name,
metric_type=metric_type,
verbose_name='%s(%s)' % (metric_type, metric_name),
datasource=datasource,
json=metric_json,
description=(
'Imported from the airolap config dir for %s' %
druid_config['name']),
)
session.add(metric_obj)
session.commit() | def sync_to_db_from_config(
cls,
druid_config,
user,
cluster,
refresh=True):
"""Merges the ds config from druid_config into one stored in the db."""
session = db.session
datasource = (
session.query(cls)
.filter_by(datasource_name=druid_config['name'])
.first()
)
# Create a new datasource.
if not datasource:
datasource = cls(
datasource_name=druid_config['name'],
cluster=cluster,
owners=[user],
changed_by_fk=user.id,
created_by_fk=user.id,
)
session.add(datasource)
elif not refresh:
return
dimensions = druid_config['dimensions']
col_objs = (
session.query(DruidColumn)
.filter(DruidColumn.datasource_id == datasource.id)
.filter(DruidColumn.column_name.in_(dimensions))
)
col_objs = {col.column_name: col for col in col_objs}
for dim in dimensions:
col_obj = col_objs.get(dim, None)
if not col_obj:
col_obj = DruidColumn(
datasource_id=datasource.id,
column_name=dim,
groupby=True,
filterable=True,
# TODO: fetch type from Hive.
type='STRING',
datasource=datasource,
)
session.add(col_obj)
# Import Druid metrics
metric_objs = (
session.query(DruidMetric)
.filter(DruidMetric.datasource_id == datasource.id)
.filter(DruidMetric.metric_name.in_(
spec['name'] for spec in druid_config['metrics_spec']
))
)
metric_objs = {metric.metric_name: metric for metric in metric_objs}
for metric_spec in druid_config['metrics_spec']:
metric_name = metric_spec['name']
metric_type = metric_spec['type']
metric_json = json.dumps(metric_spec)
if metric_type == 'count':
metric_type = 'longSum'
metric_json = json.dumps({
'type': 'longSum',
'name': metric_name,
'fieldName': metric_name,
})
metric_obj = metric_objs.get(metric_name, None)
if not metric_obj:
metric_obj = DruidMetric(
metric_name=metric_name,
metric_type=metric_type,
verbose_name='%s(%s)' % (metric_type, metric_name),
datasource=datasource,
json=metric_json,
description=(
'Imported from the airolap config dir for %s' %
druid_config['name']),
)
session.add(metric_obj)
session.commit() | [
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train | DruidDatasource.get_post_agg | For a metric specified as `postagg` returns the
kind of post aggregation for pydruid. | superset/connectors/druid/models.py | def get_post_agg(mconf):
"""
For a metric specified as `postagg` returns the
kind of post aggregation for pydruid.
"""
if mconf.get('type') == 'javascript':
return JavascriptPostAggregator(
name=mconf.get('name', ''),
field_names=mconf.get('fieldNames', []),
function=mconf.get('function', ''))
elif mconf.get('type') == 'quantile':
return Quantile(
mconf.get('name', ''),
mconf.get('probability', ''),
)
elif mconf.get('type') == 'quantiles':
return Quantiles(
mconf.get('name', ''),
mconf.get('probabilities', ''),
)
elif mconf.get('type') == 'fieldAccess':
return Field(mconf.get('name'))
elif mconf.get('type') == 'constant':
return Const(
mconf.get('value'),
output_name=mconf.get('name', ''),
)
elif mconf.get('type') == 'hyperUniqueCardinality':
return HyperUniqueCardinality(
mconf.get('name'),
)
elif mconf.get('type') == 'arithmetic':
return Postaggregator(
mconf.get('fn', '/'),
mconf.get('fields', []),
mconf.get('name', ''))
else:
return CustomPostAggregator(
mconf.get('name', ''),
mconf) | def get_post_agg(mconf):
"""
For a metric specified as `postagg` returns the
kind of post aggregation for pydruid.
"""
if mconf.get('type') == 'javascript':
return JavascriptPostAggregator(
name=mconf.get('name', ''),
field_names=mconf.get('fieldNames', []),
function=mconf.get('function', ''))
elif mconf.get('type') == 'quantile':
return Quantile(
mconf.get('name', ''),
mconf.get('probability', ''),
)
elif mconf.get('type') == 'quantiles':
return Quantiles(
mconf.get('name', ''),
mconf.get('probabilities', ''),
)
elif mconf.get('type') == 'fieldAccess':
return Field(mconf.get('name'))
elif mconf.get('type') == 'constant':
return Const(
mconf.get('value'),
output_name=mconf.get('name', ''),
)
elif mconf.get('type') == 'hyperUniqueCardinality':
return HyperUniqueCardinality(
mconf.get('name'),
)
elif mconf.get('type') == 'arithmetic':
return Postaggregator(
mconf.get('fn', '/'),
mconf.get('fields', []),
mconf.get('name', ''))
else:
return CustomPostAggregator(
mconf.get('name', ''),
mconf) | [
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train | DruidDatasource.find_postaggs_for | Return a list of metrics that are post aggregations | superset/connectors/druid/models.py | def find_postaggs_for(postagg_names, metrics_dict):
"""Return a list of metrics that are post aggregations"""
postagg_metrics = [
metrics_dict[name] for name in postagg_names
if metrics_dict[name].metric_type == POST_AGG_TYPE
]
# Remove post aggregations that were found
for postagg in postagg_metrics:
postagg_names.remove(postagg.metric_name)
return postagg_metrics | def find_postaggs_for(postagg_names, metrics_dict):
"""Return a list of metrics that are post aggregations"""
postagg_metrics = [
metrics_dict[name] for name in postagg_names
if metrics_dict[name].metric_type == POST_AGG_TYPE
]
# Remove post aggregations that were found
for postagg in postagg_metrics:
postagg_names.remove(postagg.metric_name)
return postagg_metrics | [
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train | DruidDatasource.values_for_column | Retrieve some values for the given column | superset/connectors/druid/models.py | def values_for_column(self,
column_name,
limit=10000):
"""Retrieve some values for the given column"""
logging.info(
'Getting values for columns [{}] limited to [{}]'
.format(column_name, limit))
# TODO: Use Lexicographic TopNMetricSpec once supported by PyDruid
if self.fetch_values_from:
from_dttm = utils.parse_human_datetime(self.fetch_values_from)
else:
from_dttm = datetime(1970, 1, 1)
qry = dict(
datasource=self.datasource_name,
granularity='all',
intervals=from_dttm.isoformat() + '/' + datetime.now().isoformat(),
aggregations=dict(count=count('count')),
dimension=column_name,
metric='count',
threshold=limit,
)
client = self.cluster.get_pydruid_client()
client.topn(**qry)
df = client.export_pandas()
return [row[column_name] for row in df.to_records(index=False)] | def values_for_column(self,
column_name,
limit=10000):
"""Retrieve some values for the given column"""
logging.info(
'Getting values for columns [{}] limited to [{}]'
.format(column_name, limit))
# TODO: Use Lexicographic TopNMetricSpec once supported by PyDruid
if self.fetch_values_from:
from_dttm = utils.parse_human_datetime(self.fetch_values_from)
else:
from_dttm = datetime(1970, 1, 1)
qry = dict(
datasource=self.datasource_name,
granularity='all',
intervals=from_dttm.isoformat() + '/' + datetime.now().isoformat(),
aggregations=dict(count=count('count')),
dimension=column_name,
metric='count',
threshold=limit,
)
client = self.cluster.get_pydruid_client()
client.topn(**qry)
df = client.export_pandas()
return [row[column_name] for row in df.to_records(index=False)] | [
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train | DruidDatasource.get_aggregations | Returns a dictionary of aggregation metric names to aggregation json objects
:param metrics_dict: dictionary of all the metrics
:param saved_metrics: list of saved metric names
:param adhoc_metrics: list of adhoc metric names
:raise SupersetException: if one or more metric names are not aggregations | superset/connectors/druid/models.py | def get_aggregations(metrics_dict, saved_metrics, adhoc_metrics=[]):
"""
Returns a dictionary of aggregation metric names to aggregation json objects
:param metrics_dict: dictionary of all the metrics
:param saved_metrics: list of saved metric names
:param adhoc_metrics: list of adhoc metric names
:raise SupersetException: if one or more metric names are not aggregations
"""
aggregations = OrderedDict()
invalid_metric_names = []
for metric_name in saved_metrics:
if metric_name in metrics_dict:
metric = metrics_dict[metric_name]
if metric.metric_type == POST_AGG_TYPE:
invalid_metric_names.append(metric_name)
else:
aggregations[metric_name] = metric.json_obj
else:
invalid_metric_names.append(metric_name)
if len(invalid_metric_names) > 0:
raise SupersetException(
_('Metric(s) {} must be aggregations.').format(invalid_metric_names))
for adhoc_metric in adhoc_metrics:
aggregations[adhoc_metric['label']] = {
'fieldName': adhoc_metric['column']['column_name'],
'fieldNames': [adhoc_metric['column']['column_name']],
'type': DruidDatasource.druid_type_from_adhoc_metric(adhoc_metric),
'name': adhoc_metric['label'],
}
return aggregations | def get_aggregations(metrics_dict, saved_metrics, adhoc_metrics=[]):
"""
Returns a dictionary of aggregation metric names to aggregation json objects
:param metrics_dict: dictionary of all the metrics
:param saved_metrics: list of saved metric names
:param adhoc_metrics: list of adhoc metric names
:raise SupersetException: if one or more metric names are not aggregations
"""
aggregations = OrderedDict()
invalid_metric_names = []
for metric_name in saved_metrics:
if metric_name in metrics_dict:
metric = metrics_dict[metric_name]
if metric.metric_type == POST_AGG_TYPE:
invalid_metric_names.append(metric_name)
else:
aggregations[metric_name] = metric.json_obj
else:
invalid_metric_names.append(metric_name)
if len(invalid_metric_names) > 0:
raise SupersetException(
_('Metric(s) {} must be aggregations.').format(invalid_metric_names))
for adhoc_metric in adhoc_metrics:
aggregations[adhoc_metric['label']] = {
'fieldName': adhoc_metric['column']['column_name'],
'fieldNames': [adhoc_metric['column']['column_name']],
'type': DruidDatasource.druid_type_from_adhoc_metric(adhoc_metric),
'name': adhoc_metric['label'],
}
return aggregations | [
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train | DruidDatasource._dimensions_to_values | Replace dimensions specs with their `dimension`
values, and ignore those without | superset/connectors/druid/models.py | def _dimensions_to_values(dimensions):
"""
Replace dimensions specs with their `dimension`
values, and ignore those without
"""
values = []
for dimension in dimensions:
if isinstance(dimension, dict):
if 'extractionFn' in dimension:
values.append(dimension)
elif 'dimension' in dimension:
values.append(dimension['dimension'])
else:
values.append(dimension)
return values | def _dimensions_to_values(dimensions):
"""
Replace dimensions specs with their `dimension`
values, and ignore those without
"""
values = []
for dimension in dimensions:
if isinstance(dimension, dict):
if 'extractionFn' in dimension:
values.append(dimension)
elif 'dimension' in dimension:
values.append(dimension['dimension'])
else:
values.append(dimension)
return values | [
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train | DruidDatasource.run_query | Runs a query against Druid and returns a dataframe. | superset/connectors/druid/models.py | def run_query( # noqa / druid
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=None,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None, inner_to_dttm=None,
orderby=None,
extras=None, # noqa
columns=None, phase=2, client=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Runs a query against Druid and returns a dataframe.
"""
# TODO refactor into using a TBD Query object
client = client or self.cluster.get_pydruid_client()
row_limit = row_limit or conf.get('ROW_LIMIT')
if not is_timeseries:
granularity = 'all'
if granularity == 'all':
phase = 1
inner_from_dttm = inner_from_dttm or from_dttm
inner_to_dttm = inner_to_dttm or to_dttm
timezone = from_dttm.replace(tzinfo=DRUID_TZ).tzname() if from_dttm else None
query_str = ''
metrics_dict = {m.metric_name: m for m in self.metrics}
columns_dict = {c.column_name: c for c in self.columns}
if (
self.cluster and
LooseVersion(self.cluster.get_druid_version()) < LooseVersion('0.11.0')
):
for metric in metrics:
self.sanitize_metric_object(metric)
self.sanitize_metric_object(timeseries_limit_metric)
aggregations, post_aggs = DruidDatasource.metrics_and_post_aggs(
metrics,
metrics_dict)
self.check_restricted_metrics(aggregations)
# the dimensions list with dimensionSpecs expanded
dimensions = self.get_dimensions(groupby, columns_dict)
extras = extras or {}
qry = dict(
datasource=self.datasource_name,
dimensions=dimensions,
aggregations=aggregations,
granularity=DruidDatasource.granularity(
granularity,
timezone=timezone,
origin=extras.get('druid_time_origin'),
),
post_aggregations=post_aggs,
intervals=self.intervals_from_dttms(from_dttm, to_dttm),
)
filters = DruidDatasource.get_filters(filter, self.num_cols, columns_dict)
if filters:
qry['filter'] = filters
having_filters = self.get_having_filters(extras.get('having_druid'))
if having_filters:
qry['having'] = having_filters
order_direction = 'descending' if order_desc else 'ascending'
if columns:
columns.append('__time')
del qry['post_aggregations']
del qry['aggregations']
qry['dimensions'] = columns
qry['metrics'] = []
qry['granularity'] = 'all'
qry['limit'] = row_limit
client.scan(**qry)
elif len(groupby) == 0 and not having_filters:
logging.info('Running timeseries query for no groupby values')
del qry['dimensions']
client.timeseries(**qry)
elif (
not having_filters and
len(groupby) == 1 and
order_desc
):
dim = list(qry.get('dimensions'))[0]
logging.info('Running two-phase topn query for dimension [{}]'.format(dim))
pre_qry = deepcopy(qry)
if timeseries_limit_metric:
order_by = utils.get_metric_name(timeseries_limit_metric)
aggs_dict, post_aggs_dict = DruidDatasource.metrics_and_post_aggs(
[timeseries_limit_metric],
metrics_dict)
if phase == 1:
pre_qry['aggregations'].update(aggs_dict)
pre_qry['post_aggregations'].update(post_aggs_dict)
else:
pre_qry['aggregations'] = aggs_dict
pre_qry['post_aggregations'] = post_aggs_dict
else:
agg_keys = qry['aggregations'].keys()
order_by = list(agg_keys)[0] if agg_keys else None
# Limit on the number of timeseries, doing a two-phases query
pre_qry['granularity'] = 'all'
pre_qry['threshold'] = min(row_limit,
timeseries_limit or row_limit)
pre_qry['metric'] = order_by
pre_qry['dimension'] = self._dimensions_to_values(qry.get('dimensions'))[0]
del pre_qry['dimensions']
client.topn(**pre_qry)
logging.info('Phase 1 Complete')
if phase == 2:
query_str += '// Two phase query\n// Phase 1\n'
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
query_str += '\n'
if phase == 1:
return query_str
query_str += (
"// Phase 2 (built based on phase one's results)\n")
df = client.export_pandas()
qry['filter'] = self._add_filter_from_pre_query_data(
df,
[pre_qry['dimension']],
filters)
qry['threshold'] = timeseries_limit or 1000
if row_limit and granularity == 'all':
qry['threshold'] = row_limit
qry['dimension'] = dim
del qry['dimensions']
qry['metric'] = list(qry['aggregations'].keys())[0]
client.topn(**qry)
logging.info('Phase 2 Complete')
elif len(groupby) > 0 or having_filters:
# If grouping on multiple fields or using a having filter
# we have to force a groupby query
logging.info('Running groupby query for dimensions [{}]'.format(dimensions))
if timeseries_limit and is_timeseries:
logging.info('Running two-phase query for timeseries')
pre_qry = deepcopy(qry)
pre_qry_dims = self._dimensions_to_values(qry['dimensions'])
# Can't use set on an array with dicts
# Use set with non-dict items only
non_dict_dims = list(
set([x for x in pre_qry_dims if not isinstance(x, dict)]),
)
dict_dims = [x for x in pre_qry_dims if isinstance(x, dict)]
pre_qry['dimensions'] = non_dict_dims + dict_dims
order_by = None
if metrics:
order_by = utils.get_metric_name(metrics[0])
else:
order_by = pre_qry_dims[0]
if timeseries_limit_metric:
order_by = utils.get_metric_name(timeseries_limit_metric)
aggs_dict, post_aggs_dict = DruidDatasource.metrics_and_post_aggs(
[timeseries_limit_metric],
metrics_dict)
if phase == 1:
pre_qry['aggregations'].update(aggs_dict)
pre_qry['post_aggregations'].update(post_aggs_dict)
else:
pre_qry['aggregations'] = aggs_dict
pre_qry['post_aggregations'] = post_aggs_dict
# Limit on the number of timeseries, doing a two-phases query
pre_qry['granularity'] = 'all'
pre_qry['limit_spec'] = {
'type': 'default',
'limit': min(timeseries_limit, row_limit),
'intervals': self.intervals_from_dttms(
inner_from_dttm, inner_to_dttm),
'columns': [{
'dimension': order_by,
'direction': order_direction,
}],
}
client.groupby(**pre_qry)
logging.info('Phase 1 Complete')
query_str += '// Two phase query\n// Phase 1\n'
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
query_str += '\n'
if phase == 1:
return query_str
query_str += (
"// Phase 2 (built based on phase one's results)\n")
df = client.export_pandas()
qry['filter'] = self._add_filter_from_pre_query_data(
df,
pre_qry['dimensions'],
filters,
)
qry['limit_spec'] = None
if row_limit:
dimension_values = self._dimensions_to_values(dimensions)
qry['limit_spec'] = {
'type': 'default',
'limit': row_limit,
'columns': [{
'dimension': (
utils.get_metric_name(
metrics[0],
) if metrics else dimension_values[0]
),
'direction': order_direction,
}],
}
client.groupby(**qry)
logging.info('Query Complete')
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
return query_str | def run_query( # noqa / druid
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=None,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None, inner_to_dttm=None,
orderby=None,
extras=None, # noqa
columns=None, phase=2, client=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Runs a query against Druid and returns a dataframe.
"""
# TODO refactor into using a TBD Query object
client = client or self.cluster.get_pydruid_client()
row_limit = row_limit or conf.get('ROW_LIMIT')
if not is_timeseries:
granularity = 'all'
if granularity == 'all':
phase = 1
inner_from_dttm = inner_from_dttm or from_dttm
inner_to_dttm = inner_to_dttm or to_dttm
timezone = from_dttm.replace(tzinfo=DRUID_TZ).tzname() if from_dttm else None
query_str = ''
metrics_dict = {m.metric_name: m for m in self.metrics}
columns_dict = {c.column_name: c for c in self.columns}
if (
self.cluster and
LooseVersion(self.cluster.get_druid_version()) < LooseVersion('0.11.0')
):
for metric in metrics:
self.sanitize_metric_object(metric)
self.sanitize_metric_object(timeseries_limit_metric)
aggregations, post_aggs = DruidDatasource.metrics_and_post_aggs(
metrics,
metrics_dict)
self.check_restricted_metrics(aggregations)
# the dimensions list with dimensionSpecs expanded
dimensions = self.get_dimensions(groupby, columns_dict)
extras = extras or {}
qry = dict(
datasource=self.datasource_name,
dimensions=dimensions,
aggregations=aggregations,
granularity=DruidDatasource.granularity(
granularity,
timezone=timezone,
origin=extras.get('druid_time_origin'),
),
post_aggregations=post_aggs,
intervals=self.intervals_from_dttms(from_dttm, to_dttm),
)
filters = DruidDatasource.get_filters(filter, self.num_cols, columns_dict)
if filters:
qry['filter'] = filters
having_filters = self.get_having_filters(extras.get('having_druid'))
if having_filters:
qry['having'] = having_filters
order_direction = 'descending' if order_desc else 'ascending'
if columns:
columns.append('__time')
del qry['post_aggregations']
del qry['aggregations']
qry['dimensions'] = columns
qry['metrics'] = []
qry['granularity'] = 'all'
qry['limit'] = row_limit
client.scan(**qry)
elif len(groupby) == 0 and not having_filters:
logging.info('Running timeseries query for no groupby values')
del qry['dimensions']
client.timeseries(**qry)
elif (
not having_filters and
len(groupby) == 1 and
order_desc
):
dim = list(qry.get('dimensions'))[0]
logging.info('Running two-phase topn query for dimension [{}]'.format(dim))
pre_qry = deepcopy(qry)
if timeseries_limit_metric:
order_by = utils.get_metric_name(timeseries_limit_metric)
aggs_dict, post_aggs_dict = DruidDatasource.metrics_and_post_aggs(
[timeseries_limit_metric],
metrics_dict)
if phase == 1:
pre_qry['aggregations'].update(aggs_dict)
pre_qry['post_aggregations'].update(post_aggs_dict)
else:
pre_qry['aggregations'] = aggs_dict
pre_qry['post_aggregations'] = post_aggs_dict
else:
agg_keys = qry['aggregations'].keys()
order_by = list(agg_keys)[0] if agg_keys else None
# Limit on the number of timeseries, doing a two-phases query
pre_qry['granularity'] = 'all'
pre_qry['threshold'] = min(row_limit,
timeseries_limit or row_limit)
pre_qry['metric'] = order_by
pre_qry['dimension'] = self._dimensions_to_values(qry.get('dimensions'))[0]
del pre_qry['dimensions']
client.topn(**pre_qry)
logging.info('Phase 1 Complete')
if phase == 2:
query_str += '// Two phase query\n// Phase 1\n'
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
query_str += '\n'
if phase == 1:
return query_str
query_str += (
"// Phase 2 (built based on phase one's results)\n")
df = client.export_pandas()
qry['filter'] = self._add_filter_from_pre_query_data(
df,
[pre_qry['dimension']],
filters)
qry['threshold'] = timeseries_limit or 1000
if row_limit and granularity == 'all':
qry['threshold'] = row_limit
qry['dimension'] = dim
del qry['dimensions']
qry['metric'] = list(qry['aggregations'].keys())[0]
client.topn(**qry)
logging.info('Phase 2 Complete')
elif len(groupby) > 0 or having_filters:
# If grouping on multiple fields or using a having filter
# we have to force a groupby query
logging.info('Running groupby query for dimensions [{}]'.format(dimensions))
if timeseries_limit and is_timeseries:
logging.info('Running two-phase query for timeseries')
pre_qry = deepcopy(qry)
pre_qry_dims = self._dimensions_to_values(qry['dimensions'])
# Can't use set on an array with dicts
# Use set with non-dict items only
non_dict_dims = list(
set([x for x in pre_qry_dims if not isinstance(x, dict)]),
)
dict_dims = [x for x in pre_qry_dims if isinstance(x, dict)]
pre_qry['dimensions'] = non_dict_dims + dict_dims
order_by = None
if metrics:
order_by = utils.get_metric_name(metrics[0])
else:
order_by = pre_qry_dims[0]
if timeseries_limit_metric:
order_by = utils.get_metric_name(timeseries_limit_metric)
aggs_dict, post_aggs_dict = DruidDatasource.metrics_and_post_aggs(
[timeseries_limit_metric],
metrics_dict)
if phase == 1:
pre_qry['aggregations'].update(aggs_dict)
pre_qry['post_aggregations'].update(post_aggs_dict)
else:
pre_qry['aggregations'] = aggs_dict
pre_qry['post_aggregations'] = post_aggs_dict
# Limit on the number of timeseries, doing a two-phases query
pre_qry['granularity'] = 'all'
pre_qry['limit_spec'] = {
'type': 'default',
'limit': min(timeseries_limit, row_limit),
'intervals': self.intervals_from_dttms(
inner_from_dttm, inner_to_dttm),
'columns': [{
'dimension': order_by,
'direction': order_direction,
}],
}
client.groupby(**pre_qry)
logging.info('Phase 1 Complete')
query_str += '// Two phase query\n// Phase 1\n'
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
query_str += '\n'
if phase == 1:
return query_str
query_str += (
"// Phase 2 (built based on phase one's results)\n")
df = client.export_pandas()
qry['filter'] = self._add_filter_from_pre_query_data(
df,
pre_qry['dimensions'],
filters,
)
qry['limit_spec'] = None
if row_limit:
dimension_values = self._dimensions_to_values(dimensions)
qry['limit_spec'] = {
'type': 'default',
'limit': row_limit,
'columns': [{
'dimension': (
utils.get_metric_name(
metrics[0],
) if metrics else dimension_values[0]
),
'direction': order_direction,
}],
}
client.groupby(**qry)
logging.info('Query Complete')
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
return query_str | [
"Runs",
"a",
"query",
"against",
"Druid",
"and",
"returns",
"a",
"dataframe",
"."
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/druid/models.py#L1039-L1268 | [
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train | DruidDatasource.homogenize_types | Converting all GROUPBY columns to strings
When grouping by a numeric (say FLOAT) column, pydruid returns
strings in the dataframe. This creates issues downstream related
to having mixed types in the dataframe
Here we replace None with <NULL> and make the whole series a
str instead of an object. | superset/connectors/druid/models.py | def homogenize_types(df, groupby_cols):
"""Converting all GROUPBY columns to strings
When grouping by a numeric (say FLOAT) column, pydruid returns
strings in the dataframe. This creates issues downstream related
to having mixed types in the dataframe
Here we replace None with <NULL> and make the whole series a
str instead of an object.
"""
for col in groupby_cols:
df[col] = df[col].fillna('<NULL>').astype('unicode')
return df | def homogenize_types(df, groupby_cols):
"""Converting all GROUPBY columns to strings
When grouping by a numeric (say FLOAT) column, pydruid returns
strings in the dataframe. This creates issues downstream related
to having mixed types in the dataframe
Here we replace None with <NULL> and make the whole series a
str instead of an object.
"""
for col in groupby_cols:
df[col] = df[col].fillna('<NULL>').astype('unicode')
return df | [
"Converting",
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"columns",
"to",
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/druid/models.py#L1271-L1283 | [
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train | DruidDatasource.get_filters | Given Superset filter data structure, returns pydruid Filter(s) | superset/connectors/druid/models.py | def get_filters(cls, raw_filters, num_cols, columns_dict): # noqa
"""Given Superset filter data structure, returns pydruid Filter(s)"""
filters = None
for flt in raw_filters:
col = flt.get('col')
op = flt.get('op')
eq = flt.get('val')
if (
not col or
not op or
(eq is None and op not in ('IS NULL', 'IS NOT NULL'))):
continue
# Check if this dimension uses an extraction function
# If so, create the appropriate pydruid extraction object
column_def = columns_dict.get(col)
dim_spec = column_def.dimension_spec if column_def else None
extraction_fn = None
if dim_spec and 'extractionFn' in dim_spec:
(col, extraction_fn) = DruidDatasource._create_extraction_fn(dim_spec)
cond = None
is_numeric_col = col in num_cols
is_list_target = op in ('in', 'not in')
eq = cls.filter_values_handler(
eq, is_list_target=is_list_target,
target_column_is_numeric=is_numeric_col)
# For these two ops, could have used Dimension,
# but it doesn't support extraction functions
if op == '==':
cond = Filter(dimension=col, value=eq, extraction_function=extraction_fn)
elif op == '!=':
cond = ~Filter(dimension=col, value=eq, extraction_function=extraction_fn)
elif op in ('in', 'not in'):
fields = []
# ignore the filter if it has no value
if not len(eq):
continue
# if it uses an extraction fn, use the "in" operator
# as Dimension isn't supported
elif extraction_fn is not None:
cond = Filter(
dimension=col,
values=eq,
type='in',
extraction_function=extraction_fn,
)
elif len(eq) == 1:
cond = Dimension(col) == eq[0]
else:
for s in eq:
fields.append(Dimension(col) == s)
cond = Filter(type='or', fields=fields)
if op == 'not in':
cond = ~cond
elif op == 'regex':
cond = Filter(
extraction_function=extraction_fn,
type='regex',
pattern=eq,
dimension=col,
)
# For the ops below, could have used pydruid's Bound,
# but it doesn't support extraction functions
elif op == '>=':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
dimension=col,
lowerStrict=False,
upperStrict=False,
lower=eq,
upper=None,
alphaNumeric=is_numeric_col,
)
elif op == '<=':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
dimension=col,
lowerStrict=False,
upperStrict=False,
lower=None,
upper=eq,
alphaNumeric=is_numeric_col,
)
elif op == '>':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
lowerStrict=True,
upperStrict=False,
dimension=col,
lower=eq,
upper=None,
alphaNumeric=is_numeric_col,
)
elif op == '<':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
upperStrict=True,
lowerStrict=False,
dimension=col,
lower=None,
upper=eq,
alphaNumeric=is_numeric_col,
)
elif op == 'IS NULL':
cond = Dimension(col) == None # NOQA
elif op == 'IS NOT NULL':
cond = Dimension(col) != None # NOQA
if filters:
filters = Filter(type='and', fields=[
cond,
filters,
])
else:
filters = cond
return filters | def get_filters(cls, raw_filters, num_cols, columns_dict): # noqa
"""Given Superset filter data structure, returns pydruid Filter(s)"""
filters = None
for flt in raw_filters:
col = flt.get('col')
op = flt.get('op')
eq = flt.get('val')
if (
not col or
not op or
(eq is None and op not in ('IS NULL', 'IS NOT NULL'))):
continue
# Check if this dimension uses an extraction function
# If so, create the appropriate pydruid extraction object
column_def = columns_dict.get(col)
dim_spec = column_def.dimension_spec if column_def else None
extraction_fn = None
if dim_spec and 'extractionFn' in dim_spec:
(col, extraction_fn) = DruidDatasource._create_extraction_fn(dim_spec)
cond = None
is_numeric_col = col in num_cols
is_list_target = op in ('in', 'not in')
eq = cls.filter_values_handler(
eq, is_list_target=is_list_target,
target_column_is_numeric=is_numeric_col)
# For these two ops, could have used Dimension,
# but it doesn't support extraction functions
if op == '==':
cond = Filter(dimension=col, value=eq, extraction_function=extraction_fn)
elif op == '!=':
cond = ~Filter(dimension=col, value=eq, extraction_function=extraction_fn)
elif op in ('in', 'not in'):
fields = []
# ignore the filter if it has no value
if not len(eq):
continue
# if it uses an extraction fn, use the "in" operator
# as Dimension isn't supported
elif extraction_fn is not None:
cond = Filter(
dimension=col,
values=eq,
type='in',
extraction_function=extraction_fn,
)
elif len(eq) == 1:
cond = Dimension(col) == eq[0]
else:
for s in eq:
fields.append(Dimension(col) == s)
cond = Filter(type='or', fields=fields)
if op == 'not in':
cond = ~cond
elif op == 'regex':
cond = Filter(
extraction_function=extraction_fn,
type='regex',
pattern=eq,
dimension=col,
)
# For the ops below, could have used pydruid's Bound,
# but it doesn't support extraction functions
elif op == '>=':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
dimension=col,
lowerStrict=False,
upperStrict=False,
lower=eq,
upper=None,
alphaNumeric=is_numeric_col,
)
elif op == '<=':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
dimension=col,
lowerStrict=False,
upperStrict=False,
lower=None,
upper=eq,
alphaNumeric=is_numeric_col,
)
elif op == '>':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
lowerStrict=True,
upperStrict=False,
dimension=col,
lower=eq,
upper=None,
alphaNumeric=is_numeric_col,
)
elif op == '<':
cond = Filter(
type='bound',
extraction_function=extraction_fn,
upperStrict=True,
lowerStrict=False,
dimension=col,
lower=None,
upper=eq,
alphaNumeric=is_numeric_col,
)
elif op == 'IS NULL':
cond = Dimension(col) == None # NOQA
elif op == 'IS NOT NULL':
cond = Dimension(col) != None # NOQA
if filters:
filters = Filter(type='and', fields=[
cond,
filters,
])
else:
filters = cond
return filters | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/druid/models.py#L1361-L1484 | [
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train | get_env_variable | Get the environment variable or raise exception. | contrib/docker/superset_config.py | def get_env_variable(var_name, default=None):
"""Get the environment variable or raise exception."""
try:
return os.environ[var_name]
except KeyError:
if default is not None:
return default
else:
error_msg = 'The environment variable {} was missing, abort...'\
.format(var_name)
raise EnvironmentError(error_msg) | def get_env_variable(var_name, default=None):
"""Get the environment variable or raise exception."""
try:
return os.environ[var_name]
except KeyError:
if default is not None:
return default
else:
error_msg = 'The environment variable {} was missing, abort...'\
.format(var_name)
raise EnvironmentError(error_msg) | [
"Get",
"the",
"environment",
"variable",
"or",
"raise",
"exception",
"."
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/contrib/docker/superset_config.py#L20-L30 | [
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train | ConnectorRegistry.get_eager_datasource | Returns datasource with columns and metrics. | superset/connectors/connector_registry.py | def get_eager_datasource(cls, session, datasource_type, datasource_id):
"""Returns datasource with columns and metrics."""
datasource_class = ConnectorRegistry.sources[datasource_type]
return (
session.query(datasource_class)
.options(
subqueryload(datasource_class.columns),
subqueryload(datasource_class.metrics),
)
.filter_by(id=datasource_id)
.one()
) | def get_eager_datasource(cls, session, datasource_type, datasource_id):
"""Returns datasource with columns and metrics."""
datasource_class = ConnectorRegistry.sources[datasource_type]
return (
session.query(datasource_class)
.options(
subqueryload(datasource_class.columns),
subqueryload(datasource_class.metrics),
)
.filter_by(id=datasource_id)
.one()
) | [
"Returns",
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"and",
"metrics",
"."
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/connectors/connector_registry.py#L76-L87 | [
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train | load_misc_dashboard | Loading a dashboard featuring misc charts | superset/data/misc_dashboard.py | def load_misc_dashboard():
"""Loading a dashboard featuring misc charts"""
print('Creating the dashboard')
db.session.expunge_all()
dash = db.session.query(Dash).filter_by(slug=DASH_SLUG).first()
if not dash:
dash = Dash()
js = textwrap.dedent("""\
{
"CHART-BkeVbh8ANQ": {
"children": [],
"id": "CHART-BkeVbh8ANQ",
"meta": {
"chartId": 4004,
"height": 34,
"sliceName": "Multi Line",
"width": 8
},
"type": "CHART"
},
"CHART-H1HYNzEANX": {
"children": [],
"id": "CHART-H1HYNzEANX",
"meta": {
"chartId": 3940,
"height": 50,
"sliceName": "Energy Sankey",
"width": 6
},
"type": "CHART"
},
"CHART-HJOYVMV0E7": {
"children": [],
"id": "CHART-HJOYVMV0E7",
"meta": {
"chartId": 3969,
"height": 63,
"sliceName": "Mapbox Long/Lat",
"width": 6
},
"type": "CHART"
},
"CHART-S1WYNz4AVX": {
"children": [],
"id": "CHART-S1WYNz4AVX",
"meta": {
"chartId": 3989,
"height": 25,
"sliceName": "Parallel Coordinates",
"width": 4
},
"type": "CHART"
},
"CHART-r19KVMNCE7": {
"children": [],
"id": "CHART-r19KVMNCE7",
"meta": {
"chartId": 3971,
"height": 34,
"sliceName": "Calendar Heatmap multiformat 0",
"width": 4
},
"type": "CHART"
},
"CHART-rJ4K4GV04Q": {
"children": [],
"id": "CHART-rJ4K4GV04Q",
"meta": {
"chartId": 3941,
"height": 63,
"sliceName": "Energy Force Layout",
"width": 6
},
"type": "CHART"
},
"CHART-rkgF4G4A4X": {
"children": [],
"id": "CHART-rkgF4G4A4X",
"meta": {
"chartId": 3970,
"height": 25,
"sliceName": "Birth in France by department in 2016",
"width": 8
},
"type": "CHART"
},
"CHART-rywK4GVR4X": {
"children": [],
"id": "CHART-rywK4GVR4X",
"meta": {
"chartId": 3942,
"height": 50,
"sliceName": "Heatmap",
"width": 6
},
"type": "CHART"
},
"COLUMN-ByUFVf40EQ": {
"children": [
"CHART-rywK4GVR4X",
"CHART-HJOYVMV0E7"
],
"id": "COLUMN-ByUFVf40EQ",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 6
},
"type": "COLUMN"
},
"COLUMN-rkmYVGN04Q": {
"children": [
"CHART-rJ4K4GV04Q",
"CHART-H1HYNzEANX"
],
"id": "COLUMN-rkmYVGN04Q",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 6
},
"type": "COLUMN"
},
"GRID_ID": {
"children": [
"ROW-SytNzNA4X",
"ROW-S1MK4M4A4X",
"ROW-HkFFEzVRVm"
],
"id": "GRID_ID",
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "Misc Charts"
},
"type": "HEADER"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW-HkFFEzVRVm": {
"children": [
"CHART-r19KVMNCE7",
"CHART-BkeVbh8ANQ"
],
"id": "ROW-HkFFEzVRVm",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-S1MK4M4A4X": {
"children": [
"COLUMN-rkmYVGN04Q",
"COLUMN-ByUFVf40EQ"
],
"id": "ROW-S1MK4M4A4X",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-SytNzNA4X": {
"children": [
"CHART-rkgF4G4A4X",
"CHART-S1WYNz4AVX"
],
"id": "ROW-SytNzNA4X",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"DASHBOARD_VERSION_KEY": "v2"
}
""")
pos = json.loads(js)
slices = (
db.session
.query(Slice)
.filter(Slice.slice_name.in_(misc_dash_slices))
.all()
)
slices = sorted(slices, key=lambda x: x.id)
update_slice_ids(pos, slices)
dash.dashboard_title = 'Misc Charts'
dash.position_json = json.dumps(pos, indent=4)
dash.slug = DASH_SLUG
dash.slices = slices
db.session.merge(dash)
db.session.commit() | def load_misc_dashboard():
"""Loading a dashboard featuring misc charts"""
print('Creating the dashboard')
db.session.expunge_all()
dash = db.session.query(Dash).filter_by(slug=DASH_SLUG).first()
if not dash:
dash = Dash()
js = textwrap.dedent("""\
{
"CHART-BkeVbh8ANQ": {
"children": [],
"id": "CHART-BkeVbh8ANQ",
"meta": {
"chartId": 4004,
"height": 34,
"sliceName": "Multi Line",
"width": 8
},
"type": "CHART"
},
"CHART-H1HYNzEANX": {
"children": [],
"id": "CHART-H1HYNzEANX",
"meta": {
"chartId": 3940,
"height": 50,
"sliceName": "Energy Sankey",
"width": 6
},
"type": "CHART"
},
"CHART-HJOYVMV0E7": {
"children": [],
"id": "CHART-HJOYVMV0E7",
"meta": {
"chartId": 3969,
"height": 63,
"sliceName": "Mapbox Long/Lat",
"width": 6
},
"type": "CHART"
},
"CHART-S1WYNz4AVX": {
"children": [],
"id": "CHART-S1WYNz4AVX",
"meta": {
"chartId": 3989,
"height": 25,
"sliceName": "Parallel Coordinates",
"width": 4
},
"type": "CHART"
},
"CHART-r19KVMNCE7": {
"children": [],
"id": "CHART-r19KVMNCE7",
"meta": {
"chartId": 3971,
"height": 34,
"sliceName": "Calendar Heatmap multiformat 0",
"width": 4
},
"type": "CHART"
},
"CHART-rJ4K4GV04Q": {
"children": [],
"id": "CHART-rJ4K4GV04Q",
"meta": {
"chartId": 3941,
"height": 63,
"sliceName": "Energy Force Layout",
"width": 6
},
"type": "CHART"
},
"CHART-rkgF4G4A4X": {
"children": [],
"id": "CHART-rkgF4G4A4X",
"meta": {
"chartId": 3970,
"height": 25,
"sliceName": "Birth in France by department in 2016",
"width": 8
},
"type": "CHART"
},
"CHART-rywK4GVR4X": {
"children": [],
"id": "CHART-rywK4GVR4X",
"meta": {
"chartId": 3942,
"height": 50,
"sliceName": "Heatmap",
"width": 6
},
"type": "CHART"
},
"COLUMN-ByUFVf40EQ": {
"children": [
"CHART-rywK4GVR4X",
"CHART-HJOYVMV0E7"
],
"id": "COLUMN-ByUFVf40EQ",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 6
},
"type": "COLUMN"
},
"COLUMN-rkmYVGN04Q": {
"children": [
"CHART-rJ4K4GV04Q",
"CHART-H1HYNzEANX"
],
"id": "COLUMN-rkmYVGN04Q",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 6
},
"type": "COLUMN"
},
"GRID_ID": {
"children": [
"ROW-SytNzNA4X",
"ROW-S1MK4M4A4X",
"ROW-HkFFEzVRVm"
],
"id": "GRID_ID",
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "Misc Charts"
},
"type": "HEADER"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW-HkFFEzVRVm": {
"children": [
"CHART-r19KVMNCE7",
"CHART-BkeVbh8ANQ"
],
"id": "ROW-HkFFEzVRVm",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-S1MK4M4A4X": {
"children": [
"COLUMN-rkmYVGN04Q",
"COLUMN-ByUFVf40EQ"
],
"id": "ROW-S1MK4M4A4X",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-SytNzNA4X": {
"children": [
"CHART-rkgF4G4A4X",
"CHART-S1WYNz4AVX"
],
"id": "ROW-SytNzNA4X",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"DASHBOARD_VERSION_KEY": "v2"
}
""")
pos = json.loads(js)
slices = (
db.session
.query(Slice)
.filter(Slice.slice_name.in_(misc_dash_slices))
.all()
)
slices = sorted(slices, key=lambda x: x.id)
update_slice_ids(pos, slices)
dash.dashboard_title = 'Misc Charts'
dash.position_json = json.dumps(pos, indent=4)
dash.slug = DASH_SLUG
dash.slices = slices
db.session.merge(dash)
db.session.commit() | [
"Loading",
"a",
"dashboard",
"featuring",
"misc",
"charts"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/misc_dashboard.py#L32-L228 | [
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train | load_world_bank_health_n_pop | Loads the world bank health dataset, slices and a dashboard | superset/data/world_bank.py | def load_world_bank_health_n_pop():
"""Loads the world bank health dataset, slices and a dashboard"""
tbl_name = 'wb_health_population'
data = get_example_data('countries.json.gz')
pdf = pd.read_json(data)
pdf.columns = [col.replace('.', '_') for col in pdf.columns]
pdf.year = pd.to_datetime(pdf.year)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=50,
dtype={
'year': DateTime(),
'country_code': String(3),
'country_name': String(255),
'region': String(255),
},
index=False)
print('Creating table [wb_health_population] reference')
tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
if not tbl:
tbl = TBL(table_name=tbl_name)
tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
tbl.main_dttm_col = 'year'
tbl.database = utils.get_or_create_main_db()
tbl.filter_select_enabled = True
metrics = [
'sum__SP_POP_TOTL', 'sum__SH_DYN_AIDS', 'sum__SH_DYN_AIDS',
'sum__SP_RUR_TOTL_ZS', 'sum__SP_DYN_LE00_IN',
]
for m in metrics:
if not any(col.metric_name == m for col in tbl.metrics):
tbl.metrics.append(SqlMetric(
metric_name=m,
expression=f'{m[:3]}({m[5:]})',
))
db.session.merge(tbl)
db.session.commit()
tbl.fetch_metadata()
defaults = {
'compare_lag': '10',
'compare_suffix': 'o10Y',
'limit': '25',
'granularity_sqla': 'year',
'groupby': [],
'metric': 'sum__SP_POP_TOTL',
'metrics': ['sum__SP_POP_TOTL'],
'row_limit': config.get('ROW_LIMIT'),
'since': '2014-01-01',
'until': '2014-01-02',
'time_range': '2014-01-01 : 2014-01-02',
'where': '',
'markup_type': 'markdown',
'country_fieldtype': 'cca3',
'secondary_metric': 'sum__SP_POP_TOTL',
'entity': 'country_code',
'show_bubbles': True,
}
print('Creating slices')
slices = [
Slice(
slice_name='Region Filter',
viz_type='filter_box',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='filter_box',
date_filter=False,
filter_configs=[
{
'asc': False,
'clearable': True,
'column': 'region',
'key': '2s98dfu',
'metric': 'sum__SP_POP_TOTL',
'multiple': True,
}, {
'asc': False,
'clearable': True,
'key': 'li3j2lk',
'column': 'country_name',
'metric': 'sum__SP_POP_TOTL',
'multiple': True,
},
])),
Slice(
slice_name="World's Population",
viz_type='big_number',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='2000',
viz_type='big_number',
compare_lag='10',
metric='sum__SP_POP_TOTL',
compare_suffix='over 10Y')),
Slice(
slice_name='Most Populated Countries',
viz_type='table',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='table',
metrics=['sum__SP_POP_TOTL'],
groupby=['country_name'])),
Slice(
slice_name='Growth Rate',
viz_type='line',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='line',
since='1960-01-01',
metrics=['sum__SP_POP_TOTL'],
num_period_compare='10',
groupby=['country_name'])),
Slice(
slice_name='% Rural',
viz_type='world_map',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='world_map',
metric='sum__SP_RUR_TOTL_ZS',
num_period_compare='10')),
Slice(
slice_name='Life Expectancy VS Rural %',
viz_type='bubble',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='bubble',
since='2011-01-01',
until='2011-01-02',
series='region',
limit=0,
entity='country_name',
x='sum__SP_RUR_TOTL_ZS',
y='sum__SP_DYN_LE00_IN',
size='sum__SP_POP_TOTL',
max_bubble_size='50',
filters=[{
'col': 'country_code',
'val': [
'TCA', 'MNP', 'DMA', 'MHL', 'MCO', 'SXM', 'CYM',
'TUV', 'IMY', 'KNA', 'ASM', 'ADO', 'AMA', 'PLW',
],
'op': 'not in'}],
)),
Slice(
slice_name='Rural Breakdown',
viz_type='sunburst',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='sunburst',
groupby=['region', 'country_name'],
secondary_metric='sum__SP_RUR_TOTL',
since='2011-01-01',
until='2011-01-01')),
Slice(
slice_name="World's Pop Growth",
viz_type='area',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
viz_type='area',
groupby=['region'])),
Slice(
slice_name='Box plot',
viz_type='box_plot',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
whisker_options='Min/max (no outliers)',
x_ticks_layout='staggered',
viz_type='box_plot',
groupby=['region'])),
Slice(
slice_name='Treemap',
viz_type='treemap',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
viz_type='treemap',
metrics=['sum__SP_POP_TOTL'],
groupby=['region', 'country_code'])),
Slice(
slice_name='Parallel Coordinates',
viz_type='para',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='2011-01-01',
until='2011-01-01',
viz_type='para',
limit=100,
metrics=[
'sum__SP_POP_TOTL',
'sum__SP_RUR_TOTL_ZS',
'sum__SH_DYN_AIDS'],
secondary_metric='sum__SP_POP_TOTL',
series='country_name')),
]
misc_dash_slices.add(slices[-1].slice_name)
for slc in slices:
merge_slice(slc)
print("Creating a World's Health Bank dashboard")
dash_name = "World's Bank Data"
slug = 'world_health'
dash = db.session.query(Dash).filter_by(slug=slug).first()
if not dash:
dash = Dash()
js = textwrap.dedent("""\
{
"CHART-36bfc934": {
"children": [],
"id": "CHART-36bfc934",
"meta": {
"chartId": 40,
"height": 25,
"sliceName": "Region Filter",
"width": 2
},
"type": "CHART"
},
"CHART-37982887": {
"children": [],
"id": "CHART-37982887",
"meta": {
"chartId": 41,
"height": 25,
"sliceName": "World's Population",
"width": 2
},
"type": "CHART"
},
"CHART-17e0f8d8": {
"children": [],
"id": "CHART-17e0f8d8",
"meta": {
"chartId": 42,
"height": 92,
"sliceName": "Most Populated Countries",
"width": 3
},
"type": "CHART"
},
"CHART-2ee52f30": {
"children": [],
"id": "CHART-2ee52f30",
"meta": {
"chartId": 43,
"height": 38,
"sliceName": "Growth Rate",
"width": 6
},
"type": "CHART"
},
"CHART-2d5b6871": {
"children": [],
"id": "CHART-2d5b6871",
"meta": {
"chartId": 44,
"height": 52,
"sliceName": "% Rural",
"width": 7
},
"type": "CHART"
},
"CHART-0fd0d252": {
"children": [],
"id": "CHART-0fd0d252",
"meta": {
"chartId": 45,
"height": 50,
"sliceName": "Life Expectancy VS Rural %",
"width": 8
},
"type": "CHART"
},
"CHART-97f4cb48": {
"children": [],
"id": "CHART-97f4cb48",
"meta": {
"chartId": 46,
"height": 38,
"sliceName": "Rural Breakdown",
"width": 3
},
"type": "CHART"
},
"CHART-b5e05d6f": {
"children": [],
"id": "CHART-b5e05d6f",
"meta": {
"chartId": 47,
"height": 50,
"sliceName": "World's Pop Growth",
"width": 4
},
"type": "CHART"
},
"CHART-e76e9f5f": {
"children": [],
"id": "CHART-e76e9f5f",
"meta": {
"chartId": 48,
"height": 50,
"sliceName": "Box plot",
"width": 4
},
"type": "CHART"
},
"CHART-a4808bba": {
"children": [],
"id": "CHART-a4808bba",
"meta": {
"chartId": 49,
"height": 50,
"sliceName": "Treemap",
"width": 8
},
"type": "CHART"
},
"COLUMN-071bbbad": {
"children": [
"ROW-1e064e3c",
"ROW-afdefba9"
],
"id": "COLUMN-071bbbad",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 9
},
"type": "COLUMN"
},
"COLUMN-fe3914b8": {
"children": [
"CHART-36bfc934",
"CHART-37982887"
],
"id": "COLUMN-fe3914b8",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 2
},
"type": "COLUMN"
},
"GRID_ID": {
"children": [
"ROW-46632bc2",
"ROW-3fa26c5d",
"ROW-812b3f13"
],
"id": "GRID_ID",
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "World's Bank Data"
},
"type": "HEADER"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW-1e064e3c": {
"children": [
"COLUMN-fe3914b8",
"CHART-2d5b6871"
],
"id": "ROW-1e064e3c",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-3fa26c5d": {
"children": [
"CHART-b5e05d6f",
"CHART-0fd0d252"
],
"id": "ROW-3fa26c5d",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-46632bc2": {
"children": [
"COLUMN-071bbbad",
"CHART-17e0f8d8"
],
"id": "ROW-46632bc2",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-812b3f13": {
"children": [
"CHART-a4808bba",
"CHART-e76e9f5f"
],
"id": "ROW-812b3f13",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-afdefba9": {
"children": [
"CHART-2ee52f30",
"CHART-97f4cb48"
],
"id": "ROW-afdefba9",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"DASHBOARD_VERSION_KEY": "v2"
}
""")
pos = json.loads(js)
update_slice_ids(pos, slices)
dash.dashboard_title = dash_name
dash.position_json = json.dumps(pos, indent=4)
dash.slug = slug
dash.slices = slices[:-1]
db.session.merge(dash)
db.session.commit() | def load_world_bank_health_n_pop():
"""Loads the world bank health dataset, slices and a dashboard"""
tbl_name = 'wb_health_population'
data = get_example_data('countries.json.gz')
pdf = pd.read_json(data)
pdf.columns = [col.replace('.', '_') for col in pdf.columns]
pdf.year = pd.to_datetime(pdf.year)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=50,
dtype={
'year': DateTime(),
'country_code': String(3),
'country_name': String(255),
'region': String(255),
},
index=False)
print('Creating table [wb_health_population] reference')
tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
if not tbl:
tbl = TBL(table_name=tbl_name)
tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
tbl.main_dttm_col = 'year'
tbl.database = utils.get_or_create_main_db()
tbl.filter_select_enabled = True
metrics = [
'sum__SP_POP_TOTL', 'sum__SH_DYN_AIDS', 'sum__SH_DYN_AIDS',
'sum__SP_RUR_TOTL_ZS', 'sum__SP_DYN_LE00_IN',
]
for m in metrics:
if not any(col.metric_name == m for col in tbl.metrics):
tbl.metrics.append(SqlMetric(
metric_name=m,
expression=f'{m[:3]}({m[5:]})',
))
db.session.merge(tbl)
db.session.commit()
tbl.fetch_metadata()
defaults = {
'compare_lag': '10',
'compare_suffix': 'o10Y',
'limit': '25',
'granularity_sqla': 'year',
'groupby': [],
'metric': 'sum__SP_POP_TOTL',
'metrics': ['sum__SP_POP_TOTL'],
'row_limit': config.get('ROW_LIMIT'),
'since': '2014-01-01',
'until': '2014-01-02',
'time_range': '2014-01-01 : 2014-01-02',
'where': '',
'markup_type': 'markdown',
'country_fieldtype': 'cca3',
'secondary_metric': 'sum__SP_POP_TOTL',
'entity': 'country_code',
'show_bubbles': True,
}
print('Creating slices')
slices = [
Slice(
slice_name='Region Filter',
viz_type='filter_box',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='filter_box',
date_filter=False,
filter_configs=[
{
'asc': False,
'clearable': True,
'column': 'region',
'key': '2s98dfu',
'metric': 'sum__SP_POP_TOTL',
'multiple': True,
}, {
'asc': False,
'clearable': True,
'key': 'li3j2lk',
'column': 'country_name',
'metric': 'sum__SP_POP_TOTL',
'multiple': True,
},
])),
Slice(
slice_name="World's Population",
viz_type='big_number',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='2000',
viz_type='big_number',
compare_lag='10',
metric='sum__SP_POP_TOTL',
compare_suffix='over 10Y')),
Slice(
slice_name='Most Populated Countries',
viz_type='table',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='table',
metrics=['sum__SP_POP_TOTL'],
groupby=['country_name'])),
Slice(
slice_name='Growth Rate',
viz_type='line',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='line',
since='1960-01-01',
metrics=['sum__SP_POP_TOTL'],
num_period_compare='10',
groupby=['country_name'])),
Slice(
slice_name='% Rural',
viz_type='world_map',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='world_map',
metric='sum__SP_RUR_TOTL_ZS',
num_period_compare='10')),
Slice(
slice_name='Life Expectancy VS Rural %',
viz_type='bubble',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='bubble',
since='2011-01-01',
until='2011-01-02',
series='region',
limit=0,
entity='country_name',
x='sum__SP_RUR_TOTL_ZS',
y='sum__SP_DYN_LE00_IN',
size='sum__SP_POP_TOTL',
max_bubble_size='50',
filters=[{
'col': 'country_code',
'val': [
'TCA', 'MNP', 'DMA', 'MHL', 'MCO', 'SXM', 'CYM',
'TUV', 'IMY', 'KNA', 'ASM', 'ADO', 'AMA', 'PLW',
],
'op': 'not in'}],
)),
Slice(
slice_name='Rural Breakdown',
viz_type='sunburst',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type='sunburst',
groupby=['region', 'country_name'],
secondary_metric='sum__SP_RUR_TOTL',
since='2011-01-01',
until='2011-01-01')),
Slice(
slice_name="World's Pop Growth",
viz_type='area',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
viz_type='area',
groupby=['region'])),
Slice(
slice_name='Box plot',
viz_type='box_plot',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
whisker_options='Min/max (no outliers)',
x_ticks_layout='staggered',
viz_type='box_plot',
groupby=['region'])),
Slice(
slice_name='Treemap',
viz_type='treemap',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='1960-01-01',
until='now',
viz_type='treemap',
metrics=['sum__SP_POP_TOTL'],
groupby=['region', 'country_code'])),
Slice(
slice_name='Parallel Coordinates',
viz_type='para',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since='2011-01-01',
until='2011-01-01',
viz_type='para',
limit=100,
metrics=[
'sum__SP_POP_TOTL',
'sum__SP_RUR_TOTL_ZS',
'sum__SH_DYN_AIDS'],
secondary_metric='sum__SP_POP_TOTL',
series='country_name')),
]
misc_dash_slices.add(slices[-1].slice_name)
for slc in slices:
merge_slice(slc)
print("Creating a World's Health Bank dashboard")
dash_name = "World's Bank Data"
slug = 'world_health'
dash = db.session.query(Dash).filter_by(slug=slug).first()
if not dash:
dash = Dash()
js = textwrap.dedent("""\
{
"CHART-36bfc934": {
"children": [],
"id": "CHART-36bfc934",
"meta": {
"chartId": 40,
"height": 25,
"sliceName": "Region Filter",
"width": 2
},
"type": "CHART"
},
"CHART-37982887": {
"children": [],
"id": "CHART-37982887",
"meta": {
"chartId": 41,
"height": 25,
"sliceName": "World's Population",
"width": 2
},
"type": "CHART"
},
"CHART-17e0f8d8": {
"children": [],
"id": "CHART-17e0f8d8",
"meta": {
"chartId": 42,
"height": 92,
"sliceName": "Most Populated Countries",
"width": 3
},
"type": "CHART"
},
"CHART-2ee52f30": {
"children": [],
"id": "CHART-2ee52f30",
"meta": {
"chartId": 43,
"height": 38,
"sliceName": "Growth Rate",
"width": 6
},
"type": "CHART"
},
"CHART-2d5b6871": {
"children": [],
"id": "CHART-2d5b6871",
"meta": {
"chartId": 44,
"height": 52,
"sliceName": "% Rural",
"width": 7
},
"type": "CHART"
},
"CHART-0fd0d252": {
"children": [],
"id": "CHART-0fd0d252",
"meta": {
"chartId": 45,
"height": 50,
"sliceName": "Life Expectancy VS Rural %",
"width": 8
},
"type": "CHART"
},
"CHART-97f4cb48": {
"children": [],
"id": "CHART-97f4cb48",
"meta": {
"chartId": 46,
"height": 38,
"sliceName": "Rural Breakdown",
"width": 3
},
"type": "CHART"
},
"CHART-b5e05d6f": {
"children": [],
"id": "CHART-b5e05d6f",
"meta": {
"chartId": 47,
"height": 50,
"sliceName": "World's Pop Growth",
"width": 4
},
"type": "CHART"
},
"CHART-e76e9f5f": {
"children": [],
"id": "CHART-e76e9f5f",
"meta": {
"chartId": 48,
"height": 50,
"sliceName": "Box plot",
"width": 4
},
"type": "CHART"
},
"CHART-a4808bba": {
"children": [],
"id": "CHART-a4808bba",
"meta": {
"chartId": 49,
"height": 50,
"sliceName": "Treemap",
"width": 8
},
"type": "CHART"
},
"COLUMN-071bbbad": {
"children": [
"ROW-1e064e3c",
"ROW-afdefba9"
],
"id": "COLUMN-071bbbad",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 9
},
"type": "COLUMN"
},
"COLUMN-fe3914b8": {
"children": [
"CHART-36bfc934",
"CHART-37982887"
],
"id": "COLUMN-fe3914b8",
"meta": {
"background": "BACKGROUND_TRANSPARENT",
"width": 2
},
"type": "COLUMN"
},
"GRID_ID": {
"children": [
"ROW-46632bc2",
"ROW-3fa26c5d",
"ROW-812b3f13"
],
"id": "GRID_ID",
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "World's Bank Data"
},
"type": "HEADER"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW-1e064e3c": {
"children": [
"COLUMN-fe3914b8",
"CHART-2d5b6871"
],
"id": "ROW-1e064e3c",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-3fa26c5d": {
"children": [
"CHART-b5e05d6f",
"CHART-0fd0d252"
],
"id": "ROW-3fa26c5d",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-46632bc2": {
"children": [
"COLUMN-071bbbad",
"CHART-17e0f8d8"
],
"id": "ROW-46632bc2",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-812b3f13": {
"children": [
"CHART-a4808bba",
"CHART-e76e9f5f"
],
"id": "ROW-812b3f13",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"ROW-afdefba9": {
"children": [
"CHART-2ee52f30",
"CHART-97f4cb48"
],
"id": "ROW-afdefba9",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"type": "ROW"
},
"DASHBOARD_VERSION_KEY": "v2"
}
""")
pos = json.loads(js)
update_slice_ids(pos, slices)
dash.dashboard_title = dash_name
dash.position_json = json.dumps(pos, indent=4)
dash.slug = slug
dash.slices = slices[:-1]
db.session.merge(dash)
db.session.commit() | [
"Loads",
"the",
"world",
"bank",
"health",
"dataset",
"slices",
"and",
"a",
"dashboard"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/world_bank.py#L43-L507 | [
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] | ca2996c78f679260eb79c6008e276733df5fb653 |
train | load_country_map_data | Loading data for map with country map | superset/data/country_map.py | def load_country_map_data():
"""Loading data for map with country map"""
csv_bytes = get_example_data(
'birth_france_data_for_country_map.csv', is_gzip=False, make_bytes=True)
data = pd.read_csv(csv_bytes, encoding='utf-8')
data['dttm'] = datetime.datetime.now().date()
data.to_sql( # pylint: disable=no-member
'birth_france_by_region',
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'DEPT_ID': String(10),
'2003': BigInteger,
'2004': BigInteger,
'2005': BigInteger,
'2006': BigInteger,
'2007': BigInteger,
'2008': BigInteger,
'2009': BigInteger,
'2010': BigInteger,
'2011': BigInteger,
'2012': BigInteger,
'2013': BigInteger,
'2014': BigInteger,
'dttm': Date(),
},
index=False)
print('Done loading table!')
print('-' * 80)
print('Creating table reference')
obj = db.session.query(TBL).filter_by(table_name='birth_france_by_region').first()
if not obj:
obj = TBL(table_name='birth_france_by_region')
obj.main_dttm_col = 'dttm'
obj.database = utils.get_or_create_main_db()
if not any(col.metric_name == 'avg__2004' for col in obj.metrics):
obj.metrics.append(SqlMetric(
metric_name='avg__2004',
expression='AVG(2004)',
))
db.session.merge(obj)
db.session.commit()
obj.fetch_metadata()
tbl = obj
slice_data = {
'granularity_sqla': '',
'since': '',
'until': '',
'where': '',
'viz_type': 'country_map',
'entity': 'DEPT_ID',
'metric': {
'expressionType': 'SIMPLE',
'column': {
'type': 'INT',
'column_name': '2004',
},
'aggregate': 'AVG',
'label': 'Boys',
'optionName': 'metric_112342',
},
'row_limit': 500000,
}
print('Creating a slice')
slc = Slice(
slice_name='Birth in France by department in 2016',
viz_type='country_map',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(slice_data),
)
misc_dash_slices.add(slc.slice_name)
merge_slice(slc) | def load_country_map_data():
"""Loading data for map with country map"""
csv_bytes = get_example_data(
'birth_france_data_for_country_map.csv', is_gzip=False, make_bytes=True)
data = pd.read_csv(csv_bytes, encoding='utf-8')
data['dttm'] = datetime.datetime.now().date()
data.to_sql( # pylint: disable=no-member
'birth_france_by_region',
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'DEPT_ID': String(10),
'2003': BigInteger,
'2004': BigInteger,
'2005': BigInteger,
'2006': BigInteger,
'2007': BigInteger,
'2008': BigInteger,
'2009': BigInteger,
'2010': BigInteger,
'2011': BigInteger,
'2012': BigInteger,
'2013': BigInteger,
'2014': BigInteger,
'dttm': Date(),
},
index=False)
print('Done loading table!')
print('-' * 80)
print('Creating table reference')
obj = db.session.query(TBL).filter_by(table_name='birth_france_by_region').first()
if not obj:
obj = TBL(table_name='birth_france_by_region')
obj.main_dttm_col = 'dttm'
obj.database = utils.get_or_create_main_db()
if not any(col.metric_name == 'avg__2004' for col in obj.metrics):
obj.metrics.append(SqlMetric(
metric_name='avg__2004',
expression='AVG(2004)',
))
db.session.merge(obj)
db.session.commit()
obj.fetch_metadata()
tbl = obj
slice_data = {
'granularity_sqla': '',
'since': '',
'until': '',
'where': '',
'viz_type': 'country_map',
'entity': 'DEPT_ID',
'metric': {
'expressionType': 'SIMPLE',
'column': {
'type': 'INT',
'column_name': '2004',
},
'aggregate': 'AVG',
'label': 'Boys',
'optionName': 'metric_112342',
},
'row_limit': 500000,
}
print('Creating a slice')
slc = Slice(
slice_name='Birth in France by department in 2016',
viz_type='country_map',
datasource_type='table',
datasource_id=tbl.id,
params=get_slice_json(slice_data),
)
misc_dash_slices.add(slc.slice_name)
merge_slice(slc) | [
"Loading",
"data",
"for",
"map",
"with",
"country",
"map"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/country_map.py#L35-L110 | [
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train | ParsedQuery.get_statements | Returns a list of SQL statements as strings, stripped | superset/sql_parse.py | def get_statements(self):
"""Returns a list of SQL statements as strings, stripped"""
statements = []
for statement in self._parsed:
if statement:
sql = str(statement).strip(' \n;\t')
if sql:
statements.append(sql)
return statements | def get_statements(self):
"""Returns a list of SQL statements as strings, stripped"""
statements = []
for statement in self._parsed:
if statement:
sql = str(statement).strip(' \n;\t')
if sql:
statements.append(sql)
return statements | [
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train | ParsedQuery.as_create_table | Reformats the query into the create table as query.
Works only for the single select SQL statements, in all other cases
the sql query is not modified.
:param superset_query: string, sql query that will be executed
:param table_name: string, will contain the results of the
query execution
:param overwrite, boolean, table table_name will be dropped if true
:return: string, create table as query | superset/sql_parse.py | def as_create_table(self, table_name, overwrite=False):
"""Reformats the query into the create table as query.
Works only for the single select SQL statements, in all other cases
the sql query is not modified.
:param superset_query: string, sql query that will be executed
:param table_name: string, will contain the results of the
query execution
:param overwrite, boolean, table table_name will be dropped if true
:return: string, create table as query
"""
exec_sql = ''
sql = self.stripped()
if overwrite:
exec_sql = f'DROP TABLE IF EXISTS {table_name};\n'
exec_sql += f'CREATE TABLE {table_name} AS \n{sql}'
return exec_sql | def as_create_table(self, table_name, overwrite=False):
"""Reformats the query into the create table as query.
Works only for the single select SQL statements, in all other cases
the sql query is not modified.
:param superset_query: string, sql query that will be executed
:param table_name: string, will contain the results of the
query execution
:param overwrite, boolean, table table_name will be dropped if true
:return: string, create table as query
"""
exec_sql = ''
sql = self.stripped()
if overwrite:
exec_sql = f'DROP TABLE IF EXISTS {table_name};\n'
exec_sql += f'CREATE TABLE {table_name} AS \n{sql}'
return exec_sql | [
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train | ParsedQuery.get_query_with_new_limit | returns the query with the specified limit | superset/sql_parse.py | def get_query_with_new_limit(self, new_limit):
"""returns the query with the specified limit"""
"""does not change the underlying query"""
if not self._limit:
return self.sql + ' LIMIT ' + str(new_limit)
limit_pos = None
tokens = self._parsed[0].tokens
# Add all items to before_str until there is a limit
for pos, item in enumerate(tokens):
if item.ttype in Keyword and item.value.lower() == 'limit':
limit_pos = pos
break
limit = tokens[limit_pos + 2]
if limit.ttype == sqlparse.tokens.Literal.Number.Integer:
tokens[limit_pos + 2].value = new_limit
elif limit.is_group:
tokens[limit_pos + 2].value = (
'{}, {}'.format(next(limit.get_identifiers()), new_limit)
)
str_res = ''
for i in tokens:
str_res += str(i.value)
return str_res | def get_query_with_new_limit(self, new_limit):
"""returns the query with the specified limit"""
"""does not change the underlying query"""
if not self._limit:
return self.sql + ' LIMIT ' + str(new_limit)
limit_pos = None
tokens = self._parsed[0].tokens
# Add all items to before_str until there is a limit
for pos, item in enumerate(tokens):
if item.ttype in Keyword and item.value.lower() == 'limit':
limit_pos = pos
break
limit = tokens[limit_pos + 2]
if limit.ttype == sqlparse.tokens.Literal.Number.Integer:
tokens[limit_pos + 2].value = new_limit
elif limit.is_group:
tokens[limit_pos + 2].value = (
'{}, {}'.format(next(limit.get_identifiers()), new_limit)
)
str_res = ''
for i in tokens:
str_res += str(i.value)
return str_res | [
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train | url_param | Read a url or post parameter and use it in your SQL Lab query
When in SQL Lab, it's possible to add arbitrary URL "query string"
parameters, and use those in your SQL code. For instance you can
alter your url and add `?foo=bar`, as in
`{domain}/superset/sqllab?foo=bar`. Then if your query is something like
SELECT * FROM foo = '{{ url_param('foo') }}', it will be parsed at
runtime and replaced by the value in the URL.
As you create a visualization form this SQL Lab query, you can pass
parameters in the explore view as well as from the dashboard, and
it should carry through to your queries.
:param param: the parameter to lookup
:type param: str
:param default: the value to return in the absence of the parameter
:type default: str | superset/jinja_context.py | def url_param(param, default=None):
"""Read a url or post parameter and use it in your SQL Lab query
When in SQL Lab, it's possible to add arbitrary URL "query string"
parameters, and use those in your SQL code. For instance you can
alter your url and add `?foo=bar`, as in
`{domain}/superset/sqllab?foo=bar`. Then if your query is something like
SELECT * FROM foo = '{{ url_param('foo') }}', it will be parsed at
runtime and replaced by the value in the URL.
As you create a visualization form this SQL Lab query, you can pass
parameters in the explore view as well as from the dashboard, and
it should carry through to your queries.
:param param: the parameter to lookup
:type param: str
:param default: the value to return in the absence of the parameter
:type default: str
"""
if request.args.get(param):
return request.args.get(param, default)
# Supporting POST as well as get
if request.form.get('form_data'):
form_data = json.loads(request.form.get('form_data'))
url_params = form_data.get('url_params') or {}
return url_params.get(param, default)
return default | def url_param(param, default=None):
"""Read a url or post parameter and use it in your SQL Lab query
When in SQL Lab, it's possible to add arbitrary URL "query string"
parameters, and use those in your SQL code. For instance you can
alter your url and add `?foo=bar`, as in
`{domain}/superset/sqllab?foo=bar`. Then if your query is something like
SELECT * FROM foo = '{{ url_param('foo') }}', it will be parsed at
runtime and replaced by the value in the URL.
As you create a visualization form this SQL Lab query, you can pass
parameters in the explore view as well as from the dashboard, and
it should carry through to your queries.
:param param: the parameter to lookup
:type param: str
:param default: the value to return in the absence of the parameter
:type default: str
"""
if request.args.get(param):
return request.args.get(param, default)
# Supporting POST as well as get
if request.form.get('form_data'):
form_data = json.loads(request.form.get('form_data'))
url_params = form_data.get('url_params') or {}
return url_params.get(param, default)
return default | [
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train | filter_values | Gets a values for a particular filter as a list
This is useful if:
- you want to use a filter box to filter a query where the name of filter box
column doesn't match the one in the select statement
- you want to have the ability for filter inside the main query for speed purposes
This searches for "filters" and "extra_filters" in form_data for a match
Usage example:
SELECT action, count(*) as times
FROM logs
WHERE action in ( {{ "'" + "','".join(filter_values('action_type')) + "'" }} )
GROUP BY 1
:param column: column/filter name to lookup
:type column: str
:param default: default value to return if there's no matching columns
:type default: str
:return: returns a list of filter values
:type: list | superset/jinja_context.py | def filter_values(column, default=None):
""" Gets a values for a particular filter as a list
This is useful if:
- you want to use a filter box to filter a query where the name of filter box
column doesn't match the one in the select statement
- you want to have the ability for filter inside the main query for speed purposes
This searches for "filters" and "extra_filters" in form_data for a match
Usage example:
SELECT action, count(*) as times
FROM logs
WHERE action in ( {{ "'" + "','".join(filter_values('action_type')) + "'" }} )
GROUP BY 1
:param column: column/filter name to lookup
:type column: str
:param default: default value to return if there's no matching columns
:type default: str
:return: returns a list of filter values
:type: list
"""
form_data = json.loads(request.form.get('form_data', '{}'))
return_val = []
for filter_type in ['filters', 'extra_filters']:
if filter_type not in form_data:
continue
for f in form_data[filter_type]:
if f['col'] == column:
for v in f['val']:
return_val.append(v)
if return_val:
return return_val
if default:
return [default]
else:
return [] | def filter_values(column, default=None):
""" Gets a values for a particular filter as a list
This is useful if:
- you want to use a filter box to filter a query where the name of filter box
column doesn't match the one in the select statement
- you want to have the ability for filter inside the main query for speed purposes
This searches for "filters" and "extra_filters" in form_data for a match
Usage example:
SELECT action, count(*) as times
FROM logs
WHERE action in ( {{ "'" + "','".join(filter_values('action_type')) + "'" }} )
GROUP BY 1
:param column: column/filter name to lookup
:type column: str
:param default: default value to return if there's no matching columns
:type default: str
:return: returns a list of filter values
:type: list
"""
form_data = json.loads(request.form.get('form_data', '{}'))
return_val = []
for filter_type in ['filters', 'extra_filters']:
if filter_type not in form_data:
continue
for f in form_data[filter_type]:
if f['col'] == column:
for v in f['val']:
return_val.append(v)
if return_val:
return return_val
if default:
return [default]
else:
return [] | [
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train | BaseTemplateProcessor.process_template | Processes a sql template
>>> sql = "SELECT '{{ datetime(2017, 1, 1).isoformat() }}'"
>>> process_template(sql)
"SELECT '2017-01-01T00:00:00'" | superset/jinja_context.py | def process_template(self, sql, **kwargs):
"""Processes a sql template
>>> sql = "SELECT '{{ datetime(2017, 1, 1).isoformat() }}'"
>>> process_template(sql)
"SELECT '2017-01-01T00:00:00'"
"""
template = self.env.from_string(sql)
kwargs.update(self.context)
return template.render(kwargs) | def process_template(self, sql, **kwargs):
"""Processes a sql template
>>> sql = "SELECT '{{ datetime(2017, 1, 1).isoformat() }}'"
>>> process_template(sql)
"SELECT '2017-01-01T00:00:00'"
"""
template = self.env.from_string(sql)
kwargs.update(self.context)
return template.render(kwargs) | [
"Processes",
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"sql",
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/jinja_context.py#L165-L174 | [
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train | get_datasource_info | Compatibility layer for handling of datasource info
datasource_id & datasource_type used to be passed in the URL
directory, now they should come as part of the form_data,
This function allows supporting both without duplicating code | superset/views/utils.py | def get_datasource_info(datasource_id, datasource_type, form_data):
"""Compatibility layer for handling of datasource info
datasource_id & datasource_type used to be passed in the URL
directory, now they should come as part of the form_data,
This function allows supporting both without duplicating code"""
datasource = form_data.get('datasource', '')
if '__' in datasource:
datasource_id, datasource_type = datasource.split('__')
# The case where the datasource has been deleted
datasource_id = None if datasource_id == 'None' else datasource_id
if not datasource_id:
raise Exception(
'The datasource associated with this chart no longer exists')
datasource_id = int(datasource_id)
return datasource_id, datasource_type | def get_datasource_info(datasource_id, datasource_type, form_data):
"""Compatibility layer for handling of datasource info
datasource_id & datasource_type used to be passed in the URL
directory, now they should come as part of the form_data,
This function allows supporting both without duplicating code"""
datasource = form_data.get('datasource', '')
if '__' in datasource:
datasource_id, datasource_type = datasource.split('__')
# The case where the datasource has been deleted
datasource_id = None if datasource_id == 'None' else datasource_id
if not datasource_id:
raise Exception(
'The datasource associated with this chart no longer exists')
datasource_id = int(datasource_id)
return datasource_id, datasource_type | [
"Compatibility",
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train | SupersetSecurityManager.can_access | Protecting from has_access failing from missing perms/view | superset/security.py | def can_access(self, permission_name, view_name):
"""Protecting from has_access failing from missing perms/view"""
user = g.user
if user.is_anonymous:
return self.is_item_public(permission_name, view_name)
return self._has_view_access(user, permission_name, view_name) | def can_access(self, permission_name, view_name):
"""Protecting from has_access failing from missing perms/view"""
user = g.user
if user.is_anonymous:
return self.is_item_public(permission_name, view_name)
return self._has_view_access(user, permission_name, view_name) | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/security.py#L106-L111 | [
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train | SupersetSecurityManager.create_missing_perms | Creates missing perms for datasources, schemas and metrics | superset/security.py | def create_missing_perms(self):
"""Creates missing perms for datasources, schemas and metrics"""
from superset import db
from superset.models import core as models
logging.info(
'Fetching a set of all perms to lookup which ones are missing')
all_pvs = set()
for pv in self.get_session.query(self.permissionview_model).all():
if pv.permission and pv.view_menu:
all_pvs.add((pv.permission.name, pv.view_menu.name))
def merge_pv(view_menu, perm):
"""Create permission view menu only if it doesn't exist"""
if view_menu and perm and (view_menu, perm) not in all_pvs:
self.merge_perm(view_menu, perm)
logging.info('Creating missing datasource permissions.')
datasources = ConnectorRegistry.get_all_datasources(db.session)
for datasource in datasources:
merge_pv('datasource_access', datasource.get_perm())
merge_pv('schema_access', datasource.schema_perm)
logging.info('Creating missing database permissions.')
databases = db.session.query(models.Database).all()
for database in databases:
merge_pv('database_access', database.perm)
logging.info('Creating missing metrics permissions')
metrics = []
for datasource_class in ConnectorRegistry.sources.values():
metrics += list(db.session.query(datasource_class.metric_class).all())
for metric in metrics:
if metric.is_restricted:
merge_pv('metric_access', metric.perm) | def create_missing_perms(self):
"""Creates missing perms for datasources, schemas and metrics"""
from superset import db
from superset.models import core as models
logging.info(
'Fetching a set of all perms to lookup which ones are missing')
all_pvs = set()
for pv in self.get_session.query(self.permissionview_model).all():
if pv.permission and pv.view_menu:
all_pvs.add((pv.permission.name, pv.view_menu.name))
def merge_pv(view_menu, perm):
"""Create permission view menu only if it doesn't exist"""
if view_menu and perm and (view_menu, perm) not in all_pvs:
self.merge_perm(view_menu, perm)
logging.info('Creating missing datasource permissions.')
datasources = ConnectorRegistry.get_all_datasources(db.session)
for datasource in datasources:
merge_pv('datasource_access', datasource.get_perm())
merge_pv('schema_access', datasource.schema_perm)
logging.info('Creating missing database permissions.')
databases = db.session.query(models.Database).all()
for database in databases:
merge_pv('database_access', database.perm)
logging.info('Creating missing metrics permissions')
metrics = []
for datasource_class in ConnectorRegistry.sources.values():
metrics += list(db.session.query(datasource_class.metric_class).all())
for metric in metrics:
if metric.is_restricted:
merge_pv('metric_access', metric.perm) | [
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train | SupersetSecurityManager.clean_perms | FAB leaves faulty permissions that need to be cleaned up | superset/security.py | def clean_perms(self):
"""FAB leaves faulty permissions that need to be cleaned up"""
logging.info('Cleaning faulty perms')
sesh = self.get_session
pvms = (
sesh.query(ab_models.PermissionView)
.filter(or_(
ab_models.PermissionView.permission == None, # NOQA
ab_models.PermissionView.view_menu == None, # NOQA
))
)
deleted_count = pvms.delete()
sesh.commit()
if deleted_count:
logging.info('Deleted {} faulty permissions'.format(deleted_count)) | def clean_perms(self):
"""FAB leaves faulty permissions that need to be cleaned up"""
logging.info('Cleaning faulty perms')
sesh = self.get_session
pvms = (
sesh.query(ab_models.PermissionView)
.filter(or_(
ab_models.PermissionView.permission == None, # NOQA
ab_models.PermissionView.view_menu == None, # NOQA
))
)
deleted_count = pvms.delete()
sesh.commit()
if deleted_count:
logging.info('Deleted {} faulty permissions'.format(deleted_count)) | [
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train | SupersetSecurityManager.sync_role_definitions | Inits the Superset application with security roles and such | superset/security.py | def sync_role_definitions(self):
"""Inits the Superset application with security roles and such"""
from superset import conf
logging.info('Syncing role definition')
self.create_custom_permissions()
# Creating default roles
self.set_role('Admin', self.is_admin_pvm)
self.set_role('Alpha', self.is_alpha_pvm)
self.set_role('Gamma', self.is_gamma_pvm)
self.set_role('granter', self.is_granter_pvm)
self.set_role('sql_lab', self.is_sql_lab_pvm)
if conf.get('PUBLIC_ROLE_LIKE_GAMMA', False):
self.set_role('Public', self.is_gamma_pvm)
self.create_missing_perms()
# commit role and view menu updates
self.get_session.commit()
self.clean_perms() | def sync_role_definitions(self):
"""Inits the Superset application with security roles and such"""
from superset import conf
logging.info('Syncing role definition')
self.create_custom_permissions()
# Creating default roles
self.set_role('Admin', self.is_admin_pvm)
self.set_role('Alpha', self.is_alpha_pvm)
self.set_role('Gamma', self.is_gamma_pvm)
self.set_role('granter', self.is_granter_pvm)
self.set_role('sql_lab', self.is_sql_lab_pvm)
if conf.get('PUBLIC_ROLE_LIKE_GAMMA', False):
self.set_role('Public', self.is_gamma_pvm)
self.create_missing_perms()
# commit role and view menu updates
self.get_session.commit()
self.clean_perms() | [
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train | export_schema_to_dict | Exports the supported import/export schema to a dictionary | superset/utils/dict_import_export.py | def export_schema_to_dict(back_references):
"""Exports the supported import/export schema to a dictionary"""
databases = [Database.export_schema(recursive=True,
include_parent_ref=back_references)]
clusters = [DruidCluster.export_schema(recursive=True,
include_parent_ref=back_references)]
data = dict()
if databases:
data[DATABASES_KEY] = databases
if clusters:
data[DRUID_CLUSTERS_KEY] = clusters
return data | def export_schema_to_dict(back_references):
"""Exports the supported import/export schema to a dictionary"""
databases = [Database.export_schema(recursive=True,
include_parent_ref=back_references)]
clusters = [DruidCluster.export_schema(recursive=True,
include_parent_ref=back_references)]
data = dict()
if databases:
data[DATABASES_KEY] = databases
if clusters:
data[DRUID_CLUSTERS_KEY] = clusters
return data | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/utils/dict_import_export.py#L28-L39 | [
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train | export_to_dict | Exports databases and druid clusters to a dictionary | superset/utils/dict_import_export.py | def export_to_dict(session,
recursive,
back_references,
include_defaults):
"""Exports databases and druid clusters to a dictionary"""
logging.info('Starting export')
dbs = session.query(Database)
databases = [database.export_to_dict(recursive=recursive,
include_parent_ref=back_references,
include_defaults=include_defaults) for database in dbs]
logging.info('Exported %d %s', len(databases), DATABASES_KEY)
cls = session.query(DruidCluster)
clusters = [cluster.export_to_dict(recursive=recursive,
include_parent_ref=back_references,
include_defaults=include_defaults) for cluster in cls]
logging.info('Exported %d %s', len(clusters), DRUID_CLUSTERS_KEY)
data = dict()
if databases:
data[DATABASES_KEY] = databases
if clusters:
data[DRUID_CLUSTERS_KEY] = clusters
return data | def export_to_dict(session,
recursive,
back_references,
include_defaults):
"""Exports databases and druid clusters to a dictionary"""
logging.info('Starting export')
dbs = session.query(Database)
databases = [database.export_to_dict(recursive=recursive,
include_parent_ref=back_references,
include_defaults=include_defaults) for database in dbs]
logging.info('Exported %d %s', len(databases), DATABASES_KEY)
cls = session.query(DruidCluster)
clusters = [cluster.export_to_dict(recursive=recursive,
include_parent_ref=back_references,
include_defaults=include_defaults) for cluster in cls]
logging.info('Exported %d %s', len(clusters), DRUID_CLUSTERS_KEY)
data = dict()
if databases:
data[DATABASES_KEY] = databases
if clusters:
data[DRUID_CLUSTERS_KEY] = clusters
return data | [
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train | import_from_dict | Imports databases and druid clusters from dictionary | superset/utils/dict_import_export.py | def import_from_dict(session, data, sync=[]):
"""Imports databases and druid clusters from dictionary"""
if isinstance(data, dict):
logging.info('Importing %d %s',
len(data.get(DATABASES_KEY, [])),
DATABASES_KEY)
for database in data.get(DATABASES_KEY, []):
Database.import_from_dict(session, database, sync=sync)
logging.info('Importing %d %s',
len(data.get(DRUID_CLUSTERS_KEY, [])),
DRUID_CLUSTERS_KEY)
for datasource in data.get(DRUID_CLUSTERS_KEY, []):
DruidCluster.import_from_dict(session, datasource, sync=sync)
session.commit()
else:
logging.info('Supplied object is not a dictionary.') | def import_from_dict(session, data, sync=[]):
"""Imports databases and druid clusters from dictionary"""
if isinstance(data, dict):
logging.info('Importing %d %s',
len(data.get(DATABASES_KEY, [])),
DATABASES_KEY)
for database in data.get(DATABASES_KEY, []):
Database.import_from_dict(session, database, sync=sync)
logging.info('Importing %d %s',
len(data.get(DRUID_CLUSTERS_KEY, [])),
DRUID_CLUSTERS_KEY)
for datasource in data.get(DRUID_CLUSTERS_KEY, []):
DruidCluster.import_from_dict(session, datasource, sync=sync)
session.commit()
else:
logging.info('Supplied object is not a dictionary.') | [
"Imports",
"databases",
"and",
"druid",
"clusters",
"from",
"dictionary"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/utils/dict_import_export.py#L66-L82 | [
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train | Api.query | Takes a query_obj constructed in the client and returns payload data response
for the given query_obj.
params: query_context: json_blob | superset/views/api.py | def query(self):
"""
Takes a query_obj constructed in the client and returns payload data response
for the given query_obj.
params: query_context: json_blob
"""
query_context = QueryContext(**json.loads(request.form.get('query_context')))
security_manager.assert_datasource_permission(query_context.datasource)
payload_json = query_context.get_payload()
return json.dumps(
payload_json,
default=utils.json_int_dttm_ser,
ignore_nan=True,
) | def query(self):
"""
Takes a query_obj constructed in the client and returns payload data response
for the given query_obj.
params: query_context: json_blob
"""
query_context = QueryContext(**json.loads(request.form.get('query_context')))
security_manager.assert_datasource_permission(query_context.datasource)
payload_json = query_context.get_payload()
return json.dumps(
payload_json,
default=utils.json_int_dttm_ser,
ignore_nan=True,
) | [
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train | Api.query_form_data | Get the formdata stored in the database for existing slice.
params: slice_id: integer | superset/views/api.py | def query_form_data(self):
"""
Get the formdata stored in the database for existing slice.
params: slice_id: integer
"""
form_data = {}
slice_id = request.args.get('slice_id')
if slice_id:
slc = db.session.query(models.Slice).filter_by(id=slice_id).one_or_none()
if slc:
form_data = slc.form_data.copy()
update_time_range(form_data)
return json.dumps(form_data) | def query_form_data(self):
"""
Get the formdata stored in the database for existing slice.
params: slice_id: integer
"""
form_data = {}
slice_id = request.args.get('slice_id')
if slice_id:
slc = db.session.query(models.Slice).filter_by(id=slice_id).one_or_none()
if slc:
form_data = slc.form_data.copy()
update_time_range(form_data)
return json.dumps(form_data) | [
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train | load_css_templates | Loads 2 css templates to demonstrate the feature | superset/data/css_templates.py | def load_css_templates():
"""Loads 2 css templates to demonstrate the feature"""
print('Creating default CSS templates')
obj = db.session.query(CssTemplate).filter_by(template_name='Flat').first()
if not obj:
obj = CssTemplate(template_name='Flat')
css = textwrap.dedent("""\
.gridster div.widget {
transition: background-color 0.5s ease;
background-color: #FAFAFA;
border: 1px solid #CCC;
box-shadow: none;
border-radius: 0px;
}
.gridster div.widget:hover {
border: 1px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit()
obj = (
db.session.query(CssTemplate).filter_by(template_name='Courier Black').first())
if not obj:
obj = CssTemplate(template_name='Courier Black')
css = textwrap.dedent("""\
.gridster div.widget {
transition: background-color 0.5s ease;
background-color: #EEE;
border: 2px solid #444;
border-radius: 15px;
box-shadow: none;
}
h2 {
color: white;
font-size: 52px;
}
.navbar {
box-shadow: none;
}
.gridster div.widget:hover {
border: 2px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
.nvd3 text {
font-size: 12px;
font-family: inherit;
}
body{
background: #000;
font-family: Courier, Monaco, monospace;;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit() | def load_css_templates():
"""Loads 2 css templates to demonstrate the feature"""
print('Creating default CSS templates')
obj = db.session.query(CssTemplate).filter_by(template_name='Flat').first()
if not obj:
obj = CssTemplate(template_name='Flat')
css = textwrap.dedent("""\
.gridster div.widget {
transition: background-color 0.5s ease;
background-color: #FAFAFA;
border: 1px solid #CCC;
box-shadow: none;
border-radius: 0px;
}
.gridster div.widget:hover {
border: 1px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit()
obj = (
db.session.query(CssTemplate).filter_by(template_name='Courier Black').first())
if not obj:
obj = CssTemplate(template_name='Courier Black')
css = textwrap.dedent("""\
.gridster div.widget {
transition: background-color 0.5s ease;
background-color: #EEE;
border: 2px solid #444;
border-radius: 15px;
box-shadow: none;
}
h2 {
color: white;
font-size: 52px;
}
.navbar {
box-shadow: none;
}
.gridster div.widget:hover {
border: 2px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
.nvd3 text {
font-size: 12px;
font-family: inherit;
}
body{
background: #000;
font-family: Courier, Monaco, monospace;;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit() | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/css_templates.py#L23-L119 | [
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train | ImportMixin._parent_foreign_key_mappings | Get a mapping of foreign name to the local name of foreign keys | superset/models/helpers.py | def _parent_foreign_key_mappings(cls):
"""Get a mapping of foreign name to the local name of foreign keys"""
parent_rel = cls.__mapper__.relationships.get(cls.export_parent)
if parent_rel:
return {l.name: r.name for (l, r) in parent_rel.local_remote_pairs}
return {} | def _parent_foreign_key_mappings(cls):
"""Get a mapping of foreign name to the local name of foreign keys"""
parent_rel = cls.__mapper__.relationships.get(cls.export_parent)
if parent_rel:
return {l.name: r.name for (l, r) in parent_rel.local_remote_pairs}
return {} | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/models/helpers.py#L60-L65 | [
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train | ImportMixin._unique_constrains | Get all (single column and multi column) unique constraints | superset/models/helpers.py | def _unique_constrains(cls):
"""Get all (single column and multi column) unique constraints"""
unique = [{c.name for c in u.columns} for u in cls.__table_args__
if isinstance(u, UniqueConstraint)]
unique.extend({c.name} for c in cls.__table__.columns if c.unique)
return unique | def _unique_constrains(cls):
"""Get all (single column and multi column) unique constraints"""
unique = [{c.name for c in u.columns} for u in cls.__table_args__
if isinstance(u, UniqueConstraint)]
unique.extend({c.name} for c in cls.__table__.columns if c.unique)
return unique | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/models/helpers.py#L68-L73 | [
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train | ImportMixin.export_schema | Export schema as a dictionary | superset/models/helpers.py | def export_schema(cls, recursive=True, include_parent_ref=False):
"""Export schema as a dictionary"""
parent_excludes = {}
if not include_parent_ref:
parent_ref = cls.__mapper__.relationships.get(cls.export_parent)
if parent_ref:
parent_excludes = {c.name for c in parent_ref.local_columns}
def formatter(c):
return ('{0} Default ({1})'.format(
str(c.type), c.default.arg) if c.default else str(c.type))
schema = {c.name: formatter(c) for c in cls.__table__.columns
if (c.name in cls.export_fields and
c.name not in parent_excludes)}
if recursive:
for c in cls.export_children:
child_class = cls.__mapper__.relationships[c].argument.class_
schema[c] = [child_class.export_schema(recursive=recursive,
include_parent_ref=include_parent_ref)]
return schema | def export_schema(cls, recursive=True, include_parent_ref=False):
"""Export schema as a dictionary"""
parent_excludes = {}
if not include_parent_ref:
parent_ref = cls.__mapper__.relationships.get(cls.export_parent)
if parent_ref:
parent_excludes = {c.name for c in parent_ref.local_columns}
def formatter(c):
return ('{0} Default ({1})'.format(
str(c.type), c.default.arg) if c.default else str(c.type))
schema = {c.name: formatter(c) for c in cls.__table__.columns
if (c.name in cls.export_fields and
c.name not in parent_excludes)}
if recursive:
for c in cls.export_children:
child_class = cls.__mapper__.relationships[c].argument.class_
schema[c] = [child_class.export_schema(recursive=recursive,
include_parent_ref=include_parent_ref)]
return schema | [
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train | ImportMixin.import_from_dict | Import obj from a dictionary | superset/models/helpers.py | def import_from_dict(cls, session, dict_rep, parent=None,
recursive=True, sync=[]):
"""Import obj from a dictionary"""
parent_refs = cls._parent_foreign_key_mappings()
export_fields = set(cls.export_fields) | set(parent_refs.keys())
new_children = {c: dict_rep.get(c) for c in cls.export_children
if c in dict_rep}
unique_constrains = cls._unique_constrains()
filters = [] # Using these filters to check if obj already exists
# Remove fields that should not get imported
for k in list(dict_rep):
if k not in export_fields:
del dict_rep[k]
if not parent:
if cls.export_parent:
for p in parent_refs.keys():
if p not in dict_rep:
raise RuntimeError(
'{0}: Missing field {1}'.format(cls.__name__, p))
else:
# Set foreign keys to parent obj
for k, v in parent_refs.items():
dict_rep[k] = getattr(parent, v)
# Add filter for parent obj
filters.extend([getattr(cls, k) == dict_rep.get(k)
for k in parent_refs.keys()])
# Add filter for unique constraints
ucs = [and_(*[getattr(cls, k) == dict_rep.get(k)
for k in cs if dict_rep.get(k) is not None])
for cs in unique_constrains]
filters.append(or_(*ucs))
# Check if object already exists in DB, break if more than one is found
try:
obj_query = session.query(cls).filter(and_(*filters))
obj = obj_query.one_or_none()
except MultipleResultsFound as e:
logging.error('Error importing %s \n %s \n %s', cls.__name__,
str(obj_query),
yaml.safe_dump(dict_rep))
raise e
if not obj:
is_new_obj = True
# Create new DB object
obj = cls(**dict_rep)
logging.info('Importing new %s %s', obj.__tablename__, str(obj))
if cls.export_parent and parent:
setattr(obj, cls.export_parent, parent)
session.add(obj)
else:
is_new_obj = False
logging.info('Updating %s %s', obj.__tablename__, str(obj))
# Update columns
for k, v in dict_rep.items():
setattr(obj, k, v)
# Recursively create children
if recursive:
for c in cls.export_children:
child_class = cls.__mapper__.relationships[c].argument.class_
added = []
for c_obj in new_children.get(c, []):
added.append(child_class.import_from_dict(session=session,
dict_rep=c_obj,
parent=obj,
sync=sync))
# If children should get synced, delete the ones that did not
# get updated.
if c in sync and not is_new_obj:
back_refs = child_class._parent_foreign_key_mappings()
delete_filters = [getattr(child_class, k) ==
getattr(obj, back_refs.get(k))
for k in back_refs.keys()]
to_delete = set(session.query(child_class).filter(
and_(*delete_filters))).difference(set(added))
for o in to_delete:
logging.info('Deleting %s %s', c, str(obj))
session.delete(o)
return obj | def import_from_dict(cls, session, dict_rep, parent=None,
recursive=True, sync=[]):
"""Import obj from a dictionary"""
parent_refs = cls._parent_foreign_key_mappings()
export_fields = set(cls.export_fields) | set(parent_refs.keys())
new_children = {c: dict_rep.get(c) for c in cls.export_children
if c in dict_rep}
unique_constrains = cls._unique_constrains()
filters = [] # Using these filters to check if obj already exists
# Remove fields that should not get imported
for k in list(dict_rep):
if k not in export_fields:
del dict_rep[k]
if not parent:
if cls.export_parent:
for p in parent_refs.keys():
if p not in dict_rep:
raise RuntimeError(
'{0}: Missing field {1}'.format(cls.__name__, p))
else:
# Set foreign keys to parent obj
for k, v in parent_refs.items():
dict_rep[k] = getattr(parent, v)
# Add filter for parent obj
filters.extend([getattr(cls, k) == dict_rep.get(k)
for k in parent_refs.keys()])
# Add filter for unique constraints
ucs = [and_(*[getattr(cls, k) == dict_rep.get(k)
for k in cs if dict_rep.get(k) is not None])
for cs in unique_constrains]
filters.append(or_(*ucs))
# Check if object already exists in DB, break if more than one is found
try:
obj_query = session.query(cls).filter(and_(*filters))
obj = obj_query.one_or_none()
except MultipleResultsFound as e:
logging.error('Error importing %s \n %s \n %s', cls.__name__,
str(obj_query),
yaml.safe_dump(dict_rep))
raise e
if not obj:
is_new_obj = True
# Create new DB object
obj = cls(**dict_rep)
logging.info('Importing new %s %s', obj.__tablename__, str(obj))
if cls.export_parent and parent:
setattr(obj, cls.export_parent, parent)
session.add(obj)
else:
is_new_obj = False
logging.info('Updating %s %s', obj.__tablename__, str(obj))
# Update columns
for k, v in dict_rep.items():
setattr(obj, k, v)
# Recursively create children
if recursive:
for c in cls.export_children:
child_class = cls.__mapper__.relationships[c].argument.class_
added = []
for c_obj in new_children.get(c, []):
added.append(child_class.import_from_dict(session=session,
dict_rep=c_obj,
parent=obj,
sync=sync))
# If children should get synced, delete the ones that did not
# get updated.
if c in sync and not is_new_obj:
back_refs = child_class._parent_foreign_key_mappings()
delete_filters = [getattr(child_class, k) ==
getattr(obj, back_refs.get(k))
for k in back_refs.keys()]
to_delete = set(session.query(child_class).filter(
and_(*delete_filters))).difference(set(added))
for o in to_delete:
logging.info('Deleting %s %s', c, str(obj))
session.delete(o)
return obj | [
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train | ImportMixin.export_to_dict | Export obj to dictionary | superset/models/helpers.py | def export_to_dict(self, recursive=True, include_parent_ref=False,
include_defaults=False):
"""Export obj to dictionary"""
cls = self.__class__
parent_excludes = {}
if recursive and not include_parent_ref:
parent_ref = cls.__mapper__.relationships.get(cls.export_parent)
if parent_ref:
parent_excludes = {c.name for c in parent_ref.local_columns}
dict_rep = {c.name: getattr(self, c.name)
for c in cls.__table__.columns
if (c.name in self.export_fields and
c.name not in parent_excludes and
(include_defaults or (
getattr(self, c.name) is not None and
(not c.default or
getattr(self, c.name) != c.default.arg))))
}
if recursive:
for c in self.export_children:
# sorting to make lists of children stable
dict_rep[c] = sorted(
[
child.export_to_dict(
recursive=recursive,
include_parent_ref=include_parent_ref,
include_defaults=include_defaults,
) for child in getattr(self, c)
],
key=lambda k: sorted(k.items()))
return dict_rep | def export_to_dict(self, recursive=True, include_parent_ref=False,
include_defaults=False):
"""Export obj to dictionary"""
cls = self.__class__
parent_excludes = {}
if recursive and not include_parent_ref:
parent_ref = cls.__mapper__.relationships.get(cls.export_parent)
if parent_ref:
parent_excludes = {c.name for c in parent_ref.local_columns}
dict_rep = {c.name: getattr(self, c.name)
for c in cls.__table__.columns
if (c.name in self.export_fields and
c.name not in parent_excludes and
(include_defaults or (
getattr(self, c.name) is not None and
(not c.default or
getattr(self, c.name) != c.default.arg))))
}
if recursive:
for c in self.export_children:
# sorting to make lists of children stable
dict_rep[c] = sorted(
[
child.export_to_dict(
recursive=recursive,
include_parent_ref=include_parent_ref,
include_defaults=include_defaults,
) for child in getattr(self, c)
],
key=lambda k: sorted(k.items()))
return dict_rep | [
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train | ImportMixin.override | Overrides the plain fields of the dashboard. | superset/models/helpers.py | def override(self, obj):
"""Overrides the plain fields of the dashboard."""
for field in obj.__class__.export_fields:
setattr(self, field, getattr(obj, field)) | def override(self, obj):
"""Overrides the plain fields of the dashboard."""
for field in obj.__class__.export_fields:
setattr(self, field, getattr(obj, field)) | [
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train | update_time_range | Move since and until to time_range. | superset/legacy.py | def update_time_range(form_data):
"""Move since and until to time_range."""
if 'since' in form_data or 'until' in form_data:
form_data['time_range'] = '{} : {}'.format(
form_data.pop('since', '') or '',
form_data.pop('until', '') or '',
) | def update_time_range(form_data):
"""Move since and until to time_range."""
if 'since' in form_data or 'until' in form_data:
form_data['time_range'] = '{} : {}'.format(
form_data.pop('since', '') or '',
form_data.pop('until', '') or '',
) | [
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train | memoized_func | Use this decorator to cache functions that have predefined first arg.
enable_cache is treated as True by default,
except enable_cache = False is passed to the decorated function.
force means whether to force refresh the cache and is treated as False by default,
except force = True is passed to the decorated function.
timeout of cache is set to 600 seconds by default,
except cache_timeout = {timeout in seconds} is passed to the decorated function.
memoized_func uses simple_cache and stored the data in memory.
Key is a callable function that takes function arguments and
returns the caching key. | superset/utils/cache.py | def memoized_func(key=view_cache_key, attribute_in_key=None):
"""Use this decorator to cache functions that have predefined first arg.
enable_cache is treated as True by default,
except enable_cache = False is passed to the decorated function.
force means whether to force refresh the cache and is treated as False by default,
except force = True is passed to the decorated function.
timeout of cache is set to 600 seconds by default,
except cache_timeout = {timeout in seconds} is passed to the decorated function.
memoized_func uses simple_cache and stored the data in memory.
Key is a callable function that takes function arguments and
returns the caching key.
"""
def wrap(f):
if tables_cache:
def wrapped_f(self, *args, **kwargs):
if not kwargs.get('cache', True):
return f(self, *args, **kwargs)
if attribute_in_key:
cache_key = key(*args, **kwargs).format(
getattr(self, attribute_in_key))
else:
cache_key = key(*args, **kwargs)
o = tables_cache.get(cache_key)
if not kwargs.get('force') and o is not None:
return o
o = f(self, *args, **kwargs)
tables_cache.set(cache_key, o,
timeout=kwargs.get('cache_timeout'))
return o
else:
# noop
def wrapped_f(self, *args, **kwargs):
return f(self, *args, **kwargs)
return wrapped_f
return wrap | def memoized_func(key=view_cache_key, attribute_in_key=None):
"""Use this decorator to cache functions that have predefined first arg.
enable_cache is treated as True by default,
except enable_cache = False is passed to the decorated function.
force means whether to force refresh the cache and is treated as False by default,
except force = True is passed to the decorated function.
timeout of cache is set to 600 seconds by default,
except cache_timeout = {timeout in seconds} is passed to the decorated function.
memoized_func uses simple_cache and stored the data in memory.
Key is a callable function that takes function arguments and
returns the caching key.
"""
def wrap(f):
if tables_cache:
def wrapped_f(self, *args, **kwargs):
if not kwargs.get('cache', True):
return f(self, *args, **kwargs)
if attribute_in_key:
cache_key = key(*args, **kwargs).format(
getattr(self, attribute_in_key))
else:
cache_key = key(*args, **kwargs)
o = tables_cache.get(cache_key)
if not kwargs.get('force') and o is not None:
return o
o = f(self, *args, **kwargs)
tables_cache.set(cache_key, o,
timeout=kwargs.get('cache_timeout'))
return o
else:
# noop
def wrapped_f(self, *args, **kwargs):
return f(self, *args, **kwargs)
return wrapped_f
return wrap | [
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train | Query.name | Name property | superset/models/sql_lab.py | def name(self):
"""Name property"""
ts = datetime.now().isoformat()
ts = ts.replace('-', '').replace(':', '').split('.')[0]
tab = (self.tab_name.replace(' ', '_').lower()
if self.tab_name else 'notab')
tab = re.sub(r'\W+', '', tab)
return f'sqllab_{tab}_{ts}' | def name(self):
"""Name property"""
ts = datetime.now().isoformat()
ts = ts.replace('-', '').replace(':', '').split('.')[0]
tab = (self.tab_name.replace(' ', '_').lower()
if self.tab_name else 'notab')
tab = re.sub(r'\W+', '', tab)
return f'sqllab_{tab}_{ts}' | [
"Name",
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train | check_datasource_perms | Check if user can access a cached response from explore_json.
This function takes `self` since it must have the same signature as the
the decorated method. | superset/views/core.py | def check_datasource_perms(self, datasource_type=None, datasource_id=None):
"""
Check if user can access a cached response from explore_json.
This function takes `self` since it must have the same signature as the
the decorated method.
"""
form_data = get_form_data()[0]
datasource_id, datasource_type = get_datasource_info(
datasource_id, datasource_type, form_data)
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=False,
)
security_manager.assert_datasource_permission(viz_obj.datasource) | def check_datasource_perms(self, datasource_type=None, datasource_id=None):
"""
Check if user can access a cached response from explore_json.
This function takes `self` since it must have the same signature as the
the decorated method.
"""
form_data = get_form_data()[0]
datasource_id, datasource_type = get_datasource_info(
datasource_id, datasource_type, form_data)
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=False,
)
security_manager.assert_datasource_permission(viz_obj.datasource) | [
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train | check_slice_perms | Check if user can access a cached response from slice_json.
This function takes `self` since it must have the same signature as the
the decorated method. | superset/views/core.py | def check_slice_perms(self, slice_id):
"""
Check if user can access a cached response from slice_json.
This function takes `self` since it must have the same signature as the
the decorated method.
"""
form_data, slc = get_form_data(slice_id, use_slice_data=True)
datasource_type = slc.datasource.type
datasource_id = slc.datasource.id
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=False,
)
security_manager.assert_datasource_permission(viz_obj.datasource) | def check_slice_perms(self, slice_id):
"""
Check if user can access a cached response from slice_json.
This function takes `self` since it must have the same signature as the
the decorated method.
"""
form_data, slc = get_form_data(slice_id, use_slice_data=True)
datasource_type = slc.datasource.type
datasource_id = slc.datasource.id
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=False,
)
security_manager.assert_datasource_permission(viz_obj.datasource) | [
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train | apply_caching | Applies the configuration's http headers to all responses | superset/views/core.py | def apply_caching(response):
"""Applies the configuration's http headers to all responses"""
for k, v in config.get('HTTP_HEADERS').items():
response.headers[k] = v
return response | def apply_caching(response):
"""Applies the configuration's http headers to all responses"""
for k, v in config.get('HTTP_HEADERS').items():
response.headers[k] = v
return response | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L3017-L3021 | [
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train | Superset.override_role_permissions | Updates the role with the give datasource permissions.
Permissions not in the request will be revoked. This endpoint should
be available to admins only. Expects JSON in the format:
{
'role_name': '{role_name}',
'database': [{
'datasource_type': '{table|druid}',
'name': '{database_name}',
'schema': [{
'name': '{schema_name}',
'datasources': ['{datasource name}, {datasource name}']
}]
}]
} | superset/views/core.py | def override_role_permissions(self):
"""Updates the role with the give datasource permissions.
Permissions not in the request will be revoked. This endpoint should
be available to admins only. Expects JSON in the format:
{
'role_name': '{role_name}',
'database': [{
'datasource_type': '{table|druid}',
'name': '{database_name}',
'schema': [{
'name': '{schema_name}',
'datasources': ['{datasource name}, {datasource name}']
}]
}]
}
"""
data = request.get_json(force=True)
role_name = data['role_name']
databases = data['database']
db_ds_names = set()
for dbs in databases:
for schema in dbs['schema']:
for ds_name in schema['datasources']:
fullname = utils.get_datasource_full_name(
dbs['name'], ds_name, schema=schema['name'])
db_ds_names.add(fullname)
existing_datasources = ConnectorRegistry.get_all_datasources(db.session)
datasources = [
d for d in existing_datasources if d.full_name in db_ds_names]
role = security_manager.find_role(role_name)
# remove all permissions
role.permissions = []
# grant permissions to the list of datasources
granted_perms = []
for datasource in datasources:
view_menu_perm = security_manager.find_permission_view_menu(
view_menu_name=datasource.perm,
permission_name='datasource_access')
# prevent creating empty permissions
if view_menu_perm and view_menu_perm.view_menu:
role.permissions.append(view_menu_perm)
granted_perms.append(view_menu_perm.view_menu.name)
db.session.commit()
return self.json_response({
'granted': granted_perms,
'requested': list(db_ds_names),
}, status=201) | def override_role_permissions(self):
"""Updates the role with the give datasource permissions.
Permissions not in the request will be revoked. This endpoint should
be available to admins only. Expects JSON in the format:
{
'role_name': '{role_name}',
'database': [{
'datasource_type': '{table|druid}',
'name': '{database_name}',
'schema': [{
'name': '{schema_name}',
'datasources': ['{datasource name}, {datasource name}']
}]
}]
}
"""
data = request.get_json(force=True)
role_name = data['role_name']
databases = data['database']
db_ds_names = set()
for dbs in databases:
for schema in dbs['schema']:
for ds_name in schema['datasources']:
fullname = utils.get_datasource_full_name(
dbs['name'], ds_name, schema=schema['name'])
db_ds_names.add(fullname)
existing_datasources = ConnectorRegistry.get_all_datasources(db.session)
datasources = [
d for d in existing_datasources if d.full_name in db_ds_names]
role = security_manager.find_role(role_name)
# remove all permissions
role.permissions = []
# grant permissions to the list of datasources
granted_perms = []
for datasource in datasources:
view_menu_perm = security_manager.find_permission_view_menu(
view_menu_name=datasource.perm,
permission_name='datasource_access')
# prevent creating empty permissions
if view_menu_perm and view_menu_perm.view_menu:
role.permissions.append(view_menu_perm)
granted_perms.append(view_menu_perm.view_menu.name)
db.session.commit()
return self.json_response({
'granted': granted_perms,
'requested': list(db_ds_names),
}, status=201) | [
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train | Superset.explore_json | Serves all request that GET or POST form_data
This endpoint evolved to be the entry point of many different
requests that GETs or POSTs a form_data.
`self.generate_json` receives this input and returns different
payloads based on the request args in the first block
TODO: break into one endpoint for each return shape | superset/views/core.py | def explore_json(self, datasource_type=None, datasource_id=None):
"""Serves all request that GET or POST form_data
This endpoint evolved to be the entry point of many different
requests that GETs or POSTs a form_data.
`self.generate_json` receives this input and returns different
payloads based on the request args in the first block
TODO: break into one endpoint for each return shape"""
csv = request.args.get('csv') == 'true'
query = request.args.get('query') == 'true'
results = request.args.get('results') == 'true'
samples = request.args.get('samples') == 'true'
force = request.args.get('force') == 'true'
form_data = get_form_data()[0]
datasource_id, datasource_type = get_datasource_info(
datasource_id, datasource_type, form_data)
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=force,
)
return self.generate_json(
viz_obj,
csv=csv,
query=query,
results=results,
samples=samples,
) | def explore_json(self, datasource_type=None, datasource_id=None):
"""Serves all request that GET or POST form_data
This endpoint evolved to be the entry point of many different
requests that GETs or POSTs a form_data.
`self.generate_json` receives this input and returns different
payloads based on the request args in the first block
TODO: break into one endpoint for each return shape"""
csv = request.args.get('csv') == 'true'
query = request.args.get('query') == 'true'
results = request.args.get('results') == 'true'
samples = request.args.get('samples') == 'true'
force = request.args.get('force') == 'true'
form_data = get_form_data()[0]
datasource_id, datasource_type = get_datasource_info(
datasource_id, datasource_type, form_data)
viz_obj = get_viz(
datasource_type=datasource_type,
datasource_id=datasource_id,
form_data=form_data,
force=force,
)
return self.generate_json(
viz_obj,
csv=csv,
query=query,
results=results,
samples=samples,
) | [
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train | Superset.import_dashboards | Overrides the dashboards using json instances from the file. | superset/views/core.py | def import_dashboards(self):
"""Overrides the dashboards using json instances from the file."""
f = request.files.get('file')
if request.method == 'POST' and f:
dashboard_import_export.import_dashboards(db.session, f.stream)
return redirect('/dashboard/list/')
return self.render_template('superset/import_dashboards.html') | def import_dashboards(self):
"""Overrides the dashboards using json instances from the file."""
f = request.files.get('file')
if request.method == 'POST' and f:
dashboard_import_export.import_dashboards(db.session, f.stream)
return redirect('/dashboard/list/')
return self.render_template('superset/import_dashboards.html') | [
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"from",
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"file",
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1270-L1276 | [
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train | Superset.explorev2 | Deprecated endpoint, here for backward compatibility of urls | superset/views/core.py | def explorev2(self, datasource_type, datasource_id):
"""Deprecated endpoint, here for backward compatibility of urls"""
return redirect(url_for(
'Superset.explore',
datasource_type=datasource_type,
datasource_id=datasource_id,
**request.args)) | def explorev2(self, datasource_type, datasource_id):
"""Deprecated endpoint, here for backward compatibility of urls"""
return redirect(url_for(
'Superset.explore',
datasource_type=datasource_type,
datasource_id=datasource_id,
**request.args)) | [
"Deprecated",
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"backward",
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"of",
"urls"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1281-L1287 | [
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train | Superset.filter | Endpoint to retrieve values for specified column.
:param datasource_type: Type of datasource e.g. table
:param datasource_id: Datasource id
:param column: Column name to retrieve values for
:return: | superset/views/core.py | def filter(self, datasource_type, datasource_id, column):
"""
Endpoint to retrieve values for specified column.
:param datasource_type: Type of datasource e.g. table
:param datasource_id: Datasource id
:param column: Column name to retrieve values for
:return:
"""
# TODO: Cache endpoint by user, datasource and column
datasource = ConnectorRegistry.get_datasource(
datasource_type, datasource_id, db.session)
if not datasource:
return json_error_response(DATASOURCE_MISSING_ERR)
security_manager.assert_datasource_permission(datasource)
payload = json.dumps(
datasource.values_for_column(
column,
config.get('FILTER_SELECT_ROW_LIMIT', 10000),
),
default=utils.json_int_dttm_ser)
return json_success(payload) | def filter(self, datasource_type, datasource_id, column):
"""
Endpoint to retrieve values for specified column.
:param datasource_type: Type of datasource e.g. table
:param datasource_id: Datasource id
:param column: Column name to retrieve values for
:return:
"""
# TODO: Cache endpoint by user, datasource and column
datasource = ConnectorRegistry.get_datasource(
datasource_type, datasource_id, db.session)
if not datasource:
return json_error_response(DATASOURCE_MISSING_ERR)
security_manager.assert_datasource_permission(datasource)
payload = json.dumps(
datasource.values_for_column(
column,
config.get('FILTER_SELECT_ROW_LIMIT', 10000),
),
default=utils.json_int_dttm_ser)
return json_success(payload) | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1394-L1415 | [
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train | Superset.save_or_overwrite_slice | Save or overwrite a slice | superset/views/core.py | def save_or_overwrite_slice(
self, args, slc, slice_add_perm, slice_overwrite_perm, slice_download_perm,
datasource_id, datasource_type, datasource_name):
"""Save or overwrite a slice"""
slice_name = args.get('slice_name')
action = args.get('action')
form_data = get_form_data()[0]
if action in ('saveas'):
if 'slice_id' in form_data:
form_data.pop('slice_id') # don't save old slice_id
slc = models.Slice(owners=[g.user] if g.user else [])
slc.params = json.dumps(form_data, indent=2, sort_keys=True)
slc.datasource_name = datasource_name
slc.viz_type = form_data['viz_type']
slc.datasource_type = datasource_type
slc.datasource_id = datasource_id
slc.slice_name = slice_name
if action in ('saveas') and slice_add_perm:
self.save_slice(slc)
elif action == 'overwrite' and slice_overwrite_perm:
self.overwrite_slice(slc)
# Adding slice to a dashboard if requested
dash = None
if request.args.get('add_to_dash') == 'existing':
dash = (
db.session.query(models.Dashboard)
.filter_by(id=int(request.args.get('save_to_dashboard_id')))
.one()
)
# check edit dashboard permissions
dash_overwrite_perm = check_ownership(dash, raise_if_false=False)
if not dash_overwrite_perm:
return json_error_response(
_('You don\'t have the rights to ') + _('alter this ') +
_('dashboard'),
status=400)
flash(
_('Chart [{}] was added to dashboard [{}]').format(
slc.slice_name,
dash.dashboard_title),
'info')
elif request.args.get('add_to_dash') == 'new':
# check create dashboard permissions
dash_add_perm = security_manager.can_access('can_add', 'DashboardModelView')
if not dash_add_perm:
return json_error_response(
_('You don\'t have the rights to ') + _('create a ') + _('dashboard'),
status=400)
dash = models.Dashboard(
dashboard_title=request.args.get('new_dashboard_name'),
owners=[g.user] if g.user else [])
flash(
_('Dashboard [{}] just got created and chart [{}] was added '
'to it').format(
dash.dashboard_title,
slc.slice_name),
'info')
if dash and slc not in dash.slices:
dash.slices.append(slc)
db.session.commit()
response = {
'can_add': slice_add_perm,
'can_download': slice_download_perm,
'can_overwrite': is_owner(slc, g.user),
'form_data': slc.form_data,
'slice': slc.data,
'dashboard_id': dash.id if dash else None,
}
if request.args.get('goto_dash') == 'true':
response.update({'dashboard': dash.url})
return json_success(json.dumps(response)) | def save_or_overwrite_slice(
self, args, slc, slice_add_perm, slice_overwrite_perm, slice_download_perm,
datasource_id, datasource_type, datasource_name):
"""Save or overwrite a slice"""
slice_name = args.get('slice_name')
action = args.get('action')
form_data = get_form_data()[0]
if action in ('saveas'):
if 'slice_id' in form_data:
form_data.pop('slice_id') # don't save old slice_id
slc = models.Slice(owners=[g.user] if g.user else [])
slc.params = json.dumps(form_data, indent=2, sort_keys=True)
slc.datasource_name = datasource_name
slc.viz_type = form_data['viz_type']
slc.datasource_type = datasource_type
slc.datasource_id = datasource_id
slc.slice_name = slice_name
if action in ('saveas') and slice_add_perm:
self.save_slice(slc)
elif action == 'overwrite' and slice_overwrite_perm:
self.overwrite_slice(slc)
# Adding slice to a dashboard if requested
dash = None
if request.args.get('add_to_dash') == 'existing':
dash = (
db.session.query(models.Dashboard)
.filter_by(id=int(request.args.get('save_to_dashboard_id')))
.one()
)
# check edit dashboard permissions
dash_overwrite_perm = check_ownership(dash, raise_if_false=False)
if not dash_overwrite_perm:
return json_error_response(
_('You don\'t have the rights to ') + _('alter this ') +
_('dashboard'),
status=400)
flash(
_('Chart [{}] was added to dashboard [{}]').format(
slc.slice_name,
dash.dashboard_title),
'info')
elif request.args.get('add_to_dash') == 'new':
# check create dashboard permissions
dash_add_perm = security_manager.can_access('can_add', 'DashboardModelView')
if not dash_add_perm:
return json_error_response(
_('You don\'t have the rights to ') + _('create a ') + _('dashboard'),
status=400)
dash = models.Dashboard(
dashboard_title=request.args.get('new_dashboard_name'),
owners=[g.user] if g.user else [])
flash(
_('Dashboard [{}] just got created and chart [{}] was added '
'to it').format(
dash.dashboard_title,
slc.slice_name),
'info')
if dash and slc not in dash.slices:
dash.slices.append(slc)
db.session.commit()
response = {
'can_add': slice_add_perm,
'can_download': slice_download_perm,
'can_overwrite': is_owner(slc, g.user),
'form_data': slc.form_data,
'slice': slc.data,
'dashboard_id': dash.id if dash else None,
}
if request.args.get('goto_dash') == 'true':
response.update({'dashboard': dash.url})
return json_success(json.dumps(response)) | [
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train | Superset.checkbox | endpoint for checking/unchecking any boolean in a sqla model | superset/views/core.py | def checkbox(self, model_view, id_, attr, value):
"""endpoint for checking/unchecking any boolean in a sqla model"""
modelview_to_model = {
'{}ColumnInlineView'.format(name.capitalize()): source.column_class
for name, source in ConnectorRegistry.sources.items()
}
model = modelview_to_model[model_view]
col = db.session.query(model).filter_by(id=id_).first()
checked = value == 'true'
if col:
setattr(col, attr, checked)
if checked:
metrics = col.get_metrics().values()
col.datasource.add_missing_metrics(metrics)
db.session.commit()
return json_success('OK') | def checkbox(self, model_view, id_, attr, value):
"""endpoint for checking/unchecking any boolean in a sqla model"""
modelview_to_model = {
'{}ColumnInlineView'.format(name.capitalize()): source.column_class
for name, source in ConnectorRegistry.sources.items()
}
model = modelview_to_model[model_view]
col = db.session.query(model).filter_by(id=id_).first()
checked = value == 'true'
if col:
setattr(col, attr, checked)
if checked:
metrics = col.get_metrics().values()
col.datasource.add_missing_metrics(metrics)
db.session.commit()
return json_success('OK') | [
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train | Superset.tables | Endpoint to fetch the list of tables for given database | superset/views/core.py | def tables(self, db_id, schema, substr, force_refresh='false'):
"""Endpoint to fetch the list of tables for given database"""
db_id = int(db_id)
force_refresh = force_refresh.lower() == 'true'
schema = utils.js_string_to_python(schema)
substr = utils.js_string_to_python(substr)
database = db.session.query(models.Database).filter_by(id=db_id).one()
if schema:
table_names = database.all_table_names_in_schema(
schema=schema, force=force_refresh,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout)
view_names = database.all_view_names_in_schema(
schema=schema, force=force_refresh,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout)
else:
table_names = database.all_table_names_in_database(
cache=True, force=False, cache_timeout=24 * 60 * 60)
view_names = database.all_view_names_in_database(
cache=True, force=False, cache_timeout=24 * 60 * 60)
table_names = security_manager.accessible_by_user(database, table_names, schema)
view_names = security_manager.accessible_by_user(database, view_names, schema)
if substr:
table_names = [tn for tn in table_names if substr in tn]
view_names = [vn for vn in view_names if substr in vn]
if not schema and database.default_schemas:
def get_schema(tbl_or_view_name):
return tbl_or_view_name.split('.')[0] if '.' in tbl_or_view_name else None
user_schema = g.user.email.split('@')[0]
valid_schemas = set(database.default_schemas + [user_schema])
table_names = [tn for tn in table_names if get_schema(tn) in valid_schemas]
view_names = [vn for vn in view_names if get_schema(vn) in valid_schemas]
max_items = config.get('MAX_TABLE_NAMES') or len(table_names)
total_items = len(table_names) + len(view_names)
max_tables = len(table_names)
max_views = len(view_names)
if total_items and substr:
max_tables = max_items * len(table_names) // total_items
max_views = max_items * len(view_names) // total_items
table_options = [{'value': tn, 'label': tn}
for tn in table_names[:max_tables]]
table_options.extend([{'value': vn, 'label': '[view] {}'.format(vn)}
for vn in view_names[:max_views]])
payload = {
'tableLength': len(table_names) + len(view_names),
'options': table_options,
}
return json_success(json.dumps(payload)) | def tables(self, db_id, schema, substr, force_refresh='false'):
"""Endpoint to fetch the list of tables for given database"""
db_id = int(db_id)
force_refresh = force_refresh.lower() == 'true'
schema = utils.js_string_to_python(schema)
substr = utils.js_string_to_python(substr)
database = db.session.query(models.Database).filter_by(id=db_id).one()
if schema:
table_names = database.all_table_names_in_schema(
schema=schema, force=force_refresh,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout)
view_names = database.all_view_names_in_schema(
schema=schema, force=force_refresh,
cache=database.table_cache_enabled,
cache_timeout=database.table_cache_timeout)
else:
table_names = database.all_table_names_in_database(
cache=True, force=False, cache_timeout=24 * 60 * 60)
view_names = database.all_view_names_in_database(
cache=True, force=False, cache_timeout=24 * 60 * 60)
table_names = security_manager.accessible_by_user(database, table_names, schema)
view_names = security_manager.accessible_by_user(database, view_names, schema)
if substr:
table_names = [tn for tn in table_names if substr in tn]
view_names = [vn for vn in view_names if substr in vn]
if not schema and database.default_schemas:
def get_schema(tbl_or_view_name):
return tbl_or_view_name.split('.')[0] if '.' in tbl_or_view_name else None
user_schema = g.user.email.split('@')[0]
valid_schemas = set(database.default_schemas + [user_schema])
table_names = [tn for tn in table_names if get_schema(tn) in valid_schemas]
view_names = [vn for vn in view_names if get_schema(vn) in valid_schemas]
max_items = config.get('MAX_TABLE_NAMES') or len(table_names)
total_items = len(table_names) + len(view_names)
max_tables = len(table_names)
max_views = len(view_names)
if total_items and substr:
max_tables = max_items * len(table_names) // total_items
max_views = max_items * len(view_names) // total_items
table_options = [{'value': tn, 'label': tn}
for tn in table_names[:max_tables]]
table_options.extend([{'value': vn, 'label': '[view] {}'.format(vn)}
for vn in view_names[:max_views]])
payload = {
'tableLength': len(table_names) + len(view_names),
'options': table_options,
}
return json_success(json.dumps(payload)) | [
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train | Superset.copy_dash | Copy dashboard | superset/views/core.py | def copy_dash(self, dashboard_id):
"""Copy dashboard"""
session = db.session()
data = json.loads(request.form.get('data'))
dash = models.Dashboard()
original_dash = (
session
.query(models.Dashboard)
.filter_by(id=dashboard_id).first())
dash.owners = [g.user] if g.user else []
dash.dashboard_title = data['dashboard_title']
if data['duplicate_slices']:
# Duplicating slices as well, mapping old ids to new ones
old_to_new_sliceids = {}
for slc in original_dash.slices:
new_slice = slc.clone()
new_slice.owners = [g.user] if g.user else []
session.add(new_slice)
session.flush()
new_slice.dashboards.append(dash)
old_to_new_sliceids['{}'.format(slc.id)] = \
'{}'.format(new_slice.id)
# update chartId of layout entities
# in v2_dash positions json data, chartId should be integer,
# while in older version slice_id is string type
for value in data['positions'].values():
if (
isinstance(value, dict) and value.get('meta') and
value.get('meta').get('chartId')
):
old_id = '{}'.format(value.get('meta').get('chartId'))
new_id = int(old_to_new_sliceids[old_id])
value['meta']['chartId'] = new_id
else:
dash.slices = original_dash.slices
dash.params = original_dash.params
self._set_dash_metadata(dash, data)
session.add(dash)
session.commit()
dash_json = json.dumps(dash.data)
session.close()
return json_success(dash_json) | def copy_dash(self, dashboard_id):
"""Copy dashboard"""
session = db.session()
data = json.loads(request.form.get('data'))
dash = models.Dashboard()
original_dash = (
session
.query(models.Dashboard)
.filter_by(id=dashboard_id).first())
dash.owners = [g.user] if g.user else []
dash.dashboard_title = data['dashboard_title']
if data['duplicate_slices']:
# Duplicating slices as well, mapping old ids to new ones
old_to_new_sliceids = {}
for slc in original_dash.slices:
new_slice = slc.clone()
new_slice.owners = [g.user] if g.user else []
session.add(new_slice)
session.flush()
new_slice.dashboards.append(dash)
old_to_new_sliceids['{}'.format(slc.id)] = \
'{}'.format(new_slice.id)
# update chartId of layout entities
# in v2_dash positions json data, chartId should be integer,
# while in older version slice_id is string type
for value in data['positions'].values():
if (
isinstance(value, dict) and value.get('meta') and
value.get('meta').get('chartId')
):
old_id = '{}'.format(value.get('meta').get('chartId'))
new_id = int(old_to_new_sliceids[old_id])
value['meta']['chartId'] = new_id
else:
dash.slices = original_dash.slices
dash.params = original_dash.params
self._set_dash_metadata(dash, data)
session.add(dash)
session.commit()
dash_json = json.dumps(dash.data)
session.close()
return json_success(dash_json) | [
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train | Superset.save_dash | Save a dashboard's metadata | superset/views/core.py | def save_dash(self, dashboard_id):
"""Save a dashboard's metadata"""
session = db.session()
dash = (session
.query(models.Dashboard)
.filter_by(id=dashboard_id).first())
check_ownership(dash, raise_if_false=True)
data = json.loads(request.form.get('data'))
self._set_dash_metadata(dash, data)
session.merge(dash)
session.commit()
session.close()
return json_success(json.dumps({'status': 'SUCCESS'})) | def save_dash(self, dashboard_id):
"""Save a dashboard's metadata"""
session = db.session()
dash = (session
.query(models.Dashboard)
.filter_by(id=dashboard_id).first())
check_ownership(dash, raise_if_false=True)
data = json.loads(request.form.get('data'))
self._set_dash_metadata(dash, data)
session.merge(dash)
session.commit()
session.close()
return json_success(json.dumps({'status': 'SUCCESS'})) | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1674-L1686 | [
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train | Superset.add_slices | Add and save slices to a dashboard | superset/views/core.py | def add_slices(self, dashboard_id):
"""Add and save slices to a dashboard"""
data = json.loads(request.form.get('data'))
session = db.session()
Slice = models.Slice # noqa
dash = (
session.query(models.Dashboard).filter_by(id=dashboard_id).first())
check_ownership(dash, raise_if_false=True)
new_slices = session.query(Slice).filter(
Slice.id.in_(data['slice_ids']))
dash.slices += new_slices
session.merge(dash)
session.commit()
session.close()
return 'SLICES ADDED' | def add_slices(self, dashboard_id):
"""Add and save slices to a dashboard"""
data = json.loads(request.form.get('data'))
session = db.session()
Slice = models.Slice # noqa
dash = (
session.query(models.Dashboard).filter_by(id=dashboard_id).first())
check_ownership(dash, raise_if_false=True)
new_slices = session.query(Slice).filter(
Slice.id.in_(data['slice_ids']))
dash.slices += new_slices
session.merge(dash)
session.commit()
session.close()
return 'SLICES ADDED' | [
"Add",
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"save",
"slices",
"to",
"a",
"dashboard"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1749-L1763 | [
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train | Superset.recent_activity | Recent activity (actions) for a given user | superset/views/core.py | def recent_activity(self, user_id):
"""Recent activity (actions) for a given user"""
M = models # noqa
if request.args.get('limit'):
limit = int(request.args.get('limit'))
else:
limit = 1000
qry = (
db.session.query(M.Log, M.Dashboard, M.Slice)
.outerjoin(
M.Dashboard,
M.Dashboard.id == M.Log.dashboard_id,
)
.outerjoin(
M.Slice,
M.Slice.id == M.Log.slice_id,
)
.filter(
sqla.and_(
~M.Log.action.in_(('queries', 'shortner', 'sql_json')),
M.Log.user_id == user_id,
),
)
.order_by(M.Log.dttm.desc())
.limit(limit)
)
payload = []
for log in qry.all():
item_url = None
item_title = None
if log.Dashboard:
item_url = log.Dashboard.url
item_title = log.Dashboard.dashboard_title
elif log.Slice:
item_url = log.Slice.slice_url
item_title = log.Slice.slice_name
payload.append({
'action': log.Log.action,
'item_url': item_url,
'item_title': item_title,
'time': log.Log.dttm,
})
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | def recent_activity(self, user_id):
"""Recent activity (actions) for a given user"""
M = models # noqa
if request.args.get('limit'):
limit = int(request.args.get('limit'))
else:
limit = 1000
qry = (
db.session.query(M.Log, M.Dashboard, M.Slice)
.outerjoin(
M.Dashboard,
M.Dashboard.id == M.Log.dashboard_id,
)
.outerjoin(
M.Slice,
M.Slice.id == M.Log.slice_id,
)
.filter(
sqla.and_(
~M.Log.action.in_(('queries', 'shortner', 'sql_json')),
M.Log.user_id == user_id,
),
)
.order_by(M.Log.dttm.desc())
.limit(limit)
)
payload = []
for log in qry.all():
item_url = None
item_title = None
if log.Dashboard:
item_url = log.Dashboard.url
item_title = log.Dashboard.dashboard_title
elif log.Slice:
item_url = log.Slice.slice_url
item_title = log.Slice.slice_name
payload.append({
'action': log.Log.action,
'item_url': item_url,
'item_title': item_title,
'time': log.Log.dttm,
})
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | [
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"given",
"user"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1826-L1872 | [
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train | Superset.fave_dashboards_by_username | This lets us use a user's username to pull favourite dashboards | superset/views/core.py | def fave_dashboards_by_username(self, username):
"""This lets us use a user's username to pull favourite dashboards"""
user = security_manager.find_user(username=username)
return self.fave_dashboards(user.get_id()) | def fave_dashboards_by_username(self, username):
"""This lets us use a user's username to pull favourite dashboards"""
user = security_manager.find_user(username=username)
return self.fave_dashboards(user.get_id()) | [
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train | Superset.user_slices | List of slices a user created, or faved | superset/views/core.py | def user_slices(self, user_id=None):
"""List of slices a user created, or faved"""
if not user_id:
user_id = g.user.id
Slice = models.Slice # noqa
FavStar = models.FavStar # noqa
qry = (
db.session.query(Slice,
FavStar.dttm).join(
models.FavStar,
sqla.and_(
models.FavStar.user_id == int(user_id),
models.FavStar.class_name == 'slice',
models.Slice.id == models.FavStar.obj_id,
),
isouter=True).filter(
sqla.or_(
Slice.created_by_fk == user_id,
Slice.changed_by_fk == user_id,
FavStar.user_id == user_id,
),
)
.order_by(Slice.slice_name.asc())
)
payload = [{
'id': o.Slice.id,
'title': o.Slice.slice_name,
'url': o.Slice.slice_url,
'data': o.Slice.form_data,
'dttm': o.dttm if o.dttm else o.Slice.changed_on,
'viz_type': o.Slice.viz_type,
} for o in qry.all()]
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | def user_slices(self, user_id=None):
"""List of slices a user created, or faved"""
if not user_id:
user_id = g.user.id
Slice = models.Slice # noqa
FavStar = models.FavStar # noqa
qry = (
db.session.query(Slice,
FavStar.dttm).join(
models.FavStar,
sqla.and_(
models.FavStar.user_id == int(user_id),
models.FavStar.class_name == 'slice',
models.Slice.id == models.FavStar.obj_id,
),
isouter=True).filter(
sqla.or_(
Slice.created_by_fk == user_id,
Slice.changed_by_fk == user_id,
FavStar.user_id == user_id,
),
)
.order_by(Slice.slice_name.asc())
)
payload = [{
'id': o.Slice.id,
'title': o.Slice.slice_name,
'url': o.Slice.slice_url,
'data': o.Slice.form_data,
'dttm': o.dttm if o.dttm else o.Slice.changed_on,
'viz_type': o.Slice.viz_type,
} for o in qry.all()]
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | [
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"a",
"user",
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"faved"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L1977-L2010 | [
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train | Superset.created_slices | List of slices created by this user | superset/views/core.py | def created_slices(self, user_id=None):
"""List of slices created by this user"""
if not user_id:
user_id = g.user.id
Slice = models.Slice # noqa
qry = (
db.session.query(Slice)
.filter(
sqla.or_(
Slice.created_by_fk == user_id,
Slice.changed_by_fk == user_id,
),
)
.order_by(Slice.changed_on.desc())
)
payload = [{
'id': o.id,
'title': o.slice_name,
'url': o.slice_url,
'dttm': o.changed_on,
'viz_type': o.viz_type,
} for o in qry.all()]
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | def created_slices(self, user_id=None):
"""List of slices created by this user"""
if not user_id:
user_id = g.user.id
Slice = models.Slice # noqa
qry = (
db.session.query(Slice)
.filter(
sqla.or_(
Slice.created_by_fk == user_id,
Slice.changed_by_fk == user_id,
),
)
.order_by(Slice.changed_on.desc())
)
payload = [{
'id': o.id,
'title': o.slice_name,
'url': o.slice_url,
'dttm': o.changed_on,
'viz_type': o.viz_type,
} for o in qry.all()]
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | [
"List",
"of",
"slices",
"created",
"by",
"this",
"user"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L2016-L2039 | [
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train | Superset.fave_slices | Favorite slices for a user | superset/views/core.py | def fave_slices(self, user_id=None):
"""Favorite slices for a user"""
if not user_id:
user_id = g.user.id
qry = (
db.session.query(
models.Slice,
models.FavStar.dttm,
)
.join(
models.FavStar,
sqla.and_(
models.FavStar.user_id == int(user_id),
models.FavStar.class_name == 'slice',
models.Slice.id == models.FavStar.obj_id,
),
)
.order_by(
models.FavStar.dttm.desc(),
)
)
payload = []
for o in qry.all():
d = {
'id': o.Slice.id,
'title': o.Slice.slice_name,
'url': o.Slice.slice_url,
'dttm': o.dttm,
'viz_type': o.Slice.viz_type,
}
if o.Slice.created_by:
user = o.Slice.created_by
d['creator'] = str(user)
d['creator_url'] = '/superset/profile/{}/'.format(
user.username)
payload.append(d)
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | def fave_slices(self, user_id=None):
"""Favorite slices for a user"""
if not user_id:
user_id = g.user.id
qry = (
db.session.query(
models.Slice,
models.FavStar.dttm,
)
.join(
models.FavStar,
sqla.and_(
models.FavStar.user_id == int(user_id),
models.FavStar.class_name == 'slice',
models.Slice.id == models.FavStar.obj_id,
),
)
.order_by(
models.FavStar.dttm.desc(),
)
)
payload = []
for o in qry.all():
d = {
'id': o.Slice.id,
'title': o.Slice.slice_name,
'url': o.Slice.slice_url,
'dttm': o.dttm,
'viz_type': o.Slice.viz_type,
}
if o.Slice.created_by:
user = o.Slice.created_by
d['creator'] = str(user)
d['creator_url'] = '/superset/profile/{}/'.format(
user.username)
payload.append(d)
return json_success(
json.dumps(payload, default=utils.json_int_dttm_ser)) | [
"Favorite",
"slices",
"for",
"a",
"user"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L2045-L2082 | [
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train | Superset.warm_up_cache | Warms up the cache for the slice or table.
Note for slices a force refresh occurs. | superset/views/core.py | def warm_up_cache(self):
"""Warms up the cache for the slice or table.
Note for slices a force refresh occurs.
"""
slices = None
session = db.session()
slice_id = request.args.get('slice_id')
table_name = request.args.get('table_name')
db_name = request.args.get('db_name')
if not slice_id and not (table_name and db_name):
return json_error_response(__(
'Malformed request. slice_id or table_name and db_name '
'arguments are expected'), status=400)
if slice_id:
slices = session.query(models.Slice).filter_by(id=slice_id).all()
if not slices:
return json_error_response(__(
'Chart %(id)s not found', id=slice_id), status=404)
elif table_name and db_name:
SqlaTable = ConnectorRegistry.sources['table']
table = (
session.query(SqlaTable)
.join(models.Database)
.filter(
models.Database.database_name == db_name or
SqlaTable.table_name == table_name)
).first()
if not table:
return json_error_response(__(
"Table %(t)s wasn't found in the database %(d)s",
t=table_name, s=db_name), status=404)
slices = session.query(models.Slice).filter_by(
datasource_id=table.id,
datasource_type=table.type).all()
for slc in slices:
try:
form_data = get_form_data(slc.id, use_slice_data=True)[0]
obj = get_viz(
datasource_type=slc.datasource.type,
datasource_id=slc.datasource.id,
form_data=form_data,
force=True,
)
obj.get_json()
except Exception as e:
return json_error_response(utils.error_msg_from_exception(e))
return json_success(json.dumps(
[{'slice_id': slc.id, 'slice_name': slc.slice_name}
for slc in slices])) | def warm_up_cache(self):
"""Warms up the cache for the slice or table.
Note for slices a force refresh occurs.
"""
slices = None
session = db.session()
slice_id = request.args.get('slice_id')
table_name = request.args.get('table_name')
db_name = request.args.get('db_name')
if not slice_id and not (table_name and db_name):
return json_error_response(__(
'Malformed request. slice_id or table_name and db_name '
'arguments are expected'), status=400)
if slice_id:
slices = session.query(models.Slice).filter_by(id=slice_id).all()
if not slices:
return json_error_response(__(
'Chart %(id)s not found', id=slice_id), status=404)
elif table_name and db_name:
SqlaTable = ConnectorRegistry.sources['table']
table = (
session.query(SqlaTable)
.join(models.Database)
.filter(
models.Database.database_name == db_name or
SqlaTable.table_name == table_name)
).first()
if not table:
return json_error_response(__(
"Table %(t)s wasn't found in the database %(d)s",
t=table_name, s=db_name), status=404)
slices = session.query(models.Slice).filter_by(
datasource_id=table.id,
datasource_type=table.type).all()
for slc in slices:
try:
form_data = get_form_data(slc.id, use_slice_data=True)[0]
obj = get_viz(
datasource_type=slc.datasource.type,
datasource_id=slc.datasource.id,
form_data=form_data,
force=True,
)
obj.get_json()
except Exception as e:
return json_error_response(utils.error_msg_from_exception(e))
return json_success(json.dumps(
[{'slice_id': slc.id, 'slice_name': slc.slice_name}
for slc in slices])) | [
"Warms",
"up",
"the",
"cache",
"for",
"the",
"slice",
"or",
"table",
"."
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L2087-L2138 | [
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train | Superset.favstar | Toggle favorite stars on Slices and Dashboard | superset/views/core.py | def favstar(self, class_name, obj_id, action):
"""Toggle favorite stars on Slices and Dashboard"""
session = db.session()
FavStar = models.FavStar # noqa
count = 0
favs = session.query(FavStar).filter_by(
class_name=class_name, obj_id=obj_id,
user_id=g.user.get_id()).all()
if action == 'select':
if not favs:
session.add(
FavStar(
class_name=class_name,
obj_id=obj_id,
user_id=g.user.get_id(),
dttm=datetime.now(),
),
)
count = 1
elif action == 'unselect':
for fav in favs:
session.delete(fav)
else:
count = len(favs)
session.commit()
return json_success(json.dumps({'count': count})) | def favstar(self, class_name, obj_id, action):
"""Toggle favorite stars on Slices and Dashboard"""
session = db.session()
FavStar = models.FavStar # noqa
count = 0
favs = session.query(FavStar).filter_by(
class_name=class_name, obj_id=obj_id,
user_id=g.user.get_id()).all()
if action == 'select':
if not favs:
session.add(
FavStar(
class_name=class_name,
obj_id=obj_id,
user_id=g.user.get_id(),
dttm=datetime.now(),
),
)
count = 1
elif action == 'unselect':
for fav in favs:
session.delete(fav)
else:
count = len(favs)
session.commit()
return json_success(json.dumps({'count': count})) | [
"Toggle",
"favorite",
"stars",
"on",
"Slices",
"and",
"Dashboard"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L2142-L2167 | [
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train | Superset.dashboard | Server side rendering for a dashboard | superset/views/core.py | def dashboard(self, dashboard_id):
"""Server side rendering for a dashboard"""
session = db.session()
qry = session.query(models.Dashboard)
if dashboard_id.isdigit():
qry = qry.filter_by(id=int(dashboard_id))
else:
qry = qry.filter_by(slug=dashboard_id)
dash = qry.one_or_none()
if not dash:
abort(404)
datasources = set()
for slc in dash.slices:
datasource = slc.datasource
if datasource:
datasources.add(datasource)
if config.get('ENABLE_ACCESS_REQUEST'):
for datasource in datasources:
if datasource and not security_manager.datasource_access(datasource):
flash(
__(security_manager.get_datasource_access_error_msg(datasource)),
'danger')
return redirect(
'superset/request_access/?'
f'dashboard_id={dash.id}&')
dash_edit_perm = check_ownership(dash, raise_if_false=False) and \
security_manager.can_access('can_save_dash', 'Superset')
dash_save_perm = security_manager.can_access('can_save_dash', 'Superset')
superset_can_explore = security_manager.can_access('can_explore', 'Superset')
superset_can_csv = security_manager.can_access('can_csv', 'Superset')
slice_can_edit = security_manager.can_access('can_edit', 'SliceModelView')
standalone_mode = request.args.get('standalone') == 'true'
edit_mode = request.args.get('edit') == 'true'
# Hack to log the dashboard_id properly, even when getting a slug
@log_this
def dashboard(**kwargs): # noqa
pass
dashboard(
dashboard_id=dash.id,
dashboard_version='v2',
dash_edit_perm=dash_edit_perm,
edit_mode=edit_mode)
dashboard_data = dash.data
dashboard_data.update({
'standalone_mode': standalone_mode,
'dash_save_perm': dash_save_perm,
'dash_edit_perm': dash_edit_perm,
'superset_can_explore': superset_can_explore,
'superset_can_csv': superset_can_csv,
'slice_can_edit': slice_can_edit,
})
bootstrap_data = {
'user_id': g.user.get_id(),
'dashboard_data': dashboard_data,
'datasources': {ds.uid: ds.data for ds in datasources},
'common': self.common_bootsrap_payload(),
'editMode': edit_mode,
}
if request.args.get('json') == 'true':
return json_success(json.dumps(bootstrap_data))
return self.render_template(
'superset/dashboard.html',
entry='dashboard',
standalone_mode=standalone_mode,
title=dash.dashboard_title,
bootstrap_data=json.dumps(bootstrap_data),
) | def dashboard(self, dashboard_id):
"""Server side rendering for a dashboard"""
session = db.session()
qry = session.query(models.Dashboard)
if dashboard_id.isdigit():
qry = qry.filter_by(id=int(dashboard_id))
else:
qry = qry.filter_by(slug=dashboard_id)
dash = qry.one_or_none()
if not dash:
abort(404)
datasources = set()
for slc in dash.slices:
datasource = slc.datasource
if datasource:
datasources.add(datasource)
if config.get('ENABLE_ACCESS_REQUEST'):
for datasource in datasources:
if datasource and not security_manager.datasource_access(datasource):
flash(
__(security_manager.get_datasource_access_error_msg(datasource)),
'danger')
return redirect(
'superset/request_access/?'
f'dashboard_id={dash.id}&')
dash_edit_perm = check_ownership(dash, raise_if_false=False) and \
security_manager.can_access('can_save_dash', 'Superset')
dash_save_perm = security_manager.can_access('can_save_dash', 'Superset')
superset_can_explore = security_manager.can_access('can_explore', 'Superset')
superset_can_csv = security_manager.can_access('can_csv', 'Superset')
slice_can_edit = security_manager.can_access('can_edit', 'SliceModelView')
standalone_mode = request.args.get('standalone') == 'true'
edit_mode = request.args.get('edit') == 'true'
# Hack to log the dashboard_id properly, even when getting a slug
@log_this
def dashboard(**kwargs): # noqa
pass
dashboard(
dashboard_id=dash.id,
dashboard_version='v2',
dash_edit_perm=dash_edit_perm,
edit_mode=edit_mode)
dashboard_data = dash.data
dashboard_data.update({
'standalone_mode': standalone_mode,
'dash_save_perm': dash_save_perm,
'dash_edit_perm': dash_edit_perm,
'superset_can_explore': superset_can_explore,
'superset_can_csv': superset_can_csv,
'slice_can_edit': slice_can_edit,
})
bootstrap_data = {
'user_id': g.user.get_id(),
'dashboard_data': dashboard_data,
'datasources': {ds.uid: ds.data for ds in datasources},
'common': self.common_bootsrap_payload(),
'editMode': edit_mode,
}
if request.args.get('json') == 'true':
return json_success(json.dumps(bootstrap_data))
return self.render_template(
'superset/dashboard.html',
entry='dashboard',
standalone_mode=standalone_mode,
title=dash.dashboard_title,
bootstrap_data=json.dumps(bootstrap_data),
) | [
"Server",
"side",
"rendering",
"for",
"a",
"dashboard"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/views/core.py#L2171-L2246 | [
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] | ca2996c78f679260eb79c6008e276733df5fb653 |
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