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config.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""The main config file for Superset
All configuration in this file can be overridden by providing a superset_config
in your PYTHONPATH as there is a ``from superset_config import *``
at the end of this file.
"""
# pylint: disable=too-many-lines
from __future__ import annotations
import imp # pylint: disable=deprecated-module
import importlib.util
import json
import logging
import os
import re
import sys
from collections import OrderedDict
from datetime import timedelta
from email.mime.multipart import MIMEMultipart
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Set,
Tuple,
Type,
TYPE_CHECKING,
TypedDict,
Union,
)
import pkg_resources
from cachelib.base import BaseCache
from celery.schedules import crontab
from dateutil import tz
from flask import Blueprint
from flask_appbuilder.security.manager import AUTH_DB
from pandas._libs.parsers import STR_NA_VALUES # pylint: disable=no-name-in-module
from sqlalchemy.orm.query import Query
from superset.advanced_data_type.plugins.internet_address import internet_address
from superset.advanced_data_type.plugins.internet_port import internet_port
from superset.advanced_data_type.types import AdvancedDataType
from superset.constants import CHANGE_ME_SECRET_KEY
from superset.jinja_context import BaseTemplateProcessor
from superset.stats_logger import DummyStatsLogger
from superset.superset_typing import CacheConfig
from superset.tasks.types import ExecutorType
from superset.utils.core import is_test, NO_TIME_RANGE, parse_boolean_string
from superset.utils.encrypt import SQLAlchemyUtilsAdapter
from superset.utils.log import DBEventLogger
from superset.utils.logging_configurator import DefaultLoggingConfigurator
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from flask_appbuilder.security.sqla import models
from superset.connectors.sqla.models import SqlaTable
from superset.models.core import Database
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
# Realtime stats logger, a StatsD implementation exists
STATS_LOGGER = DummyStatsLogger()
EVENT_LOGGER = DBEventLogger()
SUPERSET_LOG_VIEW = True
BASE_DIR = pkg_resources.resource_filename("superset", "")
if "SUPERSET_HOME" in os.environ:
DATA_DIR = os.environ["SUPERSET_HOME"]
else:
DATA_DIR = os.path.expanduser("~/.superset")
# ---------------------------------------------------------
# Superset specific config
# ---------------------------------------------------------
VERSION_INFO_FILE = pkg_resources.resource_filename(
"superset", "static/version_info.json"
)
PACKAGE_JSON_FILE = pkg_resources.resource_filename(
"superset", "static/assets/package.json"
)
# Multiple favicons can be specified here. The "href" property
# is mandatory, but "sizes," "type," and "rel" are optional.
# For example:
# {
# "href":path/to/image.png",
# "sizes": "16x16",
# "type": "image/png"
# "rel": "icon"
# },
FAVICONS = [{"href": "/static/assets/images/favicon.png"}]
def _try_json_readversion(filepath: str) -> Optional[str]:
try:
with open(filepath, "r") as f:
return json.load(f).get("version")
except Exception: # pylint: disable=broad-except
return None
def _try_json_readsha(filepath: str, length: int) -> Optional[str]:
try:
with open(filepath, "r") as f:
return json.load(f).get("GIT_SHA")[:length]
except Exception: # pylint: disable=broad-except
return None
#
# If True, we will skip the call to load the logger config found in alembic.init
#
ALEMBIC_SKIP_LOG_CONFIG = False
# Depending on the context in which this config is loaded, the
# version_info.json file may or may not be available, as it is
# generated on install via setup.py. In the event that we're
# actually running Superset, we will have already installed,
# therefore it WILL exist. When unit tests are running, however,
# it WILL NOT exist, so we fall back to reading package.json
VERSION_STRING = _try_json_readversion(VERSION_INFO_FILE) or _try_json_readversion(
PACKAGE_JSON_FILE
)
VERSION_SHA_LENGTH = 8
VERSION_SHA = _try_json_readsha(VERSION_INFO_FILE, VERSION_SHA_LENGTH)
# Build number is shown in the About section if available. This
# can be replaced at build time to expose build information.
BUILD_NUMBER = None
# default viz used in chart explorer & SQL Lab explore
DEFAULT_VIZ_TYPE = "table"
# default row limit when requesting chart data
ROW_LIMIT = 50000
# default row limit when requesting samples from datasource in explore view
SAMPLES_ROW_LIMIT = 1000
# max rows retrieved by filter select auto complete
FILTER_SELECT_ROW_LIMIT = 10000
# default time filter in explore
# values may be "Last day", "Last week", "<ISO date> : now", etc.
DEFAULT_TIME_FILTER = NO_TIME_RANGE
SUPERSET_WEBSERVER_PROTOCOL = "http"
SUPERSET_WEBSERVER_ADDRESS = "0.0.0.0"
SUPERSET_WEBSERVER_PORT = 8088
# This is an important setting, and should be lower than your
# [load balancer / proxy / envoy / kong / ...] timeout settings.
# You should also make sure to configure your WSGI server
# (gunicorn, nginx, apache, ...) timeout setting to be <= to this setting
SUPERSET_WEBSERVER_TIMEOUT = int(timedelta(minutes=1).total_seconds())
# this 2 settings are used by dashboard period force refresh feature
# When user choose auto force refresh frequency
# < SUPERSET_DASHBOARD_PERIODICAL_REFRESH_LIMIT
# they will see warning message in the Refresh Interval Modal.
# please check PR #9886
SUPERSET_DASHBOARD_PERIODICAL_REFRESH_LIMIT = 0
SUPERSET_DASHBOARD_PERIODICAL_REFRESH_WARNING_MESSAGE = None
SUPERSET_DASHBOARD_POSITION_DATA_LIMIT = 65535
CUSTOM_SECURITY_MANAGER = None
SQLALCHEMY_TRACK_MODIFICATIONS = False
# ---------------------------------------------------------
# Your App secret key. Make sure you override it on superset_config.py
# or use `SUPERSET_SECRET_KEY` environment variable.
# Use a strong complex alphanumeric string and use a tool to help you generate
# a sufficiently random sequence, ex: openssl rand -base64 42"
SECRET_KEY = os.environ.get("SUPERSET_SECRET_KEY") or CHANGE_ME_SECRET_KEY
# The SQLAlchemy connection string.
SQLALCHEMY_DATABASE_URI = "sqlite:///" + os.path.join(DATA_DIR, "superset.db")
# SQLALCHEMY_DATABASE_URI = 'mysql://myapp@localhost/myapp'
# SQLALCHEMY_DATABASE_URI = 'postgresql://root:password@localhost/myapp'
# In order to hook up a custom password store for all SQLACHEMY connections
# implement a function that takes a single argument of type 'sqla.engine.url',
# returns a password and set SQLALCHEMY_CUSTOM_PASSWORD_STORE.
#
# e.g.:
# def lookup_password(url):
# return 'secret'
# SQLALCHEMY_CUSTOM_PASSWORD_STORE = lookup_password
SQLALCHEMY_CUSTOM_PASSWORD_STORE = None
#
# The EncryptedFieldTypeAdapter is used whenever we're building SqlAlchemy models
# which include sensitive fields that should be app-encrypted BEFORE sending
# to the DB.
#
# Note: the default impl leverages SqlAlchemyUtils' EncryptedType, which defaults
# to AesEngine that uses AES-128 under the covers using the app's SECRET_KEY
# as key material. Do note that AesEngine allows for queryability over the
# encrypted fields.
#
# To change the default engine you need to define your own adapter:
#
# e.g.:
#
# class AesGcmEncryptedAdapter(
# AbstractEncryptedFieldAdapter
# ):
# def create(
# self,
# app_config: Optional[Dict[str, Any]],
# *args: List[Any],
# **kwargs: Optional[Dict[str, Any]],
# ) -> TypeDecorator:
# if app_config:
# return EncryptedType(
# *args, app_config["SECRET_KEY"], engine=AesGcmEngine, **kwargs
# )
# raise Exception("Missing app_config kwarg")
#
#
# SQLALCHEMY_ENCRYPTED_FIELD_TYPE_ADAPTER = AesGcmEncryptedAdapter
SQLALCHEMY_ENCRYPTED_FIELD_TYPE_ADAPTER = ( # pylint: disable=invalid-name
SQLAlchemyUtilsAdapter
)
# The limit of queries fetched for query search
QUERY_SEARCH_LIMIT = 1000
# Flask-WTF flag for CSRF
WTF_CSRF_ENABLED = True
# Add endpoints that need to be exempt from CSRF protection
WTF_CSRF_EXEMPT_LIST = [
"superset.views.core.log",
"superset.views.core.explore_json",
"superset.charts.data.api.data",
]
# Whether to run the web server in debug mode or not
DEBUG = os.environ.get("FLASK_ENV") == "development"
FLASK_USE_RELOAD = True
# Enable profiling of Python calls. Turn this on and append ``?_instrument=1``
# to the page to see the call stack.
PROFILING = False
# Superset allows server-side python stacktraces to be surfaced to the
# user when this feature is on. This may has security implications
# and it's more secure to turn it off in production settings.
SHOW_STACKTRACE = True
# Use all X-Forwarded headers when ENABLE_PROXY_FIX is True.
# When proxying to a different port, set "x_port" to 0 to avoid downstream issues.
ENABLE_PROXY_FIX = False
PROXY_FIX_CONFIG = {"x_for": 1, "x_proto": 1, "x_host": 1, "x_port": 1, "x_prefix": 1}
# Configuration for scheduling queries from SQL Lab.
SCHEDULED_QUERIES: Dict[str, Any] = {}
# ------------------------------
# GLOBALS FOR APP Builder
# ------------------------------
# Uncomment to setup Your App name
APP_NAME = "Superset"
# Specify the App icon
APP_ICON = "/static/assets/images/superset-logo-horiz.png"
# Specify where clicking the logo would take the user
# e.g. setting it to '/' would take the user to '/superset/welcome/'
LOGO_TARGET_PATH = None
# Specify tooltip that should appear when hovering over the App Icon/Logo
LOGO_TOOLTIP = ""
# Specify any text that should appear to the right of the logo
LOGO_RIGHT_TEXT: Union[Callable[[], str], str] = ""
# Enables SWAGGER UI for superset openapi spec
# ex: http://localhost:8080/swagger/v1
FAB_API_SWAGGER_UI = True
# Druid query timezone
# tz.tzutc() : Using utc timezone
# tz.tzlocal() : Using local timezone
# tz.gettz('Asia/Shanghai') : Using the time zone with specific name
# [TimeZone List]
# See: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
# other tz can be overridden by providing a local_config
DRUID_TZ = tz.tzutc()
DRUID_ANALYSIS_TYPES = ["cardinality"]
# ----------------------------------------------------
# AUTHENTICATION CONFIG
# ----------------------------------------------------
# The authentication type
# AUTH_OID : Is for OpenID
# AUTH_DB : Is for database (username/password)
# AUTH_LDAP : Is for LDAP
# AUTH_REMOTE_USER : Is for using REMOTE_USER from web server
AUTH_TYPE = AUTH_DB
# Uncomment to setup Full admin role name
# AUTH_ROLE_ADMIN = 'Admin'
# Uncomment to setup Public role name, no authentication needed
# AUTH_ROLE_PUBLIC = 'Public'
# Will allow user self registration
# AUTH_USER_REGISTRATION = True
# The default user self registration role
# AUTH_USER_REGISTRATION_ROLE = "Public"
# When using LDAP Auth, setup the LDAP server
# AUTH_LDAP_SERVER = "ldap://ldapserver.new"
# Uncomment to setup OpenID providers example for OpenID authentication
# OPENID_PROVIDERS = [
# { 'name': 'Yahoo', 'url': 'https://open.login.yahoo.com/' },
# { 'name': 'Flickr', 'url': 'https://www.flickr.com/<username>' },
# ---------------------------------------------------
# Roles config
# ---------------------------------------------------
# Grant public role the same set of permissions as for a selected builtin role.
# This is useful if one wants to enable anonymous users to view
# dashboards. Explicit grant on specific datasets is still required.
PUBLIC_ROLE_LIKE: Optional[str] = None
# ---------------------------------------------------
# Babel config for translations
# ---------------------------------------------------
# Setup default language
BABEL_DEFAULT_LOCALE = "en"
# Your application default translation path
BABEL_DEFAULT_FOLDER = "superset/translations"
# The allowed translation for you app
LANGUAGES = {
"en": {"flag": "us", "name": "English"},
"es": {"flag": "es", "name": "Spanish"},
"it": {"flag": "it", "name": "Italian"},
"fr": {"flag": "fr", "name": "French"},
"zh": {"flag": "cn", "name": "Chinese"},
"ja": {"flag": "jp", "name": "Japanese"},
"de": {"flag": "de", "name": "German"},
"pt": {"flag": "pt", "name": "Portuguese"},
"pt_BR": {"flag": "br", "name": "Brazilian Portuguese"},
"ru": {"flag": "ru", "name": "Russian"},
"ko": {"flag": "kr", "name": "Korean"},
"sk": {"flag": "sk", "name": "Slovak"},
"sl": {"flag": "si", "name": "Slovenian"},
"nl": {"flag": "nl", "name": "Dutch"},
}
# Turning off i18n by default as translation in most languages are
# incomplete and not well maintained.
LANGUAGES = {}
# ---------------------------------------------------
# Feature flags
# ---------------------------------------------------
# Feature flags that are set by default go here. Their values can be
# overwritten by those specified under FEATURE_FLAGS in superset_config.py
# For example, DEFAULT_FEATURE_FLAGS = { 'FOO': True, 'BAR': False } here
# and FEATURE_FLAGS = { 'BAR': True, 'BAZ': True } in superset_config.py
# will result in combined feature flags of { 'FOO': True, 'BAR': True, 'BAZ': True }
DEFAULT_FEATURE_FLAGS: Dict[str, bool] = {
# allow dashboard to use sub-domains to send chart request
# you also need ENABLE_CORS and
# SUPERSET_WEBSERVER_DOMAINS for list of domains
"ALLOW_DASHBOARD_DOMAIN_SHARDING": True,
# Experimental feature introducing a client (browser) cache
"CLIENT_CACHE": False,
"DISABLE_DATASET_SOURCE_EDIT": False,
# When using a recent version of Druid that supports JOINs turn this on
"DRUID_JOINS": False,
"DYNAMIC_PLUGINS": False,
# With Superset 2.0, we are updating the default so that the legacy datasource
# editor no longer shows. Currently this is set to false so that the editor
# option does show, but we will be depreciating it.
"DISABLE_LEGACY_DATASOURCE_EDITOR": True,
# For some security concerns, you may need to enforce CSRF protection on
# all query request to explore_json endpoint. In Superset, we use
# `flask-csrf <https://sjl.bitbucket.io/flask-csrf/>`_ add csrf protection
# for all POST requests, but this protection doesn't apply to GET method.
# When ENABLE_EXPLORE_JSON_CSRF_PROTECTION is set to true, your users cannot
# make GET request to explore_json. explore_json accepts both GET and POST request.
# See `PR 7935 <https://github.com/apache/superset/pull/7935>`_ for more details.
"ENABLE_EXPLORE_JSON_CSRF_PROTECTION": False,
"ENABLE_TEMPLATE_PROCESSING": False,
"ENABLE_TEMPLATE_REMOVE_FILTERS": False,
# Allow for javascript controls components
# this enables programmers to customize certain charts (like the
# geospatial ones) by inputing javascript in controls. This exposes
# an XSS security vulnerability
"ENABLE_JAVASCRIPT_CONTROLS": False,
"KV_STORE": False,
# When this feature is enabled, nested types in Presto will be
# expanded into extra columns and/or arrays. This is experimental,
# and doesn't work with all nested types.
"PRESTO_EXPAND_DATA": False,
# Exposes API endpoint to compute thumbnails
"THUMBNAILS": False,
"DASHBOARD_CACHE": False,
"REMOVE_SLICE_LEVEL_LABEL_COLORS": False,
"SHARE_QUERIES_VIA_KV_STORE": False,
"TAGGING_SYSTEM": False,
"SQLLAB_BACKEND_PERSISTENCE": True,
"LISTVIEWS_DEFAULT_CARD_VIEW": False,
# When True, this flag allows display of HTML tags in Markdown components
"DISPLAY_MARKDOWN_HTML": True,
# When True, this escapes HTML (rather than rendering it) in Markdown components
"ESCAPE_MARKDOWN_HTML": False,
"DASHBOARD_NATIVE_FILTERS": True,
"DASHBOARD_CROSS_FILTERS": False,
# Feature is under active development and breaking changes are expected
"DASHBOARD_NATIVE_FILTERS_SET": False,
"DASHBOARD_FILTERS_EXPERIMENTAL": False,
"DASHBOARD_VIRTUALIZATION": False,
"GLOBAL_ASYNC_QUERIES": False,
"VERSIONED_EXPORT": True,
"EMBEDDED_SUPERSET": False,
# Enables Alerts and reports new implementation
"ALERT_REPORTS": False,
"DASHBOARD_RBAC": False,
"ENABLE_EXPLORE_DRAG_AND_DROP": True,
"ENABLE_FILTER_BOX_MIGRATION": False,
"ENABLE_ADVANCED_DATA_TYPES": False,
"ENABLE_DND_WITH_CLICK_UX": True,
# Enabling ALERTS_ATTACH_REPORTS, the system sends email and slack message
# with screenshot and link
# Disables ALERTS_ATTACH_REPORTS, the system DOES NOT generate screenshot
# for report with type 'alert' and sends email and slack message with only link;
# for report with type 'report' still send with email and slack message with
# screenshot and link
"ALERTS_ATTACH_REPORTS": True,
# FORCE_DATABASE_CONNECTIONS_SSL is depreciated.
"FORCE_DATABASE_CONNECTIONS_SSL": False,
# Enabling ENFORCE_DB_ENCRYPTION_UI forces all database connections to be
# encrypted before being saved into superset metastore.
"ENFORCE_DB_ENCRYPTION_UI": False,
# Allow users to export full CSV of table viz type.
# This could cause the server to run out of memory or compute.
"ALLOW_FULL_CSV_EXPORT": False,
"UX_BETA": False,
"GENERIC_CHART_AXES": False,
"ALLOW_ADHOC_SUBQUERY": False,
"USE_ANALAGOUS_COLORS": False,
"DASHBOARD_EDIT_CHART_IN_NEW_TAB": False,
# Apply RLS rules to SQL Lab queries. This requires parsing and manipulating the
# query, and might break queries and/or allow users to bypass RLS. Use with care!
"RLS_IN_SQLLAB": False,
# Enable caching per impersonation key (e.g username) in a datasource where user
# impersonation is enabled
"CACHE_IMPERSONATION": False,
# Enable sharing charts with embedding
"EMBEDDABLE_CHARTS": True,
"DRILL_TO_DETAIL": False,
"DATAPANEL_CLOSED_BY_DEFAULT": False,
"HORIZONTAL_FILTER_BAR": False,
# The feature is off by default, and currently only supported in Presto and Postgres,
# and Bigquery.
# It also needs to be enabled on a per-database basis, by adding the key/value pair
# `cost_estimate_enabled: true` to the database `extra` attribute.
"ESTIMATE_QUERY_COST": False,
# Allow users to enable ssh tunneling when creating a DB.
# Users must check whether the DB engine supports SSH Tunnels
# otherwise enabling this flag won't have any effect on the DB.
"SSH_TUNNELING": False,
}
# ------------------------------
# SSH Tunnel
# ------------------------------
# Allow users to set the host used when connecting to the SSH Tunnel
# as localhost and any other alias (0.0.0.0)
# ----------------------------------------------------------------------
# |
# -------------+ | +----------+
# LOCAL | | | REMOTE | :22 SSH
# CLIENT | <== SSH ========> | SERVER | :8080 web service
# -------------+ | +----------+
# |
# FIREWALL (only port 22 is open)
# ----------------------------------------------------------------------
SSH_TUNNEL_MANAGER_CLASS = "superset.extensions.ssh.SSHManager"
SSH_TUNNEL_LOCAL_BIND_ADDRESS = "127.0.0.1"
# Feature flags may also be set via 'SUPERSET_FEATURE_' prefixed environment vars.
DEFAULT_FEATURE_FLAGS.update(
{
k[len("SUPERSET_FEATURE_") :]: parse_boolean_string(v)
for k, v in os.environ.items()
if re.search(r"^SUPERSET_FEATURE_\w+", k)
}
)
# This is merely a default.
FEATURE_FLAGS: Dict[str, bool] = {}
# A function that receives a dict of all feature flags
# (DEFAULT_FEATURE_FLAGS merged with FEATURE_FLAGS)
# can alter it, and returns a similar dict. Note the dict of feature
# flags passed to the function is a deepcopy of the dict in the config,
# and can therefore be mutated without side-effect
#
# GET_FEATURE_FLAGS_FUNC can be used to implement progressive rollouts,
# role-based features, or a full on A/B testing framework.
#
# from flask import g, request
# def GET_FEATURE_FLAGS_FUNC(feature_flags_dict: Dict[str, bool]) -> Dict[str, bool]:
# if hasattr(g, "user") and g.user.is_active:
# feature_flags_dict['some_feature'] = g.user and g.user.get_id() == 5
# return feature_flags_dict
GET_FEATURE_FLAGS_FUNC: Optional[Callable[[Dict[str, bool]], Dict[str, bool]]] = None
# A function that receives a feature flag name and an optional default value.
# Has a similar utility to GET_FEATURE_FLAGS_FUNC but it's useful to not force the
# evaluation of all feature flags when just evaluating a single one.
#
# Note that the default `get_feature_flags` will evaluate each feature with this
# callable when the config key is set, so don't use both GET_FEATURE_FLAGS_FUNC
# and IS_FEATURE_ENABLED_FUNC in conjunction.
IS_FEATURE_ENABLED_FUNC: Optional[Callable[[str, Optional[bool]], bool]] = None
# A function that expands/overrides the frontend `bootstrap_data.common` object.
# Can be used to implement custom frontend functionality,
# or dynamically change certain configs.
#
# Values in `bootstrap_data.common` should have these characteristics:
# - They are not specific to a page the user is visiting
# - They do not contain secrets
#
# Takes as a parameter the common bootstrap payload before transformations.
# Returns a dict containing data that should be added or overridden to the payload.
COMMON_BOOTSTRAP_OVERRIDES_FUNC: Callable[
[Dict[str, Any]], Dict[str, Any]
] = lambda data: {} # default: empty dict
# EXTRA_CATEGORICAL_COLOR_SCHEMES is used for adding custom categorical color schemes
# example code for "My custom warm to hot" color scheme
# EXTRA_CATEGORICAL_COLOR_SCHEMES = [
# {
# "id": 'myVisualizationColors',
# "description": '',
# "label": 'My Visualization Colors',
# "isDefault": True,
# "colors":
# ['#006699', '#009DD9', '#5AAA46', '#44AAAA', '#DDAA77', '#7799BB', '#88AA77',
# '#552288', '#5AAA46', '#CC7788', '#EEDD55', '#9977BB', '#BBAA44', '#DDCCDD']
# }]
# This is merely a default
EXTRA_CATEGORICAL_COLOR_SCHEMES: List[Dict[str, Any]] = []
# THEME_OVERRIDES is used for adding custom theme to superset
# example code for "My theme" custom scheme
# THEME_OVERRIDES = {
# "borderRadius": 4,
# "colors": {
# "primary": {
# "base": 'red',
# },
# "secondary": {
# "base": 'green',
# },
# "grayscale": {
# "base": 'orange',
# }
# }
# }
THEME_OVERRIDES: Dict[str, Any] = {}
# EXTRA_SEQUENTIAL_COLOR_SCHEMES is used for adding custom sequential color schemes
# EXTRA_SEQUENTIAL_COLOR_SCHEMES = [
# {
# "id": 'warmToHot',
# "description": '',
# "isDiverging": True,
# "label": 'My custom warm to hot',
# "isDefault": True,
# "colors":
# ['#552288', '#5AAA46', '#CC7788', '#EEDD55', '#9977BB', '#BBAA44', '#DDCCDD',
# '#006699', '#009DD9', '#5AAA46', '#44AAAA', '#DDAA77', '#7799BB', '#88AA77']
# }]
# This is merely a default
EXTRA_SEQUENTIAL_COLOR_SCHEMES: List[Dict[str, Any]] = []
# ---------------------------------------------------
# Thumbnail config (behind feature flag)
# ---------------------------------------------------
# When executing Alerts & Reports or Thumbnails as the Selenium user, this defines
# the username of the account used to render the queries and dashboards/charts
THUMBNAIL_SELENIUM_USER: Optional[str] = "admin"
# To be able to have different thumbnails for different users, use these configs to
# define which user to execute the thumbnails and potentially custom functions for
# calculating thumbnail digests. To have unique thumbnails for all users, use the
# following config:
# THUMBNAIL_EXECUTE_AS = [ExecutorType.CURRENT_USER]
THUMBNAIL_EXECUTE_AS = [ExecutorType.SELENIUM]
# By default, thumbnail digests are calculated based on various parameters in the
# chart/dashboard metadata, and in the case of user-specific thumbnails, the
# username. To specify a custom digest function, use the following config parameters
# to define callbacks that receive
# 1. the model (dashboard or chart)
# 2. the executor type (e.g. ExecutorType.SELENIUM)
# 3. the executor's username (note, this is the executor as defined by
# `THUMBNAIL_EXECUTE_AS`; the executor is only equal to the currently logged in
# user if the executor type is equal to `ExecutorType.CURRENT_USER`)
# and return the final digest string:
THUMBNAIL_DASHBOARD_DIGEST_FUNC: Optional[
Callable[[Dashboard, ExecutorType, str], str]
] = None
THUMBNAIL_CHART_DIGEST_FUNC: Optional[Callable[[Slice, ExecutorType, str], str]] = None
THUMBNAIL_CACHE_CONFIG: CacheConfig = {
"CACHE_TYPE": "NullCache",
"CACHE_NO_NULL_WARNING": True,
}
# Time before selenium times out after trying to locate an element on the page and wait
# for that element to load for a screenshot.
SCREENSHOT_LOCATE_WAIT = int(timedelta(seconds=10).total_seconds())
# Time before selenium times out after waiting for all DOM class elements named
# "loading" are gone.
SCREENSHOT_LOAD_WAIT = int(timedelta(minutes=1).total_seconds())
# Selenium destroy retries
SCREENSHOT_SELENIUM_RETRIES = 5
# Give selenium an headstart, in seconds
SCREENSHOT_SELENIUM_HEADSTART = 3
# Wait for the chart animation, in seconds
SCREENSHOT_SELENIUM_ANIMATION_WAIT = 5
# Replace unexpected errors in screenshots with real error messages
SCREENSHOT_REPLACE_UNEXPECTED_ERRORS = False
# Max time to wait for error message modal to show up, in seconds
SCREENSHOT_WAIT_FOR_ERROR_MODAL_VISIBLE = 5
# Max time to wait for error message modal to close, in seconds
SCREENSHOT_WAIT_FOR_ERROR_MODAL_INVISIBLE = 5
# ---------------------------------------------------
# Image and file configuration
# ---------------------------------------------------
# The file upload folder, when using models with files
UPLOAD_FOLDER = BASE_DIR + "/app/static/uploads/"
UPLOAD_CHUNK_SIZE = 4096
# The image upload folder, when using models with images
IMG_UPLOAD_FOLDER = BASE_DIR + "/app/static/uploads/"
# The image upload url, when using models with images
IMG_UPLOAD_URL = "/static/uploads/"
# Setup image size default is (300, 200, True)
# IMG_SIZE = (300, 200, True)
# Default cache timeout, applies to all cache backends unless specifically overridden in
# each cache config.
CACHE_DEFAULT_TIMEOUT = int(timedelta(days=1).total_seconds())
# Default cache for Superset objects
CACHE_CONFIG: CacheConfig = {"CACHE_TYPE": "NullCache"}
# Cache for datasource metadata and query results
DATA_CACHE_CONFIG: CacheConfig = {"CACHE_TYPE": "NullCache"}
# Cache for dashboard filter state (`CACHE_TYPE` defaults to `SimpleCache` when
# running in debug mode unless overridden)
FILTER_STATE_CACHE_CONFIG: CacheConfig = {
"CACHE_DEFAULT_TIMEOUT": int(timedelta(days=90).total_seconds()),
# should the timeout be reset when retrieving a cached value
"REFRESH_TIMEOUT_ON_RETRIEVAL": True,
}
# Cache for explore form data state (`CACHE_TYPE` defaults to `SimpleCache` when
# running in debug mode unless overridden)
EXPLORE_FORM_DATA_CACHE_CONFIG: CacheConfig = {
"CACHE_DEFAULT_TIMEOUT": int(timedelta(days=7).total_seconds()),
# should the timeout be reset when retrieving a cached value
"REFRESH_TIMEOUT_ON_RETRIEVAL": True,
}
# store cache keys by datasource UID (via CacheKey) for custom processing/invalidation
STORE_CACHE_KEYS_IN_METADATA_DB = False
# CORS Options
ENABLE_CORS = False
CORS_OPTIONS: Dict[Any, Any] = {}
# Sanitizes the HTML content used in markdowns to allow its rendering in a safe manner.
# Disabling this option is not recommended for security reasons. If you wish to allow
# valid safe elements that are not included in the default sanitization schema, use the
# HTML_SANITIZATION_SCHEMA_EXTENSIONS configuration.
HTML_SANITIZATION = True
# Use this configuration to extend the HTML sanitization schema.
# By default we use the Gihtub schema defined in
# https://github.com/syntax-tree/hast-util-sanitize/blob/main/lib/schema.js
# For example, the following configuration would allow the rendering of the
# style attribute for div elements and the ftp protocol in hrefs:
# HTML_SANITIZATION_SCHEMA_EXTENSIONS = {
# "attributes": {
# "div": ["style"],
# },
# "protocols": {
# "href": ["ftp"],
# }
# }
# Be careful when extending the default schema to avoid XSS attacks.
HTML_SANITIZATION_SCHEMA_EXTENSIONS: Dict[str, Any] = {}
# Chrome allows up to 6 open connections per domain at a time. When there are more
# than 6 slices in dashboard, a lot of time fetch requests are queued up and wait for
# next available socket. PR #5039 is trying to allow domain sharding for Superset,
# and this feature will be enabled by configuration only (by default Superset
# doesn't allow cross-domain request).
SUPERSET_WEBSERVER_DOMAINS = None
# Allowed format types for upload on Database view
EXCEL_EXTENSIONS = {"xlsx", "xls"}
CSV_EXTENSIONS = {"csv", "tsv", "txt"}
COLUMNAR_EXTENSIONS = {"parquet", "zip"}
ALLOWED_EXTENSIONS = {*EXCEL_EXTENSIONS, *CSV_EXTENSIONS, *COLUMNAR_EXTENSIONS}
# CSV Options: key/value pairs that will be passed as argument to DataFrame.to_csv
# method.
# note: index option should not be overridden
CSV_EXPORT = {"encoding": "utf-8"}
# Excel Options: key/value pairs that will be passed as argument to DataFrame.to_excel
# method.
# note: index option should not be overridden
EXCEL_EXPORT = {"encoding": "utf-8"}
# ---------------------------------------------------
# Time grain configurations
# ---------------------------------------------------
# List of time grains to disable in the application (see list of builtin
# time grains in superset/db_engine_specs/base.py).
# For example: to disable 1 second time grain:
# TIME_GRAIN_DENYLIST = ['PT1S']
TIME_GRAIN_DENYLIST: List[str] = []
# Additional time grains to be supported using similar definitions as in
# superset/db_engine_specs/base.py.
# For example: To add a new 2 second time grain:
# TIME_GRAIN_ADDONS = {'PT2S': '2 second'}
TIME_GRAIN_ADDONS: Dict[str, str] = {}
# Implementation of additional time grains per engine.
# The column to be truncated is denoted `{col}` in the expression.
# For example: To implement 2 second time grain on clickhouse engine:
# TIME_GRAIN_ADDON_EXPRESSIONS = {
# 'clickhouse': {
# 'PT2S': 'toDateTime(intDiv(toUInt32(toDateTime({col})), 2)*2)'
# }
# }
TIME_GRAIN_ADDON_EXPRESSIONS: Dict[str, Dict[str, str]] = {}
# ---------------------------------------------------
# List of viz_types not allowed in your environment
# For example: Disable pivot table and treemap:
# VIZ_TYPE_DENYLIST = ['pivot_table', 'treemap']
# ---------------------------------------------------
VIZ_TYPE_DENYLIST: List[str] = []
# --------------------------------------------------
# Modules, datasources and middleware to be registered
# --------------------------------------------------
DEFAULT_MODULE_DS_MAP = OrderedDict(
[
("superset.connectors.sqla.models", ["SqlaTable"]),
]
)
ADDITIONAL_MODULE_DS_MAP: Dict[str, List[str]] = {}
ADDITIONAL_MIDDLEWARE: List[Callable[..., Any]] = []
# 1) https://docs.python-guide.org/writing/logging/
# 2) https://docs.python.org/2/library/logging.config.html
# Default configurator will consume the LOG_* settings below
LOGGING_CONFIGURATOR = DefaultLoggingConfigurator()
# Console Log Settings
LOG_FORMAT = "%(asctime)s:%(levelname)s:%(name)s:%(message)s"
LOG_LEVEL = "DEBUG"
# ---------------------------------------------------
# Enable Time Rotate Log Handler
# ---------------------------------------------------
# LOG_LEVEL = DEBUG, INFO, WARNING, ERROR, CRITICAL
ENABLE_TIME_ROTATE = False
TIME_ROTATE_LOG_LEVEL = "DEBUG"
FILENAME = os.path.join(DATA_DIR, "superset.log")
ROLLOVER = "midnight"
INTERVAL = 1
BACKUP_COUNT = 30
# Custom logger for auditing queries. This can be used to send ran queries to a
# structured immutable store for auditing purposes. The function is called for
# every query ran, in both SQL Lab and charts/dashboards.
# def QUERY_LOGGER(
# database,
# query,
# schema=None,
# user=None, # TODO(john-bodley): Deprecate in 3.0.
# client=None,
# security_manager=None,
# log_params=None,
# ):
# pass
QUERY_LOGGER = None
# Set this API key to enable Mapbox visualizations
MAPBOX_API_KEY = os.environ.get("MAPBOX_API_KEY", "")
# Maximum number of rows returned for any analytical database query
SQL_MAX_ROW = 100000
# Maximum number of rows displayed in SQL Lab UI
# Is set to avoid out of memory/localstorage issues in browsers. Does not affect
# exported CSVs
DISPLAY_MAX_ROW = 10000
# Default row limit for SQL Lab queries. Is overridden by setting a new limit in
# the SQL Lab UI
DEFAULT_SQLLAB_LIMIT = 1000
# Adds a warning message on sqllab save query and schedule query modals.
SQLLAB_SAVE_WARNING_MESSAGE = None
SQLLAB_SCHEDULE_WARNING_MESSAGE = None
# Force refresh while auto-refresh in dashboard
DASHBOARD_AUTO_REFRESH_MODE: Literal["fetch", "force"] = "force"
# Dashboard auto refresh intervals
DASHBOARD_AUTO_REFRESH_INTERVALS = [
[0, "Don't refresh"],
[10, "10 seconds"],
[30, "30 seconds"],
[60, "1 minute"],
[300, "5 minutes"],
[1800, "30 minutes"],
[3600, "1 hour"],
[21600, "6 hours"],
[43200, "12 hours"],
[86400, "24 hours"],
]
# Default celery config is to use SQLA as a broker, in a production setting
# you'll want to use a proper broker as specified here:
# http://docs.celeryproject.org/en/latest/getting-started/brokers/index.html
class CeleryConfig: # pylint: disable=too-few-public-methods
broker_url = "sqla+sqlite:///celerydb.sqlite"
imports = ("superset.sql_lab",)
result_backend = "db+sqlite:///celery_results.sqlite"
worker_prefetch_multiplier = 1
task_acks_late = False
task_annotations = {
"sql_lab.get_sql_results": {"rate_limit": "100/s"},
"email_reports.send": {
"rate_limit": "1/s",
"time_limit": int(timedelta(seconds=120).total_seconds()),
"soft_time_limit": int(timedelta(seconds=150).total_seconds()),
"ignore_result": True,
},
}
beat_schedule = {
"email_reports.schedule_hourly": {
"task": "email_reports.schedule_hourly",
"schedule": crontab(minute=1, hour="*"),
},
"reports.scheduler": {
"task": "reports.scheduler",
"schedule": crontab(minute="*", hour="*"),
},
"reports.prune_log": {
"task": "reports.prune_log",
"schedule": crontab(minute=0, hour=0),
},
}
CELERY_CONFIG = CeleryConfig # pylint: disable=invalid-name
# Set celery config to None to disable all the above configuration
# CELERY_CONFIG = None
# Additional static HTTP headers to be served by your Superset server. Note
# Flask-Talisman applies the relevant security HTTP headers.
#
# DEFAULT_HTTP_HEADERS: sets default values for HTTP headers. These may be overridden
# within the app
# OVERRIDE_HTTP_HEADERS: sets override values for HTTP headers. These values will
# override anything set within the app
DEFAULT_HTTP_HEADERS: Dict[str, Any] = {}
OVERRIDE_HTTP_HEADERS: Dict[str, Any] = {}
HTTP_HEADERS: Dict[str, Any] = {}
# The db id here results in selecting this one as a default in SQL Lab
DEFAULT_DB_ID = None
# Timeout duration for SQL Lab synchronous queries
SQLLAB_TIMEOUT = int(timedelta(seconds=30).total_seconds())
# Timeout duration for SQL Lab query validation
SQLLAB_VALIDATION_TIMEOUT = int(timedelta(seconds=10).total_seconds())
# SQLLAB_DEFAULT_DBID
SQLLAB_DEFAULT_DBID = None
# The MAX duration a query can run for before being killed by celery.
SQLLAB_ASYNC_TIME_LIMIT_SEC = int(timedelta(hours=6).total_seconds())
# Some databases support running EXPLAIN queries that allow users to estimate
# query costs before they run. These EXPLAIN queries should have a small
# timeout.
SQLLAB_QUERY_COST_ESTIMATE_TIMEOUT = int(timedelta(seconds=10).total_seconds())
# The cost returned by the databases is a relative value; in order to map the cost to
# a tangible value you need to define a custom formatter that takes into consideration
# your specific infrastructure. For example, you could analyze queries a posteriori by
# running EXPLAIN on them, and compute a histogram of relative costs to present the
# cost as a percentile, this step is optional as every db engine spec has its own
# query cost formatter, but it you wanna customize it you can define it inside the config:
# def postgres_query_cost_formatter(
# result: List[Dict[str, Any]]
# ) -> List[Dict[str, str]]:
# # 25, 50, 75% percentiles
# percentile_costs = [100.0, 1000.0, 10000.0]
#
# out = []
# for row in result:
# relative_cost = row["Total cost"]
# percentile = bisect.bisect_left(percentile_costs, relative_cost) + 1
# out.append({
# "Relative cost": relative_cost,
# "Percentile": str(percentile * 25) + "%",
# })
#
# return out
#
# QUERY_COST_FORMATTERS_BY_ENGINE: {"postgresql": postgres_query_cost_formatter}
QUERY_COST_FORMATTERS_BY_ENGINE: Dict[
str, Callable[[List[Dict[str, Any]]], List[Dict[str, Any]]]
] = {}
# Flag that controls if limit should be enforced on the CTA (create table as queries).
SQLLAB_CTAS_NO_LIMIT = False
# This allows you to define custom logic around the "CREATE TABLE AS" or CTAS feature
# in SQL Lab that defines where the target schema should be for a given user.
# Database `CTAS Schema` has a precedence over this setting.
# Example below returns a username and CTA queries will write tables into the schema
# name `username`
# SQLLAB_CTAS_SCHEMA_NAME_FUNC = lambda database, user, schema, sql: user.username
# This is move involved example where depending on the database you can leverage data
# available to assign schema for the CTA query:
# def compute_schema_name(database: Database, user: User, schema: str, sql: str) -> str:
# if database.name == 'mysql_payments_slave':
# return 'tmp_superset_schema'
# if database.name == 'presto_gold':
# return user.username
# if database.name == 'analytics':
# if 'analytics' in [r.name for r in user.roles]:
# return 'analytics_cta'
# else:
# return f'tmp_{schema}'
# Function accepts database object, user object, schema name and sql that will be run.
SQLLAB_CTAS_SCHEMA_NAME_FUNC: Optional[
Callable[[Database, models.User, str, str], str]
] = None