Update UPDATING.md

Co-authored-by: Evan Rusackas <evan@preset.io>

.rat-excludes

@@ -55,6 +55,7 @@ esm/*
 tsconfig.tsbuildinfo
 .*ipynb
 .*yml
+.*yaml
 .*iml
 .esprintrc
 .prettierignore

UPDATING.md

@@ -23,6 +23,8 @@ This file documents any backwards-incompatible changes in Superset and
 assists people when migrating to a new version.
 
 ## Next
+- [34346](https://github.com/apache/superset/pull/34346) The examples system has been migrated from Python-based scripts to YAML configuration files. The CLI command `superset load-examples` has been deprecated in favor of `superset examples load`. The old command still works but will show a deprecation warning. Additional example management commands are available under `superset examples` including `clear-old` and `reload`. If you have old Python-based examples loaded, the new YAML-based examples will not load automatically to preserve your existing data. To migrate to the new examples, run `superset examples clear-old --confirm` followed by `superset examples load`.
+  **Note**: This change affects Cypress tests that rely on specific chart names from the old examples (e.g., "Num Births Trend", "Daily Totals"). These charts may not exist in the new YAML examples, causing test failures. Consider updating your Cypress tests or creating test-specific fixtures.
 - [33084](https://github.com/apache/superset/pull/33084) The DISALLOWED_SQL_FUNCTIONS configuration now includes additional potentially sensitive database functions across PostgreSQL, MySQL, SQLite, MS SQL Server, and ClickHouse. Existing queries using these functions may now be blocked. Review your SQL Lab queries and dashboards if you encounter "disallowed function" errors after upgrading
 - [34235](https://github.com/apache/superset/pull/34235) CSV exports now use `utf-8-sig` encoding by default to include a UTF-8 BOM, improving compatibility with Excel.
 - [34258](https://github.com/apache/superset/pull/34258) changing the default in Dockerfile to INCLUDE_CHROMIUM="false" (from "true") in the past. This ensures the `lean` layer is lean by default, and people can opt-in to the `chromium` layer by setting the build arg `INCLUDE_CHROMIUM=true`. This is a breaking change for anyone using the `lean` layer, as it will no longer include Chromium by default.

docker-compose-image-tag.yml

@@ -20,9 +20,6 @@
 # If you choose to use this type of deployment make sure to
 # create you own docker environment file (docker/.env) with your own
 # unique random secure passwords and SECRET_KEY.
-#
-# For verbose logging during development:
-# - Set SUPERSET_LOG_LEVEL=debug in docker/.env-local for detailed Superset logs
 # -----------------------------------------------------------------------
 x-superset-image: &superset-image apachesuperset.docker.scarf.sh/apache/superset:${TAG:-latest-dev}
 x-superset-volumes:

docker-compose-non-dev.yml

@@ -20,9 +20,6 @@
 # If you choose to use this type of deployment make sure to
 # create you own docker environment file (docker/.env) with your own
 # unique random secure passwords and SECRET_KEY.
-#
-# For verbose logging during development:
-# - Set SUPERSET_LOG_LEVEL=debug in docker/.env-local for detailed Superset logs
 # -----------------------------------------------------------------------
 x-superset-volumes:
   &superset-volumes # /app/pythonpath_docker will be appended to the PYTHONPATH in the final container

docker-compose.yml

@@ -20,9 +20,6 @@
 # If you choose to use this type of deployment make sure to
 # create you own docker environment file (docker/.env) with your own
 # unique random secure passwords and SECRET_KEY.
-#
-# For verbose logging during development:
-# - Set SUPERSET_LOG_LEVEL=debug in docker/.env-local for detailed Superset logs
 # -----------------------------------------------------------------------
 x-superset-user: &superset-user root
 x-superset-volumes: &superset-volumes

docker/.env

@@ -53,12 +53,7 @@ PYTHONPATH=/app/pythonpath:/app/docker/pythonpath_dev
 REDIS_HOST=redis
 REDIS_PORT=6379
 
-# Development and logging configuration
-# FLASK_DEBUG: Enables Flask dev features (auto-reload, better error pages) - keep 'true' for development
 FLASK_DEBUG=true
-# SUPERSET_LOG_LEVEL: Controls Superset application logging verbosity (debug, info, warning, error, critical)
-SUPERSET_LOG_LEVEL=info
-
 SUPERSET_APP_ROOT="/"
 SUPERSET_ENV=development
 SUPERSET_LOAD_EXAMPLES=yes
@@ -71,3 +66,4 @@ SUPERSET_SECRET_KEY=TEST_NON_DEV_SECRET
 ENABLE_PLAYWRIGHT=false
 PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true
 BUILD_SUPERSET_FRONTEND_IN_DOCKER=true
+SUPERSET_LOG_LEVEL=info

docker/pythonpath_dev/.gitignore

@@ -20,5 +20,4 @@
 # DON'T ignore the .gitignore
 !.gitignore
 !superset_config.py
-!superset_config_docker_light.py
 !superset_config_local.example

docker/pythonpath_dev/superset_config.py

@@ -14,6 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+# mypy: disable-error-code="assignment,misc"
 #
 # This file is included in the final Docker image and SHOULD be overridden when
 # deploying the image to prod. Settings configured here are intended for use in local

docs/docs/contributing/development.mdx

@@ -348,7 +348,7 @@ superset init
 
 # Load some data to play with.
 # Note: you MUST have previously created an admin user with the username `admin` for this command to work.
-superset load-examples
+superset examples load
 
 # Start the Flask dev web server from inside your virtualenv.
 # Note that your page may not have CSS at this point.

docs/docs/installation/docker-compose.mdx

@@ -26,14 +26,11 @@ Superset locally is using Docker Compose on a Linux or Mac OSX
 computer. Superset does not have official support for Windows. It's also the easiest
 way to launch a fully functioning **development environment** quickly.
 
-Note that there are 4 major ways we support to run `docker compose`:
+Note that there are 3 major ways we support to run `docker compose`:
 
 1. **docker-compose.yml:** for interactive development, where we mount your local folder with the
   frontend/backend files that you can edit and experience the changes you
   make in the app in real time
-1. **docker-compose-light.yml:** a lightweight configuration with minimal services (database,
-  Superset app, and frontend dev server) for development. Uses in-memory caching instead of Redis
-  and is designed for running multiple instances simultaneously
 1. **docker-compose-non-dev.yml** where we just build a more immutable image based on the
   local branch and get all the required images running. Changes in the local branch
   at the time you fire this up will be reflected, but changes to the code
@@ -47,7 +44,7 @@ Note that there are 4 major ways we support to run `docker compose`:
   The `dev` builds include the `psycopg2-binary` required to connect
   to the Postgres database launched as part of the `docker compose` builds.
 
-More on these approaches after setting up the requirements for either.
+More on these two approaches after setting up the requirements for either.
 
 ## Requirements
 
@@ -106,36 +103,13 @@ and help you start fresh. In the context of `docker compose` setting
 from within docker. This will slow down the startup, but will fix various npm-related issues.
 :::
 
-### Option #2 - lightweight development with multiple instances
-
-For a lighter development setup that uses fewer resources and supports running multiple instances:
-
-```bash
-# Single lightweight instance (default port 9001)
-docker compose -f docker-compose-light.yml up
-
-# Multiple instances with different ports
-NODE_PORT=9001 docker compose -p superset-1 -f docker-compose-light.yml up
-NODE_PORT=9002 docker compose -p superset-2 -f docker-compose-light.yml up
-NODE_PORT=9003 docker compose -p superset-3 -f docker-compose-light.yml up
-```
-
-This configuration includes:
-- PostgreSQL database (internal network only)
-- Superset application server
-- Frontend development server with webpack hot reloading
-- In-memory caching (no Redis)
-- Isolated volumes and networks per instance
-
-Access each instance at `http://localhost:{NODE_PORT}` (e.g., `http://localhost:9001`).
-
-### Option #3 - build a set of immutable images from the local branch
+### Option #2 - build a set of immutable images from the local branch
 
 ```bash
 docker compose -f docker-compose-non-dev.yml up
 ```
 
-### Option #4 - boot up an official release
+### Option #3 - boot up an official release
 
 ```bash
 # Set the version you want to run

docs/docs/installation/pypi.mdx

@@ -151,7 +151,7 @@ Finish installing by running through the following commands:
 superset fab create-admin
 
 # Load some data to play with
-superset load_examples
+superset examples load
 
 # Create default roles and permissions
 superset init

scripts/python_tests.sh

@@ -33,4 +33,4 @@ superset load-test-users
 
 echo "Running tests"
 
-pytest --durations-min=2 --maxfail=1 --cov-report= --cov=superset ./tests/integration_tests "$@"
+pytest --durations-min=2 --cov-report= --cov=superset ./tests/integration_tests "$@"

superset/cli/examples.py

@@ -30,6 +30,7 @@ def load_examples_run(
     load_big_data: bool = False,
     only_metadata: bool = False,
     force: bool = False,
+    cleanup: bool = False,
 ) -> None:
     if only_metadata:
         logger.info("Loading examples metadata")
@@ -40,51 +41,41 @@ def load_examples_run(
     # pylint: disable=import-outside-toplevel
     import superset.examples.data_loading as examples
 
+    # Clear old examples if requested
+    if cleanup:
+        clear_old_examples()
+
     examples.load_css_templates()
 
     if load_test_data:
+        # Import test fixtures from tests directory
+        from tests.fixtures.examples.energy import load_energy
+        from tests.fixtures.examples.supported_charts_dashboard import (
+            load_supported_charts_dashboard,
+        )
+        from tests.fixtures.examples.tabbed_dashboard import load_tabbed_dashboard
+
         logger.info("Loading energy related dataset")
-        examples.load_energy(only_metadata, force)
+        load_energy(only_metadata, force)
 
-    logger.info("Loading [World Bank's Health Nutrition and Population Stats]")
-    examples.load_world_bank_health_n_pop(only_metadata, force)
-
-    logger.info("Loading [Birth names]")
-    examples.load_birth_names(only_metadata, force)
-
-    if load_test_data:
         logger.info("Loading [Tabbed dashboard]")
-        examples.load_tabbed_dashboard(only_metadata)
+        load_tabbed_dashboard(only_metadata)
 
         logger.info("Loading [Supported Charts Dashboard]")
-        examples.load_supported_charts_dashboard()
+        load_supported_charts_dashboard()
     else:
-        logger.info("Loading [Random long/lat data]")
-        examples.load_long_lat_data(only_metadata, force)
-
         logger.info("Loading [Country Map data]")
         examples.load_country_map_data(only_metadata, force)
 
-        logger.info("Loading [San Francisco population polygons]")
-        examples.load_sf_population_polygons(only_metadata, force)
-
-        logger.info("Loading [Flights data]")
-        examples.load_flights(only_metadata, force)
-
-        logger.info("Loading [BART lines]")
-        examples.load_bart_lines(only_metadata, force)
-
-        logger.info("Loading [Misc Charts] dashboard")
-        examples.load_misc_dashboard()
-
-        logger.info("Loading DECK.gl demo")
-        examples.load_deck_dash()
-
     if load_big_data:
+        # Import test fixture from tests directory
+        from tests.fixtures.examples.big_data import load_big_data as load_big_data_func
+
         logger.info("Loading big synthetic data for tests")
-        examples.load_big_data()
+        load_big_data_func()
 
     # load examples that are stored as YAML config files
+    logger.info("Loading examples from YAML configuration files")
     examples.load_examples_from_configs(force, load_test_data)
 
 
@@ -112,4 +103,222 @@ def load_examples(
     force: bool = False,
 ) -> None:
     """Loads a set of Slices and Dashboards and a supporting dataset"""
+    # Show deprecation warning
+    click.echo(
+        click.style(
+            "\nWARNING: 'superset load-examples' is deprecated. "
+            "Please use 'superset examples load' instead.\n",
+            fg="yellow",
+        ),
+        err=True,
+    )
+
     load_examples_run(load_test_data, load_big_data, only_metadata, force)
+
+
+# New CLI structure
+@click.group(name="examples", help="Manage example data")
+def examples_cli() -> None:
+    """Group for example-related commands."""
+    pass
+
+
+@examples_cli.command(name="load", help="Load example data into the database")
+@with_appcontext
+@transaction()
+@click.option("--load-test-data", "-t", is_flag=True, help="Load additional test data")
+@click.option("--load-big-data", "-b", is_flag=True, help="Load additional big data")
+@click.option(
+    "--only-metadata",
+    "-m",
+    is_flag=True,
+    help="Only load metadata, skip actual data",
+)
+@click.option(
+    "--force",
+    "-f",
+    is_flag=True,
+    help="Force load data even if table already exists",
+)
+def load(
+    load_test_data: bool = False,
+    load_big_data: bool = False,
+    only_metadata: bool = False,
+    force: bool = False,
+) -> None:
+    """Load example datasets, charts, and dashboards."""
+    load_examples_run(
+        load_test_data, load_big_data, only_metadata, force, cleanup=False
+    )
+
+
+def clear_old_examples() -> bool:
+    """
+    Clear old Python-generated examples.
+    Returns True if clear was performed, False otherwise.
+    """
+    from superset import db
+    from superset.connectors.sqla.models import SqlaTable
+    from superset.examples.utils import _has_old_examples
+    from superset.models.core import Database
+    from superset.models.dashboard import Dashboard, dashboard_slices
+    from superset.models.slice import Slice
+
+    # Check if old examples exist
+    if not _has_old_examples():
+        logger.info("No old examples found to clear")
+        return False
+
+    # Find the examples database
+    examples_db = db.session.query(Database).filter_by(database_name="examples").first()
+
+    if not examples_db:
+        return False
+
+    logger.info("Found examples database (id=%s)", examples_db.id)
+    logger.info("Clearing old examples...")
+
+    # 1. Get all datasets from examples database
+    example_datasets = (
+        db.session.query(SqlaTable).filter_by(database_id=examples_db.id).all()
+    )
+    dataset_ids = [ds.id for ds in example_datasets]
+    logger.info("Found %d example datasets", len(example_datasets))
+
+    # 2. Find all charts using these datasets
+    example_charts = []
+    if dataset_ids:
+        example_charts = (
+            db.session.query(Slice)
+            .filter(
+                Slice.datasource_id.in_(dataset_ids),
+                Slice.datasource_type == "table",
+            )
+            .all()
+        )
+        logger.info("Found %d example charts", len(example_charts))
+
+    chart_ids = [chart.id for chart in example_charts]
+
+    # 3. Find dashboards that contain these charts
+    example_dashboards = []
+    if chart_ids:
+        # Get dashboards that have relationships with our example charts
+        example_dashboards = (
+            db.session.query(Dashboard)
+            .join(dashboard_slices)
+            .filter(dashboard_slices.c.slice_id.in_(chart_ids))
+            .distinct()
+            .all()
+        )
+        logger.info("Found %d example dashboards", len(example_dashboards))
+
+        # Remove dashboard-slice relationships first
+        db.session.execute(
+            dashboard_slices.delete().where(dashboard_slices.c.slice_id.in_(chart_ids))
+        )
+        logger.info(
+            "Removed dashboard-slice relationships for %d charts",
+            len(chart_ids),
+        )
+
+    # 4. Delete dashboards that are now empty (contain only example charts)
+    for dashboard in example_dashboards:
+        # Since we already deleted the relationships, check if dashboard is empty
+        remaining_charts = (
+            db.session.query(dashboard_slices.c.slice_id)
+            .filter(dashboard_slices.c.dashboard_id == dashboard.id)
+            .count()
+        )
+
+        if remaining_charts == 0:
+            db.session.delete(dashboard)
+            logger.info(
+                "Deleted dashboard: %s (slug: %s)",
+                dashboard.dashboard_title,
+                dashboard.slug,
+            )
+        else:
+            logger.info(
+                "Keeping dashboard %s as it contains non-example charts",
+                dashboard.dashboard_title,
+            )
+
+    # 5. Delete charts
+    for chart in example_charts:
+        db.session.delete(chart)
+    logger.info("Deleted %d example charts", len(example_charts))
+
+    # 6. Delete the database - this will cascade delete all datasets,
+    # columns, and metrics thanks to the cascade="all, delete-orphan"
+    db.session.delete(examples_db)
+
+    logger.info("Examples database and all related objects removed successfully")
+    return True
+
+
+@examples_cli.command(name="clear-old", help="Clear old Python-based example data")
+@with_appcontext
+@transaction()
+@click.option(
+    "--confirm",
+    is_flag=True,
+    help="Skip confirmation prompt",
+)
+def clear_old(confirm: bool = False) -> None:
+    """Clear old Python-generated example datasets, charts, and dashboards."""
+    if not confirm:
+        click.confirm(
+            "This will delete old Python-based example data. Are you sure?",
+            abort=True,
+        )
+
+    try:
+        if clear_old_examples():
+            logger.info("Old examples cleared successfully")
+        else:
+            logger.info("No old examples found to clear")
+    except Exception as e:
+        logger.error(f"Failed to clear old examples: {e}")
+        raise
+
+
+@examples_cli.command(name="clear", help="Clear all example data (NOT YET IMPLEMENTED)")
+@with_appcontext
+def clear() -> None:
+    """Clear all example data including YAML-based examples."""
+    click.echo(
+        click.style(
+            "Clearing YAML-based examples is NOT YET IMPLEMENTED.\n"
+            "Use 'superset examples clear-old' to remove old Python-based examples.",
+            fg="yellow",
+        )
+    )
+
+
+@examples_cli.command(name="reload", help="Clear and reload example data")
+@with_appcontext
+@transaction()
+@click.option("--load-test-data", "-t", is_flag=True, help="Load additional test data")
+@click.option("--load-big-data", "-b", is_flag=True, help="Load additional big data")
+@click.option(
+    "--only-metadata",
+    "-m",
+    is_flag=True,
+    help="Only load metadata, skip actual data",
+)
+@click.option(
+    "--force",
+    "-f",
+    is_flag=True,
+    help="Force load data even if table already exists",
+)
+def reload(
+    load_test_data: bool = False,
+    load_big_data: bool = False,
+    only_metadata: bool = False,
+    force: bool = False,
+) -> None:
+    """Clear existing examples and load fresh ones."""
+    # This is essentially the old --cleanup behavior
+    load_examples_run(load_test_data, load_big_data, only_metadata, force, cleanup=True)

superset/commands/chart/importers/v1/utils.py

@@ -16,6 +16,7 @@
 # under the License.
 
 import copy
+import logging
 from inspect import isclass
 from typing import Any
 
@@ -27,6 +28,8 @@ from superset.models.slice import Slice
 from superset.utils import json
 from superset.utils.core import AnnotationType, get_user
 
+logger = logging.getLogger(__name__)
+
 
 def filter_chart_annotations(chart_config: dict[str, Any]) -> None:
     """
@@ -63,10 +66,13 @@ def import_chart(
         if not overwrite or not can_write:
             return existing
         config["id"] = existing.id
+        logger.info(f"Updating existing chart: {config.get('slice_name')}")
     elif not can_write:
         raise ImportFailedError(
             "Chart doesn't exist and user doesn't have permission to create charts"
         )
+    else:
+        logger.info(f"Creating new chart: {config.get('slice_name')}")
 
     filter_chart_annotations(config)
 

superset/commands/dashboard/export.py

@@ -123,6 +123,9 @@ class ExportDashboardsCommand(ExportModelsCommand):
             include_defaults=True,
             export_uuids=True,
         )
+        # Remove theme_id from export to make dashboards theme-free
+        payload.pop("theme_id", None)
+
         # TODO (betodealmeida): move this logic to export_to_dict once this
         #  becomes the default export endpoint
         for key, new_name in JSON_KEYS.items():

superset/commands/dashboard/importers/v1/utils.py

@@ -166,11 +166,14 @@ def import_dashboard(  # noqa: C901
         elif not overwrite or not can_write:
             return existing
         config["id"] = existing.id
+        logger.info(f"Updating existing dashboard: {config.get('dashboard_title')}")
     elif not can_write:
         raise ImportFailedError(
             "Dashboard doesn't exist and user doesn't "
             "have permission to create dashboards"
         )
+    else:
+        logger.info(f"Creating new dashboard: {config.get('dashboard_title')}")
 
     # TODO (betodealmeida): move this logic to import_from_dict
     config = config.copy()

superset/commands/database/importers/v1/utils.py

@@ -46,10 +46,13 @@ def import_database(
         if not overwrite or not can_write:
             return existing
         config["id"] = existing.id
+        logger.info(f"Updating existing database: {config.get('database_name')}")
     elif not can_write:
         raise ImportFailedError(
             "Database doesn't exist and user doesn't have permission to create databases"  # noqa: E501
         )
+    else:
+        logger.info(f"Creating new database: {config.get('database_name')}")
     # Check if this URI is allowed
     if app.config["PREVENT_UNSAFE_DB_CONNECTIONS"]:
         try:

superset/commands/dataset/importers/v1/utils.py

@@ -124,11 +124,13 @@ def import_dataset(  # noqa: C901
         if not overwrite or not can_write:
             return existing
         config["id"] = existing.id
-
+        logger.info(f"Updating existing dataset: {config.get('table_name')}")
     elif not can_write:
         raise ImportFailedError(
             "Dataset doesn't exist and user doesn't have permission to create datasets"
         )
+    else:
+        logger.info(f"Creating new dataset: {config.get('table_name')}")
 
     # TODO (betodealmeida): move this logic to import_from_dict
     config = config.copy()
@@ -209,7 +211,12 @@ def load_data(data_uri: str, dataset: SqlaTable, database: Database) -> None:
     data = request.urlopen(data_uri)  # pylint: disable=consider-using-with  # noqa: S310
     if data_uri.endswith(".gz"):
         data = gzip.open(data)
-    df = pd.read_csv(data, encoding="utf-8")
+
+    # Determine file format based on URI
+    if ".json" in data_uri:
+        df = pd.read_json(data, encoding="utf-8")
+    else:
+        df = pd.read_csv(data, encoding="utf-8")
     dtype = get_dtype(df, dataset)
 
     # convert temporal columns

superset/commands/importers/v1/examples.py

@@ -195,4 +195,5 @@ class ImportExamplesCommand(ImportModelsCommand):
             {"dashboard_id": dashboard_id, "slice_id": chart_id}
             for (dashboard_id, chart_id) in dashboard_chart_ids
         ]
-        db.session.execute(dashboard_slices.insert(), values)
+        if values:
+            db.session.execute(dashboard_slices.insert(), values)

superset/config.py

@@ -1155,7 +1155,7 @@ class CeleryConfig:  # pylint: disable=too-few-public-methods
     }
 
 
-CELERY_CONFIG: type[CeleryConfig] | None = CeleryConfig
+CELERY_CONFIG: type[CeleryConfig] = CeleryConfig
 
 # Set celery config to None to disable all the above configuration
 # CELERY_CONFIG = None

superset/examples/bart_lines.py

@@ -1,71 +0,0 @@
-# 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.
-import logging
-
-import polyline
-from sqlalchemy import inspect, String, Text
-
-from superset import db
-from superset.sql.parse import Table
-from superset.utils import json
-
-from ..utils.database import get_example_database  # noqa: TID252
-from .helpers import get_table_connector_registry, read_example_data
-
-logger = logging.getLogger(__name__)
-
-
-def load_bart_lines(only_metadata: bool = False, force: bool = False) -> None:
-    tbl_name = "bart_lines"
-    database = get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            df = read_example_data(
-                "examples://bart-lines.json.gz", encoding="latin-1", compression="gzip"
-            )
-            df["path_json"] = df.path.map(json.dumps)
-            df["polyline"] = df.path.map(polyline.encode)
-            del df["path"]
-
-            df.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={
-                    "color": String(255),
-                    "name": String(255),
-                    "polyline": Text,
-                    "path_json": Text,
-                },
-                index=False,
-            )
-
-    logger.debug(f"Creating table {tbl_name} reference")
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = table(table_name=tbl_name, schema=schema)
-        db.session.add(tbl)
-    tbl.description = "BART lines"
-    tbl.database = database
-    tbl.filter_select_enabled = True
-    tbl.fetch_metadata()

superset/examples/birth_names.py

@@ -1,869 +0,0 @@
-# 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.
-import logging
-import textwrap
-from typing import Union
-
-import pandas as pd
-from sqlalchemy import DateTime, inspect, String
-from sqlalchemy.sql import column
-
-from superset import app, db, security_manager
-from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn
-from superset.models.core import Database
-from superset.models.dashboard import Dashboard
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils import json
-from superset.utils.core import DatasourceType
-
-from ..utils.database import get_example_database  # noqa: TID252
-from .helpers import (
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    misc_dash_slices,
-    read_example_data,
-    update_slice_ids,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def gen_filter(
-    subject: str, comparator: str, operator: str = "=="
-) -> dict[str, Union[bool, str]]:
-    return {
-        "clause": "WHERE",
-        "comparator": comparator,
-        "expressionType": "SIMPLE",
-        "operator": operator,
-        "subject": subject,
-    }
-
-
-def load_data(tbl_name: str, database: Database, sample: bool = False) -> None:
-    pdf = read_example_data("examples://birth_names2.json.gz", compression="gzip")
-
-    # TODO(bkyryliuk): move load examples data into the pytest fixture
-    if database.backend == "presto":
-        pdf.ds = pd.to_datetime(pdf.ds, unit="ms")
-        pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d %H:%M%:%S")
-    else:
-        pdf.ds = pd.to_datetime(pdf.ds, unit="ms")
-    pdf = pdf.head(100) if sample else pdf
-
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-
-        pdf.to_sql(
-            tbl_name,
-            engine,
-            schema=schema,
-            if_exists="replace",
-            chunksize=500,
-            dtype={
-                # TODO(bkyryliuk): use TIMESTAMP type for presto
-                "ds": DateTime if database.backend != "presto" else String(255),
-                "gender": String(16),
-                "state": String(10),
-                "name": String(255),
-            },
-            method="multi",
-            index=False,
-        )
-    logger.debug("Done loading table!")
-    logger.debug("-" * 80)
-
-
-def load_birth_names(
-    only_metadata: bool = False, force: bool = False, sample: bool = False
-) -> None:
-    """Loading birth name dataset from a zip file in the repo"""
-    database = get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-
-    tbl_name = "birth_names"
-    table_exists = database.has_table(Table(tbl_name, schema))
-
-    if not only_metadata and (not table_exists or force):
-        load_data(tbl_name, database, sample=sample)
-
-    table = get_table_connector_registry()
-    obj = db.session.query(table).filter_by(table_name=tbl_name, schema=schema).first()
-    if not obj:
-        logger.debug(f"Creating table [{tbl_name}] reference")
-        obj = table(table_name=tbl_name, schema=schema)
-        db.session.add(obj)
-
-    _set_table_metadata(obj, database)
-    _add_table_metrics(obj)
-
-    slices, _ = create_slices(obj)
-    create_dashboard(slices)
-
-
-def _set_table_metadata(datasource: SqlaTable, database: "Database") -> None:
-    datasource.main_dttm_col = "ds"
-    datasource.database = database
-    datasource.filter_select_enabled = True
-    datasource.fetch_metadata()
-
-
-def _add_table_metrics(datasource: SqlaTable) -> None:
-    # By accessing the attribute first, we make sure `datasource.columns` and
-    # `datasource.metrics` are already loaded. Otherwise accessing them later
-    # may trigger an unnecessary and unexpected `after_update` event.
-    columns, metrics = datasource.columns, datasource.metrics
-
-    if not any(col.column_name == "num_california" for col in columns):
-        col_state = str(column("state").compile(db.engine))
-        col_num = str(column("num").compile(db.engine))
-        columns.append(
-            TableColumn(
-                column_name="num_california",
-                expression=f"CASE WHEN {col_state} = 'CA' THEN {col_num} ELSE 0 END",
-            )
-        )
-
-    if not any(col.metric_name == "sum__num" for col in metrics):
-        col = str(column("num").compile(db.engine))
-        metrics.append(SqlMetric(metric_name="sum__num", expression=f"SUM({col})"))
-
-    for col in columns:
-        if col.column_name == "ds":  # type: ignore
-            col.is_dttm = True  # type: ignore
-            break
-
-    datasource.columns = columns
-    datasource.metrics = metrics
-
-
-def create_slices(tbl: SqlaTable) -> tuple[list[Slice], list[Slice]]:
-    owner = security_manager.get_user_by_id(1)
-    metrics = [
-        {
-            "expressionType": "SIMPLE",
-            "column": {"column_name": "num", "type": "BIGINT"},
-            "aggregate": "SUM",
-            "label": "Births",
-            "optionName": "metric_11",
-        }
-    ]
-    metric = "sum__num"
-
-    defaults = {
-        "compare_lag": "10",
-        "compare_suffix": "o10Y",
-        "limit": "25",
-        "granularity_sqla": "ds",
-        "groupby": [],
-        "row_limit": app.config["ROW_LIMIT"],
-        "time_range": "100 years ago : now",
-        "viz_type": "table",
-        "markup_type": "markdown",
-    }
-
-    default_query_context = {
-        "result_format": "json",
-        "result_type": "full",
-        "datasource": {
-            "id": tbl.id,
-            "type": "table",
-        },
-        "queries": [
-            {
-                "columns": [],
-                "metrics": [],
-            },
-        ],
-    }
-
-    slice_kwargs = {
-        "datasource_id": tbl.id,
-        "datasource_type": DatasourceType.TABLE,
-    }
-
-    logger.debug("Creating some slices")
-    slices = [
-        Slice(
-            **slice_kwargs,
-            slice_name="Participants",
-            viz_type="big_number",
-            params=get_slice_json(
-                defaults,
-                viz_type="big_number",
-                granularity_sqla="ds",
-                compare_lag="5",
-                compare_suffix="over 5Y",
-                metric=metric,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Genders",
-            viz_type="pie",
-            params=get_slice_json(
-                defaults, viz_type="pie", groupby=["gender"], metric=metric
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Trends",
-            viz_type="echarts_timeseries_line",
-            params=get_slice_json(
-                defaults,
-                viz_type="echarts_timeseries_line",
-                groupby=["name"],
-                granularity_sqla="ds",
-                rich_tooltip=True,
-                show_legend=True,
-                metrics=metrics,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Genders by State",
-            viz_type="echarts_timeseries_bar",
-            params=get_slice_json(
-                defaults,
-                adhoc_filters=[
-                    {
-                        "clause": "WHERE",
-                        "expressionType": "SIMPLE",
-                        "filterOptionName": "2745eae5",
-                        "comparator": ["other"],
-                        "operator": "NOT IN",
-                        "subject": "state",
-                    }
-                ],
-                viz_type="echarts_timeseries_bar",
-                metrics=[
-                    {
-                        "expressionType": "SIMPLE",
-                        "column": {"column_name": "num_boys", "type": "BIGINT(20)"},
-                        "aggregate": "SUM",
-                        "label": "Boys",
-                        "optionName": "metric_11",
-                    },
-                    {
-                        "expressionType": "SIMPLE",
-                        "column": {"column_name": "num_girls", "type": "BIGINT(20)"},
-                        "aggregate": "SUM",
-                        "label": "Girls",
-                        "optionName": "metric_12",
-                    },
-                ],
-                groupby=["state"],
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Girls",
-            viz_type="table",
-            params=get_slice_json(
-                defaults,
-                groupby=["name"],
-                adhoc_filters=[gen_filter("gender", "girl")],
-                row_limit=50,
-                timeseries_limit_metric=metric,
-                metrics=[metric],
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Girl Name Cloud",
-            viz_type="word_cloud",
-            params=get_slice_json(
-                defaults,
-                viz_type="word_cloud",
-                size_from="10",
-                series="name",
-                size_to="70",
-                rotation="square",
-                limit="100",
-                adhoc_filters=[gen_filter("gender", "girl")],
-                metric=metric,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Boys",
-            viz_type="table",
-            params=get_slice_json(
-                defaults,
-                groupby=["name"],
-                adhoc_filters=[gen_filter("gender", "boy")],
-                row_limit=50,
-                timeseries_limit_metric=metric,
-                metrics=[metric],
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Boy Name Cloud",
-            viz_type="word_cloud",
-            params=get_slice_json(
-                defaults,
-                viz_type="word_cloud",
-                size_from="10",
-                series="name",
-                size_to="70",
-                rotation="square",
-                limit="100",
-                adhoc_filters=[gen_filter("gender", "boy")],
-                metric=metric,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Top 10 Girl Name Share",
-            viz_type="echarts_area",
-            params=get_slice_json(
-                defaults,
-                adhoc_filters=[gen_filter("gender", "girl")],
-                comparison_type="values",
-                groupby=["name"],
-                limit=10,
-                stacked_style="expand",
-                time_grain_sqla="P1D",
-                viz_type="echarts_area",
-                x_axis_forma="smart_date",
-                metrics=metrics,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Top 10 Boy Name Share",
-            viz_type="echarts_area",
-            params=get_slice_json(
-                defaults,
-                adhoc_filters=[gen_filter("gender", "boy")],
-                comparison_type="values",
-                groupby=["name"],
-                limit=10,
-                stacked_style="expand",
-                time_grain_sqla="P1D",
-                viz_type="echarts_area",
-                x_axis_forma="smart_date",
-                metrics=metrics,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Pivot Table v2",
-            viz_type="pivot_table_v2",
-            params=get_slice_json(
-                defaults,
-                viz_type="pivot_table_v2",
-                groupbyRows=["name"],
-                groupbyColumns=["state"],
-                metrics=[metric],
-            ),
-            query_context=get_slice_json(
-                default_query_context,
-                queries=[
-                    {
-                        "columns": ["name", "state"],
-                        "metrics": [metric],
-                    }
-                ],
-            ),
-            owners=[],
-        ),
-    ]
-    misc_slices = [
-        Slice(
-            **slice_kwargs,
-            slice_name="Average and Sum Trends",
-            viz_type="mixed_timeseries",
-            params=get_slice_json(
-                defaults,
-                viz_type="mixed_timeseries",
-                metrics=[
-                    {
-                        "expressionType": "SIMPLE",
-                        "column": {"column_name": "num", "type": "BIGINT(20)"},
-                        "aggregate": "AVG",
-                        "label": "AVG(num)",
-                        "optionName": "metric_vgops097wej_g8uff99zhk7",
-                    }
-                ],
-                metrics_b=["sum__num"],
-                granularity_sqla="ds",
-                yAxisIndex=0,
-                yAxisIndexB=1,
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Num Births Trend",
-            viz_type="echarts_timeseries_line",
-            params=get_slice_json(
-                defaults, viz_type="echarts_timeseries_line", metrics=metrics
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Daily Totals",
-            viz_type="table",
-            params=get_slice_json(
-                defaults,
-                groupby=["ds"],
-                time_range="1983 : 2023",
-                viz_type="table",
-                metrics=metrics,
-            ),
-            query_context=get_slice_json(
-                default_query_context,
-                queries=[
-                    {
-                        "columns": ["ds"],
-                        "metrics": metrics,
-                        "time_range": "1983 : 2023",
-                    }
-                ],
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Number of California Births",
-            viz_type="big_number_total",
-            params=get_slice_json(
-                defaults,
-                metric={
-                    "expressionType": "SIMPLE",
-                    "column": {
-                        "column_name": "num_california",
-                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    "aggregate": "SUM",
-                    "label": "SUM(num_california)",
-                },
-                viz_type="big_number_total",
-                granularity_sqla="ds",
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Top 10 California Names Timeseries",
-            viz_type="echarts_timeseries_line",
-            params=get_slice_json(
-                defaults,
-                metrics=[
-                    {
-                        "expressionType": "SIMPLE",
-                        "column": {
-                            "column_name": "num_california",
-                            "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                        },
-                        "aggregate": "SUM",
-                        "label": "SUM(num_california)",
-                    }
-                ],
-                viz_type="echarts_timeseries_line",
-                granularity_sqla="ds",
-                groupby=["name"],
-                timeseries_limit_metric={
-                    "expressionType": "SIMPLE",
-                    "column": {
-                        "column_name": "num_california",
-                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    "aggregate": "SUM",
-                    "label": "SUM(num_california)",
-                },
-                limit="10",
-            ),
-            owners=[owner] if owner else [],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Names Sorted by Num in California",
-            viz_type="table",
-            params=get_slice_json(
-                defaults,
-                metrics=metrics,
-                groupby=["name"],
-                row_limit=50,
-                timeseries_limit_metric={
-                    "expressionType": "SIMPLE",
-                    "column": {
-                        "column_name": "num_california",
-                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    "aggregate": "SUM",
-                    "label": "SUM(num_california)",
-                },
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Number of Girls",
-            viz_type="big_number_total",
-            params=get_slice_json(
-                defaults,
-                metric=metric,
-                viz_type="big_number_total",
-                granularity_sqla="ds",
-                adhoc_filters=[gen_filter("gender", "girl")],
-                subheader="total female participants",
-            ),
-            owners=[],
-        ),
-        Slice(
-            **slice_kwargs,
-            slice_name="Pivot Table",
-            viz_type="pivot_table_v2",
-            params=get_slice_json(
-                defaults,
-                viz_type="pivot_table_v2",
-                groupbyRows=["name"],
-                groupbyColumns=["state"],
-                metrics=metrics,
-            ),
-            owners=[],
-        ),
-    ]
-    for slc in slices:
-        merge_slice(slc)
-
-    for slc in misc_slices:
-        merge_slice(slc)
-        misc_dash_slices.add(slc.slice_name)
-
-    return slices, misc_slices
-
-
-def create_dashboard(slices: list[Slice]) -> Dashboard:
-    logger.debug("Creating a dashboard")
-    dash = db.session.query(Dashboard).filter_by(slug="births").first()
-    if not dash:
-        dash = Dashboard()
-        db.session.add(dash)
-
-    dash.published = True
-    dash.json_metadata = textwrap.dedent(
-        """\
-    {
-        "label_colors": {
-            "Girls": "#FF69B4",
-            "Boys": "#ADD8E6",
-            "girl": "#FF69B4",
-            "boy": "#ADD8E6"
-        }
-    }"""
-    )
-    # pylint: disable=line-too-long
-    pos = json.loads(  # noqa: TID251
-        textwrap.dedent(
-            """\
-        {
-          "CHART-6GdlekVise": {
-            "children": [],
-            "id": "CHART-6GdlekVise",
-            "meta": {
-              "chartId": 5547,
-              "height": 50,
-              "sliceName": "Top 10 Girl Name Share",
-              "width": 5
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-eh0w37bWbR"
-            ],
-            "type": "CHART"
-          },
-          "CHART-6n9jxb30JG": {
-            "children": [],
-            "id": "CHART-6n9jxb30JG",
-            "meta": {
-              "chartId": 5540,
-              "height": 36,
-              "sliceName": "Genders by State",
-              "width": 5
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW--EyBZQlDi"
-            ],
-            "type": "CHART"
-          },
-          "CHART-Jj9qh1ol-N": {
-            "children": [],
-            "id": "CHART-Jj9qh1ol-N",
-            "meta": {
-              "chartId": 5545,
-              "height": 50,
-              "sliceName": "Boy Name Cloud",
-              "width": 4
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-kzWtcvo8R1"
-            ],
-            "type": "CHART"
-          },
-          "CHART-ODvantb_bF": {
-            "children": [],
-            "id": "CHART-ODvantb_bF",
-            "meta": {
-              "chartId": 5548,
-              "height": 50,
-              "sliceName": "Top 10 Boy Name Share",
-              "width": 5
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-kzWtcvo8R1"
-            ],
-            "type": "CHART"
-          },
-          "CHART-PAXUUqwmX9": {
-            "children": [],
-            "id": "CHART-PAXUUqwmX9",
-            "meta": {
-              "chartId": 5538,
-              "height": 34,
-              "sliceName": "Genders",
-              "width": 3
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-2n0XgiHDgs"
-            ],
-            "type": "CHART"
-          },
-          "CHART-_T6n_K9iQN": {
-            "children": [],
-            "id": "CHART-_T6n_K9iQN",
-            "meta": {
-              "chartId": 5539,
-              "height": 36,
-              "sliceName": "Trends",
-              "width": 7
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW--EyBZQlDi"
-            ],
-            "type": "CHART"
-          },
-          "CHART-eNY0tcE_ic": {
-            "children": [],
-            "id": "CHART-eNY0tcE_ic",
-            "meta": {
-              "chartId": 5537,
-              "height": 34,
-              "sliceName": "Participants",
-              "width": 3
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-2n0XgiHDgs"
-            ],
-            "type": "CHART"
-          },
-          "CHART-g075mMgyYb": {
-            "children": [],
-            "id": "CHART-g075mMgyYb",
-            "meta": {
-              "chartId": 5541,
-              "height": 50,
-              "sliceName": "Girls",
-              "width": 3
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-eh0w37bWbR"
-            ],
-            "type": "CHART"
-          },
-          "CHART-n-zGGE6S1y": {
-            "children": [],
-            "id": "CHART-n-zGGE6S1y",
-            "meta": {
-              "chartId": 5542,
-              "height": 50,
-              "sliceName": "Girl Name Cloud",
-              "width": 4
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-eh0w37bWbR"
-            ],
-            "type": "CHART"
-          },
-          "CHART-vJIPjmcbD3": {
-            "children": [],
-            "id": "CHART-vJIPjmcbD3",
-            "meta": {
-              "chartId": 5543,
-              "height": 50,
-              "sliceName": "Boys",
-              "width": 3
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-kzWtcvo8R1"
-            ],
-            "type": "CHART"
-          },
-          "DASHBOARD_VERSION_KEY": "v2",
-          "GRID_ID": {
-            "children": [
-              "ROW-2n0XgiHDgs",
-              "ROW--EyBZQlDi",
-              "ROW-eh0w37bWbR",
-              "ROW-kzWtcvo8R1"
-            ],
-            "id": "GRID_ID",
-            "parents": [
-              "ROOT_ID"
-            ],
-            "type": "GRID"
-          },
-          "HEADER_ID": {
-            "id": "HEADER_ID",
-            "meta": {
-              "text": "Births"
-            },
-            "type": "HEADER"
-          },
-          "MARKDOWN-zaflB60tbC": {
-            "children": [],
-            "id": "MARKDOWN-zaflB60tbC",
-            "meta": {
-              "code": "<div style=\\"text-align:center\\">  <h1>Birth Names Dashboard</h1>  <img src=\\"/static/assets/images/babies.png\\" style=\\"width:50%;\\"></div>",
-              "height": 34,
-              "width": 6
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID",
-              "ROW-2n0XgiHDgs"
-            ],
-            "type": "MARKDOWN"
-          },
-          "ROOT_ID": {
-            "children": [
-              "GRID_ID"
-            ],
-            "id": "ROOT_ID",
-            "type": "ROOT"
-          },
-          "ROW--EyBZQlDi": {
-            "children": [
-              "CHART-_T6n_K9iQN",
-              "CHART-6n9jxb30JG"
-            ],
-            "id": "ROW--EyBZQlDi",
-            "meta": {
-              "background": "BACKGROUND_TRANSPARENT"
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID"
-            ],
-            "type": "ROW"
-          },
-          "ROW-2n0XgiHDgs": {
-            "children": [
-              "CHART-eNY0tcE_ic",
-              "MARKDOWN-zaflB60tbC",
-              "CHART-PAXUUqwmX9"
-            ],
-            "id": "ROW-2n0XgiHDgs",
-            "meta": {
-              "background": "BACKGROUND_TRANSPARENT"
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID"
-            ],
-            "type": "ROW"
-          },
-          "ROW-eh0w37bWbR": {
-            "children": [
-              "CHART-g075mMgyYb",
-              "CHART-n-zGGE6S1y",
-              "CHART-6GdlekVise"
-            ],
-            "id": "ROW-eh0w37bWbR",
-            "meta": {
-              "background": "BACKGROUND_TRANSPARENT"
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID"
-            ],
-            "type": "ROW"
-          },
-          "ROW-kzWtcvo8R1": {
-            "children": [
-              "CHART-vJIPjmcbD3",
-              "CHART-Jj9qh1ol-N",
-              "CHART-ODvantb_bF"
-            ],
-            "id": "ROW-kzWtcvo8R1",
-            "meta": {
-              "background": "BACKGROUND_TRANSPARENT"
-            },
-            "parents": [
-              "ROOT_ID",
-              "GRID_ID"
-            ],
-            "type": "ROW"
-          }
-        }
-        """  # noqa: E501
-        )
-    )
-    # pylint: enable=line-too-long
-    # dashboard v2 doesn't allow add markup slice
-    dash.slices = [slc for slc in slices if slc.viz_type != "markup"]
-    update_slice_ids(pos)
-    dash.dashboard_title = "USA Births Names"
-    dash.position_json = json.dumps(pos, indent=4)  # noqa: TID251
-    dash.slug = "births"
-    return dash

superset/examples/configs/charts/Misc Charts/Birth_in_France_by_department_in_2016.yaml

@@ -0,0 +1,26 @@
+slice_name: Birth in France by department in 2016
+description: null
+certified_by: null
+certification_details: null
+viz_type: country_map
+params:
+  entity: DEPT_ID
+  granularity_sqla: ''
+  metric:
+    aggregate: AVG
+    column:
+      column_name: '2004'
+      type: INT
+    expressionType: SIMPLE
+    label: Boys
+    optionName: metric_112342
+  row_limit: 500000
+  select_country: france
+  since: ''
+  until: ''
+  viz_type: country_map
+query_context: null
+cache_timeout: null
+uuid: 6bd584f1-0ef5-44fc-8a05-61400f83bb62
+version: 1.0.0
+dataset_uuid: c21dd48d-9a4b-4a08-a926-47c3601c2a8d

superset/examples/configs/charts/Misc Charts/OSM_Long_Lat.yaml

@@ -0,0 +1,21 @@
+slice_name: OSM Long/Lat
+description: null
+certified_by: null
+certification_details: null
+viz_type: osm
+params:
+  all_columns:
+  - occupancy
+  all_columns_x: LON
+  all_columns_y: LAT
+  granularity_sqla: day
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  row_limit: 500000
+  since: '2014-01-01'
+  until: now
+  viz_type: mapbox
+query_context: null
+cache_timeout: null
+uuid: a4e90860-c8f5-4c50-8c04-06b2e144809c
+version: 1.0.0
+dataset_uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7

superset/examples/configs/charts/Misc Charts/Parallel_Coordinates.yaml

@@ -0,0 +1,31 @@
+slice_name: Parallel Coordinates
+description: null
+certified_by: null
+certification_details: null
+viz_type: para
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby: []
+  limit: 100
+  markup_type: markdown
+  metrics:
+  - sum__SP_POP_TOTL
+  - sum__SP_RUR_TOTL_ZS
+  - sum__SH_DYN_AIDS
+  row_limit: 50000
+  secondary_metric: sum__SP_POP_TOTL
+  series: country_name
+  show_bubbles: true
+  since: '2011-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2012-01-01'
+  viz_type: para
+query_context: null
+cache_timeout: null
+uuid: 041377c4-0ca9-4a40-8abd-befcd137c0dc
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/Misc Charts/Pivot_Table_v2.yaml

@@ -0,0 +1,30 @@
+slice_name: Pivot Table v2
+description: null
+certified_by: null
+certification_details: null
+viz_type: pivot_table_v2
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  groupbyColumns:
+  - state
+  groupbyRows:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__num
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: pivot_table_v2
+query_context: "{\n    \"datasource\": {\n        \"id\": 2,\n        \"type\": \"\
+  table\"\n    },\n    \"queries\": [\n        {\n            \"columns\": [\n   \
+  \             \"name\",\n                \"state\"\n            ],\n           \
+  \ \"metrics\": [\n                \"sum__num\"\n            ]\n        }\n    ],\n\
+  \    \"result_format\": \"json\",\n    \"result_type\": \"full\"\n}"
+cache_timeout: null
+uuid: 86778b63-19d8-4278-a79f-c90a1b31e162
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Average_and_Sum_Trends.yaml

@@ -0,0 +1,32 @@
+slice_name: Average and Sum Trends
+description: null
+certified_by: null
+certification_details: null
+viz_type: mixed_timeseries
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: AVG
+    column:
+      column_name: num
+      type: BIGINT(20)
+    expressionType: SIMPLE
+    label: AVG(num)
+    optionName: metric_vgops097wej_g8uff99zhk7
+  metrics_b:
+  - sum__num
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: mixed_timeseries
+  yAxisIndex: 0
+  yAxisIndexB: 1
+query_context: null
+cache_timeout: null
+uuid: 9c690f97-9196-5e01-bec9-8f4975ea5108
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Boy_Name_Cloud.yaml

@@ -0,0 +1,31 @@
+slice_name: Boy Name Cloud
+description: null
+certified_by: null
+certification_details: null
+viz_type: word_cloud
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: boy
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '100'
+  markup_type: markdown
+  metric: sum__num
+  rotation: square
+  row_limit: 50000
+  series: name
+  size_from: '10'
+  size_to: '70'
+  time_range: '100 years ago : now'
+  viz_type: word_cloud
+query_context: null
+cache_timeout: null
+uuid: 6994ec83-0cf2-4a26-97e2-1e30b0002aa0
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Boys.yaml

@@ -0,0 +1,30 @@
+slice_name: Boys
+description: null
+certified_by: null
+certification_details: null
+viz_type: table
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: boy
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__num
+  row_limit: 50
+  time_range: '100 years ago : now'
+  timeseries_limit_metric: sum__num
+  viz_type: table
+query_context: null
+cache_timeout: null
+uuid: 0af97164-82f0-42bb-a611-7093e5c56596
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Daily_Totals.yaml

@@ -0,0 +1,28 @@
+slice_name: Daily Totals
+description: null
+certified_by: null
+certification_details: null
+viz_type: table
+params:
+  granularity_sqla: ds
+  groupby:
+  - ds
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  row_limit: 50
+  time_range: '1983 : 2023'
+  timeseries_limit_metric: sum__num
+  viz_type: table
+query_context: null
+cache_timeout: null
+uuid: a3d4f2e1-8c9b-4d2a-9e7f-1b6c8d5e2f4a
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Genders.yaml

@@ -0,0 +1,22 @@
+slice_name: Genders
+description: null
+certified_by: null
+certification_details: null
+viz_type: pie
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - gender
+  limit: '25'
+  markup_type: markdown
+  metric: sum__num
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: pie
+query_context: null
+cache_timeout: null
+uuid: fb05dca0-bd3e-4953-a0a5-94b51de3a653
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Genders_by_State.yaml

@@ -0,0 +1,44 @@
+slice_name: Genders by State
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_timeseries_bar
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator:
+    - other
+    expressionType: SIMPLE
+    filterOptionName: 2745eae5
+    operator: NOT IN
+    subject: state
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - state
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num_boys
+      type: BIGINT(20)
+    expressionType: SIMPLE
+    label: Boys
+    optionName: metric_11
+  - aggregate: SUM
+    column:
+      column_name: num_girls
+      type: BIGINT(20)
+    expressionType: SIMPLE
+    label: Girls
+    optionName: metric_12
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: echarts_timeseries_bar
+query_context: null
+cache_timeout: null
+uuid: 2cc25185-3d8c-494c-aa3c-14f081ac7e54
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Girl_Name_Cloud.yaml

@@ -0,0 +1,31 @@
+slice_name: Girl Name Cloud
+description: null
+certified_by: null
+certification_details: null
+viz_type: word_cloud
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: girl
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '100'
+  markup_type: markdown
+  metric: sum__num
+  rotation: square
+  row_limit: 50000
+  series: name
+  size_from: '10'
+  size_to: '70'
+  time_range: '100 years ago : now'
+  viz_type: word_cloud
+query_context: null
+cache_timeout: null
+uuid: ba6574fe-a6c0-41ef-9499-1ea6ff36bd2d
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Girls.yaml

@@ -0,0 +1,30 @@
+slice_name: Girls
+description: null
+certified_by: null
+certification_details: null
+viz_type: table
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: girl
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__num
+  row_limit: 50
+  time_range: '100 years ago : now'
+  timeseries_limit_metric: sum__num
+  viz_type: table
+query_context: null
+cache_timeout: null
+uuid: 44cfa30e-af8e-4176-8612-4df0c0609516
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Names_Sorted_by_Num_in_California.yaml

@@ -0,0 +1,36 @@
+slice_name: Names Sorted by Num in California
+description: null
+certified_by: null
+certification_details: null
+viz_type: table
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  row_limit: 50
+  time_range: '100 years ago : now'
+  timeseries_limit_metric:
+    aggregate: SUM
+    column:
+      column_name: num_california
+      expression: CASE WHEN state = 'CA' THEN num ELSE 0 END
+    expressionType: SIMPLE
+    label: SUM(num_california)
+  viz_type: table
+query_context: null
+cache_timeout: null
+uuid: e49ed2c4-b8a3-5736-bafe-4658790b113a
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Num_Births_Trend.yaml

@@ -0,0 +1,31 @@
+slice_name: Num Births Trend
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_timeseries_line
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  rich_tooltip: true
+  row_limit: 50000
+  show_legend: true
+  time_range: '100 years ago : now'
+  viz_type: echarts_timeseries_line
+query_context: null
+cache_timeout: null
+uuid: 5b8c76e5-0e5e-45c1-b07e-3b2cb9b9c7e8
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Number_of_California_Births.yaml

@@ -0,0 +1,27 @@
+slice_name: Number of California Births
+description: null
+certified_by: null
+certification_details: null
+viz_type: big_number_total
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric:
+    aggregate: SUM
+    column:
+      column_name: num_california
+      expression: CASE WHEN state = 'CA' THEN num ELSE 0 END
+    expressionType: SIMPLE
+    label: SUM(num_california)
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: big_number_total
+query_context: null
+cache_timeout: null
+uuid: 400ee69f-eda4-5fe8-bc30-299184e08048
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Number_of_Girls.yaml

@@ -0,0 +1,28 @@
+slice_name: Number of Girls
+description: null
+certified_by: null
+certification_details: null
+viz_type: big_number_total
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: girl
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric: sum__num
+  row_limit: 50000
+  subheader: total female participants
+  time_range: '100 years ago : now'
+  viz_type: big_number_total
+query_context: null
+cache_timeout: null
+uuid: 2f1a8720-7ea6-5b0f-b419-b75163f6bf17
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Participants.yaml

@@ -0,0 +1,21 @@
+slice_name: Participants
+description: null
+certified_by: null
+certification_details: null
+viz_type: big_number
+params:
+  compare_lag: '5'
+  compare_suffix: over 5Y
+  granularity_sqla: ds
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric: sum__num
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: big_number
+query_context: null
+cache_timeout: null
+uuid: 89ae3c32-eafa-4466-82cf-8c4328420782
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Pivot_Table.yaml

@@ -0,0 +1,32 @@
+slice_name: Pivot Table
+description: null
+certified_by: null
+certification_details: null
+viz_type: pivot_table_v2
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby: []
+  groupbyColumns:
+  - state
+  groupbyRows:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  viz_type: pivot_table_v2
+query_context: null
+cache_timeout: null
+uuid: b9038f33-aea3-52de-840b-0a32f4c0eb41
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Top_10_Boy_Name_Share.yaml

@@ -0,0 +1,39 @@
+slice_name: Top 10 Boy Name Share
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_area
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: boy
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  comparison_type: values
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: 10
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  row_limit: 50000
+  stacked_style: expand
+  time_grain_sqla: P1D
+  time_range: '100 years ago : now'
+  viz_type: echarts_area
+  x_axis_forma: smart_date
+query_context: null
+cache_timeout: null
+uuid: f35cca46-bb11-440e-8ba1-7f021bfe52a7
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Top_10_California_Names_Timeseries.yaml

@@ -0,0 +1,35 @@
+slice_name: Top 10 California Names Timeseries
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_timeseries_line
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '10'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num_california
+      expression: CASE WHEN state = 'CA' THEN num ELSE 0 END
+    expressionType: SIMPLE
+    label: SUM(num_california)
+  row_limit: 50000
+  time_range: '100 years ago : now'
+  timeseries_limit_metric:
+    aggregate: SUM
+    column:
+      column_name: num_california
+      expression: CASE WHEN state = 'CA' THEN num ELSE 0 END
+    expressionType: SIMPLE
+    label: SUM(num_california)
+  viz_type: echarts_timeseries_line
+query_context: null
+cache_timeout: null
+uuid: 6a587b9e-e28b-5c2a-abb9-c6c1f4fd56b5
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Top_10_Girl_Name_Share.yaml

@@ -0,0 +1,39 @@
+slice_name: Top 10 Girl Name Share
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_area
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator: girl
+    expressionType: SIMPLE
+    operator: ==
+    subject: gender
+  compare_lag: '10'
+  compare_suffix: o10Y
+  comparison_type: values
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: 10
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  row_limit: 50000
+  stacked_style: expand
+  time_grain_sqla: P1D
+  time_range: '100 years ago : now'
+  viz_type: echarts_area
+  x_axis_forma: smart_date
+query_context: null
+cache_timeout: null
+uuid: da76899a-d75c-467b-b0ce-cfa4819ed1b1
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/USA Births Names/Trends.yaml

@@ -0,0 +1,31 @@
+slice_name: Trends
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_timeseries_line
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  granularity_sqla: ds
+  groupby:
+  - name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: num
+      type: BIGINT
+    expressionType: SIMPLE
+    label: Births
+    optionName: metric_11
+  rich_tooltip: true
+  row_limit: 50000
+  show_legend: true
+  time_range: '100 years ago : now'
+  viz_type: echarts_timeseries_line
+query_context: null
+cache_timeout: null
+uuid: c6024db9-1695-4aa6-b846-42d9c96bfcbf
+version: 1.0.0
+dataset_uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76

superset/examples/configs/charts/Video Game Sales/Rise_and_Fall_of_Video_Game_Consoles.yaml

@@ -0,0 +1,120 @@
+slice_name: Rise & Fall of Video Game Consoles
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_area
+params:
+  adhoc_filters: []
+  annotation_layers: []
+  bottom_margin: auto
+  color_scheme: supersetColors
+  comparison_type: values
+  contribution: false
+  datasource: 21__table
+  granularity_sqla: year
+  groupby:
+  - platform
+  label_colors:
+    '0': '#1FA8C9'
+    '1': '#454E7C'
+    '2600': '#666666'
+    3DO: '#B2B2B2'
+    3DS: '#D1C6BC'
+    Action: '#1FA8C9'
+    Adventure: '#454E7C'
+    DC: '#A38F79'
+    DS: '#8FD3E4'
+    Europe: '#5AC189'
+    Fighting: '#5AC189'
+    GB: '#FDE380'
+    GBA: '#ACE1C4'
+    GC: '#5AC189'
+    GEN: '#3CCCCB'
+    GG: '#EFA1AA'
+    Japan: '#FF7F44'
+    Microsoft Game Studios: '#D1C6BC'
+    Misc: '#FF7F44'
+    N64: '#1FA8C9'
+    NES: '#9EE5E5'
+    NG: '#A1A6BD'
+    Nintendo: '#D3B3DA'
+    North America: '#666666'
+    Other: '#E04355'
+    PC: '#EFA1AA'
+    PCFX: '#FDE380'
+    PS: '#A1A6BD'
+    PS2: '#FCC700'
+    PS3: '#3CCCCB'
+    PS4: '#B2B2B2'
+    PSP: '#FEC0A1'
+    PSV: '#FCC700'
+    Platform: '#666666'
+    Puzzle: '#E04355'
+    Racing: '#FCC700'
+    Role-Playing: '#A868B7'
+    SAT: '#A868B7'
+    SCD: '#8FD3E4'
+    SNES: '#454E7C'
+    Shooter: '#3CCCCB'
+    Simulation: '#A38F79'
+    Sports: '#8FD3E4'
+    Strategy: '#A1A6BD'
+    TG16: '#FEC0A1'
+    Take-Two Interactive: '#9EE5E5'
+    WS: '#ACE1C4'
+    Wii: '#A38F79'
+    WiiU: '#E04355'
+    X360: '#A868B7'
+    XB: '#D3B3DA'
+    XOne: '#FF7F44'
+  line_interpolation: linear
+  metrics:
+  - aggregate: SUM
+    column:
+      column_name: global_sales
+      description: null
+      expression: null
+      filterable: true
+      groupby: true
+      id: 887
+      is_dttm: false
+      optionName: _col_Global_Sales
+      python_date_format: null
+      type: DOUBLE PRECISION
+      verbose_name: null
+    expressionType: SIMPLE
+    hasCustomLabel: false
+    isNew: false
+    label: SUM(Global_Sales)
+    optionName: metric_ufl75addr8c_oqqhdumirpn
+    sqlExpression: null
+  order_desc: true
+  queryFields:
+    groupby: groupby
+    metrics: metrics
+  rich_tooltip: true
+  rolling_type: None
+  row_limit: null
+  show_brush: auto
+  show_legend: false
+  slice_id: 659
+  stacked_style: stream
+  time_grain_sqla: null
+  time_range: No filter
+  url_params:
+    preselect_filters: '{"1389": {"platform": ["PS", "PS2", "PS3", "PS4"], "genre":
+      null, "__time_range": "No filter"}}'
+  viz_type: echarts_area
+  x_axis_format: smart_date
+  x_axis_label: Year Published
+  x_axis_showminmax: true
+  x_ticks_layout: auto
+  y_axis_bounds:
+  - null
+  - null
+  y_axis_format: SMART_NUMBER
+query_context: null
+cache_timeout: null
+uuid: 3d926244-6e32-5e42-8ade-7302b83a65d7
+version: 1.0.0
+dataset_uuid: 53d47c0c-c03d-47f0-b9ac-81225f808283

superset/examples/configs/charts/World Bank Health/Box_plot.yaml

@@ -0,0 +1,30 @@
+slice_name: Box plot
+description: null
+certified_by: null
+certification_details: null
+viz_type: box_plot
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby:
+  - region
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__SP_POP_TOTL
+  row_limit: 50000
+  show_bubbles: true
+  since: '1960-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: now
+  viz_type: box_plot
+  whisker_options: Min/max (no outliers)
+  x_ticks_layout: staggered
+query_context: null
+cache_timeout: null
+uuid: d31ba9c7-798b-4f84-87ef-ab31721680a8
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Growth_Rate.yaml

@@ -0,0 +1,29 @@
+slice_name: Growth Rate
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_timeseries_line
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby:
+  - country_name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__SP_POP_TOTL
+  num_period_compare: '10'
+  row_limit: 50000
+  show_bubbles: true
+  since: '1960-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2014-01-02'
+  viz_type: echarts_timeseries_line
+query_context: null
+cache_timeout: null
+uuid: cfcd7c5e-4759-4b28-bb7c-e2200508e978
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Life_Expectancy_VS_Rural.yaml

@@ -0,0 +1,51 @@
+slice_name: Life Expectancy VS Rural %
+description: null
+certified_by: null
+certification_details: null
+viz_type: bubble
+params:
+  adhoc_filters:
+  - clause: WHERE
+    comparator:
+    - TCA
+    - MNP
+    - DMA
+    - MHL
+    - MCO
+    - SXM
+    - CYM
+    - TUV
+    - IMY
+    - KNA
+    - ASM
+    - ADO
+    - AMA
+    - PLW
+    expressionType: SIMPLE
+    filterOptionName: 2745eae5
+    operator: NOT IN
+    subject: country_code
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_name
+  granularity_sqla: year
+  groupby: []
+  limit: 0
+  markup_type: markdown
+  max_bubble_size: '50'
+  row_limit: 50000
+  series: region
+  show_bubbles: true
+  since: '2011-01-01'
+  size: sum__SP_POP_TOTL
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2011-01-02'
+  viz_type: bubble
+  x: sum__SP_RUR_TOTL_ZS
+  y: sum__SP_DYN_LE00_IN
+query_context: null
+cache_timeout: null
+uuid: fa927236-7b66-4d03-ae6c-463d2d394123
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Most_Populated_Countries.yaml

@@ -0,0 +1,28 @@
+slice_name: Most Populated Countries
+description: null
+certified_by: null
+certification_details: null
+viz_type: table
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby:
+  - country_name
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__SP_POP_TOTL
+  row_limit: 50000
+  show_bubbles: true
+  since: '2014-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2014-01-02'
+  viz_type: table
+query_context: null
+cache_timeout: null
+uuid: 4183745e-1cc4-4f88-9ae6-973c69845ce4
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Rural.yaml

@@ -0,0 +1,36 @@
+slice_name: '% Rural'
+description: null
+certified_by: null
+certification_details: null
+viz_type: world_map
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric: sum__SP_RUR_TOTL_ZS
+  num_period_compare: '10'
+  row_limit: 50000
+  secondary_metric:
+    aggregate: SUM
+    column:
+      column_name: SP_RUR_TOTL
+      optionName: _col_SP_RUR_TOTL
+      type: DOUBLE
+    expressionType: SIMPLE
+    hasCustomLabel: true
+    label: Rural Population
+  show_bubbles: true
+  since: '2014-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2014-01-02'
+  viz_type: world_map
+query_context: null
+cache_timeout: null
+uuid: 8d889488-edb5-40cb-a69c-e2c14f009e2b
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Rural_Breakdown.yaml

@@ -0,0 +1,38 @@
+slice_name: Rural Breakdown
+description: null
+certified_by: null
+certification_details: null
+viz_type: sunburst_v2
+params:
+  columns:
+  - region
+  - country_name
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric: sum__SP_POP_TOTL
+  row_limit: 50000
+  secondary_metric:
+    aggregate: SUM
+    column:
+      column_name: SP_RUR_TOTL
+      optionName: _col_SP_RUR_TOTL
+      type: DOUBLE
+    expressionType: SIMPLE
+    hasCustomLabel: true
+    label: Rural Population
+  show_bubbles: true
+  since: '2011-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2011-01-02'
+  viz_type: sunburst_v2
+query_context: null
+cache_timeout: null
+uuid: 70a2e07b-0f45-4532-96ae-0c6db52d2e7c
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Treemap.yaml

@@ -0,0 +1,28 @@
+slice_name: Treemap
+description: null
+certified_by: null
+certification_details: null
+viz_type: treemap_v2
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby:
+  - region
+  - country_code
+  limit: '25'
+  markup_type: markdown
+  metric: sum__SP_POP_TOTL
+  row_limit: 50000
+  show_bubbles: true
+  since: '1960-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: now
+  viz_type: treemap_v2
+query_context: null
+cache_timeout: null
+uuid: fc941a12-88a0-42e7-ac48-c1ec4ed84640
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Worlds_Pop_Growth.yaml

@@ -0,0 +1,28 @@
+slice_name: World's Pop Growth
+description: null
+certified_by: null
+certification_details: null
+viz_type: echarts_area
+params:
+  compare_lag: '10'
+  compare_suffix: o10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby:
+  - region
+  limit: '25'
+  markup_type: markdown
+  metrics:
+  - sum__SP_POP_TOTL
+  row_limit: 50000
+  show_bubbles: true
+  since: '1960-01-01'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: now
+  viz_type: echarts_area
+query_context: null
+cache_timeout: null
+uuid: e18b5d28-3a3d-43ea-8a20-b198b44b08e3
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/World Bank Health/Worlds_Population.yaml

@@ -0,0 +1,26 @@
+slice_name: World's Population
+description: null
+certified_by: null
+certification_details: null
+viz_type: big_number
+params:
+  compare_lag: '10'
+  compare_suffix: over 10Y
+  country_fieldtype: cca3
+  entity: country_code
+  granularity_sqla: year
+  groupby: []
+  limit: '25'
+  markup_type: markdown
+  metric: sum__SP_POP_TOTL
+  row_limit: 50000
+  show_bubbles: true
+  since: '2000'
+  time_range: '2014-01-01 : 2014-01-02'
+  until: '2014-01-02'
+  viz_type: big_number
+query_context: null
+cache_timeout: null
+uuid: c50fc6e3-96fc-4e72-877b-2ea1a5e25c7a
+version: 1.0.0
+dataset_uuid: 3b851597-e0e9-42a1-83e4-55547811742e

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Arcs.yaml

@@ -0,0 +1,48 @@
+slice_name: Deck.gl Arcs
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_arc
+params:
+  color_picker:
+    a: 1
+    b: 135
+    g: 122
+    r: 0
+  datasource: 10__table
+  end_spatial:
+    latCol: LATITUDE_DEST
+    lonCol: LONGITUDE_DEST
+    type: latlong
+  granularity_sqla: null
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  row_limit: 5000
+  slice_id: 42
+  start_spatial:
+    latCol: LATITUDE
+    lonCol: LONGITUDE
+    type: latlong
+  stroke_width: 1
+  time_grain_sqla: null
+  time_range: ' : '
+  viewport:
+    altitude: 1.5
+    bearing: 8.546256357301871
+    height: 642
+    latitude: 44.596651438714254
+    longitude: -91.84340711201104
+    maxLatitude: 85.05113
+    maxPitch: 60
+    maxZoom: 20
+    minLatitude: -85.05113
+    minPitch: 0
+    minZoom: 0
+    pitch: 60
+    width: 997
+    zoom: 2.929837070560775
+  viz_type: deck_arc
+query_context: null
+cache_timeout: null
+uuid: 51a68f80-d538-4094-bb9e-346aad49b306
+version: 1.0.0
+dataset_uuid: 92980b06-cbec-4f34-9c2e-7308edc8c2b9

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Grid.yaml

@@ -0,0 +1,43 @@
+slice_name: Deck.gl Grid
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_grid
+params:
+  autozoom: false
+  color_picker:
+    a: 1
+    b: 0
+    g: 255
+    r: 14
+  datasource: 5__table
+  extruded: true
+  granularity_sqla: null
+  grid_size: 120
+  groupby: []
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  point_radius: Auto
+  point_radius_fixed:
+    type: fix
+    value: 2000
+  point_radius_unit: Pixels
+  row_limit: 5000
+  size: count
+  spatial:
+    latCol: LAT
+    lonCol: LON
+    type: latlong
+  time_grain_sqla: null
+  time_range: No filter
+  viewport:
+    bearing: 155.80099696026355
+    latitude: 37.7942314882596
+    longitude: -122.42066918995666
+    pitch: 53.470800300695146
+    zoom: 12.699690845482069
+  viz_type: deck_grid
+query_context: null
+cache_timeout: null
+uuid: a1b96ab6-3c0b-4cbc-b13a-a70749e84068
+version: 1.0.0
+dataset_uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Hexagons.yaml

@@ -0,0 +1,42 @@
+slice_name: Deck.gl Hexagons
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_hex
+params:
+  color_picker:
+    a: 1
+    b: 0
+    g: 255
+    r: 14
+  datasource: 5__table
+  extruded: true
+  granularity_sqla: null
+  grid_size: 40
+  groupby: []
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  point_radius: Auto
+  point_radius_fixed:
+    type: fix
+    value: 2000
+  point_radius_unit: Pixels
+  row_limit: 5000
+  size: count
+  spatial:
+    latCol: LAT
+    lonCol: LON
+    type: latlong
+  time_grain_sqla: null
+  time_range: No filter
+  viewport:
+    bearing: -2.3984797349335167
+    latitude: 37.789795085160335
+    longitude: -122.40632230075536
+    pitch: 54.08961642447763
+    zoom: 13.835465702403654
+  viz_type: deck_hex
+query_context: null
+cache_timeout: null
+uuid: bdfdce5d-c44d-4c63-8a45-0b2a1a29715b
+version: 1.0.0
+dataset_uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Path.yaml

@@ -0,0 +1,48 @@
+slice_name: Deck.gl Path
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_path
+params:
+  color_picker:
+    a: 1
+    b: 135
+    g: 122
+    r: 0
+  datasource: 12__table
+  js_columns:
+  - color
+  js_data_mutator: "data => data.map(d => ({\n    ...d,\n    color: colors.hexToRGB(d.extraProps.color)\n\
+    }));"
+  js_onclick_href: ''
+  js_tooltip: ''
+  line_column: path_json
+  line_type: json
+  line_width: 150
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  reverse_long_lat: false
+  row_limit: 5000
+  slice_id: 43
+  time_grain_sqla: null
+  time_range: ' : '
+  viewport:
+    altitude: 1.5
+    bearing: 0
+    height: 1094
+    latitude: 37.73671752604488
+    longitude: -122.18885402582598
+    maxLatitude: 85.05113
+    maxPitch: 60
+    maxZoom: 20
+    minLatitude: -85.05113
+    minPitch: 0
+    minZoom: 0
+    pitch: 0
+    width: 669
+    zoom: 9.51847667620428
+  viz_type: deck_path
+query_context: null
+cache_timeout: null
+uuid: 6332daf6-e442-469d-b66c-a6a38423d4c7
+version: 1.0.0
+dataset_uuid: 151c283f-c076-437a-8e2f-1cf65fe6db0d

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Polygons.yaml

@@ -0,0 +1,88 @@
+slice_name: Deck.gl Polygons
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_polygon
+params:
+  datasource: 11__table
+  extruded: true
+  fill_color_picker:
+    a: 1
+    b: 73
+    g: 65
+    r: 3
+  filled: true
+  granularity_sqla: null
+  js_columns: []
+  js_data_mutator: ''
+  js_onclick_href: ''
+  js_tooltip: ''
+  legend_format: .1s
+  legend_position: tr
+  line_column: contour
+  line_type: json
+  line_width: 10
+  line_width_unit: meters
+  linear_color_scheme: oranges
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  metric:
+    aggregate: SUM
+    column:
+      column_name: population
+      description: null
+      expression: null
+      filterable: true
+      groupby: true
+      id: 1332
+      is_dttm: false
+      optionName: _col_population
+      python_date_format: null
+      type: BIGINT
+      verbose_name: null
+    expressionType: SIMPLE
+    hasCustomLabel: true
+    label: Population
+    optionName: metric_t2v4qbfiz1_w6qgpx4h2p
+    sqlExpression: null
+  multiplier: 0.1
+  point_radius_fixed:
+    type: metric
+    value:
+      aggregate: null
+      column: null
+      expressionType: SQL
+      hasCustomLabel: null
+      label: Density
+      optionName: metric_c5rvwrzoo86_293h6yrv2ic
+      sqlExpression: SUM(population)/SUM(area)
+  reverse_long_lat: false
+  slice_id: 41
+  stroke_color_picker:
+    a: 1
+    b: 135
+    g: 122
+    r: 0
+  stroked: false
+  time_grain_sqla: null
+  time_range: ' : '
+  viewport:
+    altitude: 1.5
+    bearing: 37.89506450385642
+    height: 906
+    latitude: 37.752020331384834
+    longitude: -122.43388541747726
+    maxLatitude: 85.05113
+    maxPitch: 60
+    maxZoom: 20
+    minLatitude: -85.05113
+    minPitch: 0
+    minZoom: 0
+    pitch: 60
+    width: 667
+    zoom: 11.133995608594631
+  viz_type: deck_polygon
+query_context: null
+cache_timeout: null
+uuid: f3236785-149e-4cab-9408-f2cc69afd977
+version: 1.0.0
+dataset_uuid: a480e881-e90d-4dc8-818e-f9338c3ca839

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Scatterplot.yaml

@@ -0,0 +1,42 @@
+slice_name: Deck.gl Scatterplot
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_scatter
+params:
+  color_picker:
+    a: 0.82
+    b: 3
+    g: 0
+    r: 205
+  datasource: 5__table
+  granularity_sqla: null
+  groupby: []
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  max_radius: 250
+  min_radius: 1
+  multiplier: 10
+  point_radius_fixed:
+    type: metric
+    value: count
+  point_unit: square_m
+  row_limit: 5000
+  size: count
+  spatial:
+    latCol: LAT
+    lonCol: LON
+    type: latlong
+  time_grain_sqla: null
+  time_range: ' : '
+  viewport:
+    bearing: -4.952916738791771
+    latitude: 37.78926922909199
+    longitude: -122.42613341901688
+    pitch: 4.750411100577438
+    zoom: 12.729132798697304
+  viz_type: deck_scatter
+query_context: null
+cache_timeout: null
+uuid: cc75c4d5-8f79-4ffd-8e75-06162d4a867f
+version: 1.0.0
+dataset_uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7

superset/examples/configs/charts/deck.gl Demo/Deck.gl_Screen_grid.yaml

@@ -0,0 +1,41 @@
+slice_name: Deck.gl Screen grid
+description: null
+certified_by: null
+certification_details: null
+viz_type: deck_screengrid
+params:
+  color_picker:
+    a: 1
+    b: 0
+    g: 255
+    r: 14
+  datasource: 5__table
+  granularity_sqla: null
+  grid_size: 20
+  groupby: []
+  mapbox_style: https://tile.openstreetmap.org/{z}/{x}/{y}.png
+  point_radius: Auto
+  point_radius_fixed:
+    type: fix
+    value: 2000
+  point_unit: square_m
+  row_limit: 5000
+  size: count
+  spatial:
+    latCol: LAT
+    lonCol: LON
+    type: latlong
+  time_grain_sqla: null
+  time_range: No filter
+  viewport:
+    bearing: -4.952916738791771
+    latitude: 37.76024135844065
+    longitude: -122.41827069521386
+    pitch: 4.750411100577438
+    zoom: 14.161641703941438
+  viz_type: deck_screengrid
+query_context: null
+cache_timeout: null
+uuid: 966c802c-4733-489f-b65b-385083c85d90
+version: 1.0.0
+dataset_uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7

superset/examples/configs/dashboards/Misc_Charts.yaml

@@ -0,0 +1,83 @@
+dashboard_title: Misc Charts
+description: null
+css: null
+slug: misc_charts
+certified_by: null
+certification_details: null
+published: false
+uuid: 55a4fe9f-2682-4b0d-84c7-49ded4be11db
+position:
+  CHART-HJOYVMV0E7:
+    children: []
+    id: CHART-HJOYVMV0E7
+    meta:
+      chartId: 30
+      height: 69
+      sliceName: OSM Long/Lat
+      uuid: a4e90860-c8f5-4c50-8c04-06b2e144809c
+      width: 4
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-S1MK4M4A4X
+    - COLUMN-ByUFVf40EQ
+    type: CHART
+  CHART-S1WYNz4AVX:
+    children: []
+    id: CHART-S1WYNz4AVX
+    meta:
+      chartId: 10
+      height: 69
+      sliceName: Parallel Coordinates
+      uuid: 041377c4-0ca9-4a40-8abd-befcd137c0dc
+      width: 4
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-SytNzNA4X
+    type: CHART
+  CHART-rkgF4G4A4X:
+    children: []
+    id: CHART-rkgF4G4A4X
+    meta:
+      chartId: 31
+      height: 69
+      sliceName: Birth in France by department in 2016
+      uuid: 6bd584f1-0ef5-44fc-8a05-61400f83bb62
+      width: 4
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-SytNzNA4X
+    type: CHART
+  DASHBOARD_VERSION_KEY: v2
+  GRID_ID:
+    children:
+    - ROW-SytNzNA4X
+    id: GRID_ID
+    parents:
+    - ROOT_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-SytNzNA4X:
+    children:
+    - CHART-rkgF4G4A4X
+    - CHART-S1WYNz4AVX
+    - CHART-HJOYVMV0E7
+    id: ROW-SytNzNA4X
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    type: ROW
+version: 1.0.0

superset/examples/configs/dashboards/USA_Births_Names.yaml

@@ -0,0 +1,263 @@
+dashboard_title: USA Births Names
+description: null
+css: null
+slug: births
+certified_by: null
+certification_details: null
+published: true
+uuid: fb7d30bc-b160-4371-861c-235d19bf6e25
+position:
+  CHART-6GdlekVise:
+    children: []
+    id: CHART-6GdlekVise
+    meta:
+      chartId: 19
+      height: 50
+      sliceName: Top 10 Girl Name Share
+      width: 5
+      uuid: da76899a-d75c-467b-b0ce-cfa4819ed1b1
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-eh0w37bWbR
+    type: CHART
+  CHART-6n9jxb30JG:
+    children: []
+    id: CHART-6n9jxb30JG
+    meta:
+      chartId: 14
+      height: 36
+      sliceName: Genders by State
+      width: 5
+      uuid: 2cc25185-3d8c-494c-aa3c-14f081ac7e54
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW--EyBZQlDi
+    type: CHART
+  CHART-Jj9qh1ol-N:
+    children: []
+    id: CHART-Jj9qh1ol-N
+    meta:
+      chartId: 18
+      height: 50
+      sliceName: Boy Name Cloud
+      width: 4
+      uuid: 6994ec83-0cf2-4a26-97e2-1e30b0002aa0
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-kzWtcvo8R1
+    type: CHART
+  CHART-ODvantb_bF:
+    children: []
+    id: CHART-ODvantb_bF
+    meta:
+      chartId: 20
+      height: 50
+      sliceName: Top 10 Boy Name Share
+      width: 5
+      uuid: f35cca46-bb11-440e-8ba1-7f021bfe52a7
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-kzWtcvo8R1
+    type: CHART
+  CHART-PAXUUqwmX9:
+    children: []
+    id: CHART-PAXUUqwmX9
+    meta:
+      chartId: 12
+      height: 34
+      sliceName: Genders
+      width: 3
+      uuid: fb05dca0-bd3e-4953-a0a5-94b51de3a653
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-2n0XgiHDgs
+    type: CHART
+  CHART-_T6n_K9iQN:
+    children: []
+    id: CHART-_T6n_K9iQN
+    meta:
+      chartId: 13
+      height: 36
+      sliceName: Trends
+      width: 7
+      uuid: c6024db9-1695-4aa6-b846-42d9c96bfcbf
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW--EyBZQlDi
+    type: CHART
+  CHART-eNY0tcE_ic:
+    children: []
+    id: CHART-eNY0tcE_ic
+    meta:
+      chartId: 11
+      height: 34
+      sliceName: Participants
+      width: 3
+      uuid: 89ae3c32-eafa-4466-82cf-8c4328420782
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-2n0XgiHDgs
+    type: CHART
+  CHART-g075mMgyYb:
+    children: []
+    id: CHART-g075mMgyYb
+    meta:
+      chartId: 15
+      height: 50
+      sliceName: Girls
+      width: 3
+      uuid: 44cfa30e-af8e-4176-8612-4df0c0609516
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-eh0w37bWbR
+    type: CHART
+  CHART-n-zGGE6S1y:
+    children: []
+    id: CHART-n-zGGE6S1y
+    meta:
+      chartId: 16
+      height: 50
+      sliceName: Girl Name Cloud
+      width: 4
+      uuid: ba6574fe-a6c0-41ef-9499-1ea6ff36bd2d
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-eh0w37bWbR
+    type: CHART
+  CHART-vJIPjmcbD3:
+    children: []
+    id: CHART-vJIPjmcbD3
+    meta:
+      chartId: 17
+      height: 50
+      sliceName: Boys
+      width: 3
+      uuid: 0af97164-82f0-42bb-a611-7093e5c56596
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-kzWtcvo8R1
+    type: CHART
+  DASHBOARD_VERSION_KEY: v2
+  GRID_ID:
+    children:
+    - ROW-2n0XgiHDgs
+    - ROW--EyBZQlDi
+    - ROW-eh0w37bWbR
+    - ROW-kzWtcvo8R1
+    - ROW-N-0P6H2KVI
+    id: GRID_ID
+    parents:
+    - ROOT_ID
+    type: GRID
+  HEADER_ID:
+    id: HEADER_ID
+    meta:
+      text: Births
+    type: HEADER
+  MARKDOWN-zaflB60tbC:
+    children: []
+    id: MARKDOWN-zaflB60tbC
+    meta:
+      code: <div style="text-align:center">  <h1>Birth Names Dashboard</h1>  <img
+        src="/static/assets/images/babies.png" style="width:50%;"></div>
+      height: 34
+      width: 6
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-2n0XgiHDgs
+    type: MARKDOWN
+  ROOT_ID:
+    children:
+    - GRID_ID
+    id: ROOT_ID
+    type: ROOT
+  ROW--EyBZQlDi:
+    children:
+    - CHART-_T6n_K9iQN
+    - CHART-6n9jxb30JG
+    id: ROW--EyBZQlDi
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    type: ROW
+  ROW-2n0XgiHDgs:
+    children:
+    - CHART-eNY0tcE_ic
+    - MARKDOWN-zaflB60tbC
+    - CHART-PAXUUqwmX9
+    id: ROW-2n0XgiHDgs
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    type: ROW
+  ROW-eh0w37bWbR:
+    children:
+    - CHART-g075mMgyYb
+    - CHART-n-zGGE6S1y
+    - CHART-6GdlekVise
+    id: ROW-eh0w37bWbR
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    type: ROW
+  ROW-kzWtcvo8R1:
+    children:
+    - CHART-vJIPjmcbD3
+    - CHART-Jj9qh1ol-N
+    - CHART-ODvantb_bF
+    id: ROW-kzWtcvo8R1
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    type: ROW
+  ROW-N-0P6H2KVI:
+    children:
+    - CHART-A62J4Z7R
+    id: ROW-N-0P6H2KVI
+    meta:
+      '0': ROOT_ID
+      background: BACKGROUND_TRANSPARENT
+    type: ROW
+    parents:
+    - ROOT_ID
+    - GRID_ID
+  CHART-A62J4Z7R:
+    children: []
+    id: CHART-A62J4Z7R
+    meta:
+      chartId: 21
+      height: 50
+      sliceName: Pivot Table v2
+      uuid: 86778b63-19d8-4278-a79f-c90a1b31e162
+      width: 4
+    type: CHART
+    parents:
+    - ROOT_ID
+    - GRID_ID
+    - ROW-N-0P6H2KVI
+metadata:
+  label_colors:
+    Girls: '#FF69B4'
+    Boys: '#ADD8E6'
+    girl: '#FF69B4'
+    boy: '#ADD8E6'
+version: 1.0.0

superset/examples/configs/dashboards/World_Banks_Data.yaml

@@ -0,0 +1,175 @@
+dashboard_title: World Bank's Data
+description: null
+css: null
+slug: world_health
+certified_by: null
+certification_details: null
+published: true
+uuid: d37232b3-43b9-486a-a132-e387dc0ff8de
+position:
+  CHART-37982887:
+    children: []
+    id: CHART-37982887
+    meta:
+      chartId: 1
+      height: 52
+      sliceName: World's Population
+      width: 2
+      uuid: c50fc6e3-96fc-4e72-877b-2ea1a5e25c7a
+    type: CHART
+  CHART-17e0f8d8:
+    children: []
+    id: CHART-17e0f8d8
+    meta:
+      chartId: 2
+      height: 92
+      sliceName: Most Populated Countries
+      width: 3
+      uuid: 4183745e-1cc4-4f88-9ae6-973c69845ce4
+    type: CHART
+  CHART-2ee52f30:
+    children: []
+    id: CHART-2ee52f30
+    meta:
+      chartId: 3
+      height: 38
+      sliceName: Growth Rate
+      width: 6
+      uuid: cfcd7c5e-4759-4b28-bb7c-e2200508e978
+    type: CHART
+  CHART-2d5b6871:
+    children: []
+    id: CHART-2d5b6871
+    meta:
+      chartId: 4
+      height: 52
+      sliceName: '% Rural'
+      width: 7
+      uuid: 8d889488-edb5-40cb-a69c-e2c14f009e2b
+    type: CHART
+  CHART-0fd0d252:
+    children: []
+    id: CHART-0fd0d252
+    meta:
+      chartId: 5
+      height: 50
+      sliceName: Life Expectancy VS Rural %
+      width: 8
+      uuid: fa927236-7b66-4d03-ae6c-463d2d394123
+    type: CHART
+  CHART-97f4cb48:
+    children: []
+    id: CHART-97f4cb48
+    meta:
+      chartId: 6
+      height: 38
+      sliceName: Rural Breakdown
+      width: 3
+      uuid: 70a2e07b-0f45-4532-96ae-0c6db52d2e7c
+    type: CHART
+  CHART-b5e05d6f:
+    children: []
+    id: CHART-b5e05d6f
+    meta:
+      chartId: 7
+      height: 50
+      sliceName: World's Pop Growth
+      width: 4
+      uuid: e18b5d28-3a3d-43ea-8a20-b198b44b08e3
+    type: CHART
+  CHART-e76e9f5f:
+    children: []
+    id: CHART-e76e9f5f
+    meta:
+      chartId: 8
+      height: 50
+      sliceName: Box plot
+      width: 4
+      uuid: d31ba9c7-798b-4f84-87ef-ab31721680a8
+    type: CHART
+  CHART-a4808bba:
+    children: []
+    id: CHART-a4808bba
+    meta:
+      chartId: 9
+      height: 50
+      sliceName: Treemap
+      width: 8
+      uuid: fc941a12-88a0-42e7-ac48-c1ec4ed84640
+    type: CHART
+  COLUMN-071bbbad:
+    children:
+    - ROW-1e064e3c
+    - ROW-afdefba9
+    id: COLUMN-071bbbad
+    meta:
+      background: BACKGROUND_TRANSPARENT
+      width: 9
+    type: COLUMN
+  COLUMN-fe3914b8:
+    children:
+    - 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
+version: 1.0.0

superset/examples/configs/dashboards/deck.gl_Demo.yaml

@@ -0,0 +1,130 @@
+dashboard_title: deck.gl Demo
+description: null
+css: null
+slug: deck
+certified_by: null
+certification_details: null
+published: true
+uuid: b78795f1-0b33-41a9-a6c7-186f38a526ad
+position:
+  CHART-3afd9d70:
+    meta:
+      chartId: 32
+      sliceName: Deck.gl Scatterplot
+      width: 6
+      height: 50
+      uuid: cc75c4d5-8f79-4ffd-8e75-06162d4a867f
+    type: CHART
+    id: CHART-3afd9d70
+    children: []
+  CHART-2ee7fa5e:
+    meta:
+      chartId: 33
+      sliceName: Deck.gl Screen grid
+      width: 6
+      height: 50
+      uuid: 966c802c-4733-489f-b65b-385083c85d90
+    type: CHART
+    id: CHART-2ee7fa5e
+    children: []
+  CHART-201f7715:
+    meta:
+      chartId: 34
+      sliceName: Deck.gl Hexagons
+      width: 6
+      height: 50
+      uuid: bdfdce5d-c44d-4c63-8a45-0b2a1a29715b
+    type: CHART
+    id: CHART-201f7715
+    children: []
+  CHART-d02f6c40:
+    meta:
+      chartId: 35
+      sliceName: Deck.gl Grid
+      width: 6
+      height: 50
+      uuid: a1b96ab6-3c0b-4cbc-b13a-a70749e84068
+    type: CHART
+    id: CHART-d02f6c40
+    children: []
+  CHART-2673431d:
+    meta:
+      chartId: 36
+      sliceName: Deck.gl Polygons
+      width: 6
+      height: 50
+      uuid: f3236785-149e-4cab-9408-f2cc69afd977
+    type: CHART
+    id: CHART-2673431d
+    children: []
+  CHART-85265a60:
+    meta:
+      chartId: 37
+      sliceName: Deck.gl Arcs
+      width: 6
+      height: 50
+      uuid: 51a68f80-d538-4094-bb9e-346aad49b306
+    type: CHART
+    id: CHART-85265a60
+    children: []
+  CHART-2b87513c:
+    meta:
+      chartId: 38
+      sliceName: Deck.gl Path
+      width: 6
+      height: 50
+      uuid: 6332daf6-e442-469d-b66c-a6a38423d4c7
+    type: CHART
+    id: CHART-2b87513c
+    children: []
+  GRID_ID:
+    type: GRID
+    id: GRID_ID
+    children:
+    - ROW-a7b16cb5
+    - ROW-72c218a5
+    - ROW-957ba55b
+    - ROW-af041bdd
+  HEADER_ID:
+    meta:
+      text: deck.gl Demo
+    type: HEADER
+    id: HEADER_ID
+  ROOT_ID:
+    type: ROOT
+    id: ROOT_ID
+    children:
+    - GRID_ID
+  ROW-72c218a5:
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    type: ROW
+    id: ROW-72c218a5
+    children:
+    - CHART-d02f6c40
+    - CHART-201f7715
+  ROW-957ba55b:
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    type: ROW
+    id: ROW-957ba55b
+    children:
+    - CHART-2673431d
+    - CHART-85265a60
+  ROW-a7b16cb5:
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    type: ROW
+    id: ROW-a7b16cb5
+    children:
+    - CHART-3afd9d70
+    - CHART-2ee7fa5e
+  ROW-af041bdd:
+    meta:
+      background: BACKGROUND_TRANSPARENT
+    type: ROW
+    id: ROW-af041bdd
+    children:
+    - CHART-2b87513c
+  DASHBOARD_VERSION_KEY: v2
+version: 1.0.0

superset/examples/configs/datasets/examples/bart_lines.yaml

@@ -0,0 +1,80 @@
+table_name: bart_lines
+main_dttm_col: null
+description: BART lines
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: 151c283f-c076-437a-8e2f-1cf65fe6db0d
+metrics:
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: name
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(255)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: color
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(255)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: path_json
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: polyline
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/bart-lines.json.gz

superset/examples/configs/datasets/examples/birth_france_by_region.yaml

@@ -0,0 +1,209 @@
+table_name: birth_france_by_region
+main_dttm_col: dttm
+description: null
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: c21dd48d-9a4b-4a08-a926-47c3601c2a8d
+metrics:
+- metric_name: avg__2004
+  verbose_name: null
+  metric_type: null
+  expression: AVG("2004")
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: DEPT_ID
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(10)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2010'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2003'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2004'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2005'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2006'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2007'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2008'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2009'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2011'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2012'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2013'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: '2014'
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: dttm
+  verbose_name: null
+  is_dttm: true
+  is_active: true
+  type: DATE
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/paris_iris.json.gz

superset/examples/configs/datasets/examples/birth_names.yaml

@@ -0,0 +1,137 @@
+table_name: birth_names
+main_dttm_col: ds
+description: null
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: 4ec507ac-bece-4d2b-8dc3-cfb7c3515e76
+metrics:
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+- metric_name: sum__num
+  verbose_name: null
+  metric_type: null
+  expression: SUM(num)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: num_california
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: null
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: CASE WHEN state = 'CA' THEN num ELSE 0 END
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ds
+  verbose_name: null
+  is_dttm: true
+  is_active: true
+  type: TIMESTAMP WITHOUT TIME ZONE
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: state
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(10)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: gender
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(16)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: name
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(255)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: num_boys
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: num_girls
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: num
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/birth_names2.json.gz

superset/examples/configs/datasets/examples/flights.yaml

@@ -0,0 +1,560 @@
+table_name: flights
+main_dttm_col: ds
+description: Random set of flights in the US
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: 92980b06-cbec-4f34-9c2e-7308edc8c2b9
+metrics:
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: ds
+  verbose_name: null
+  is_dttm: true
+  is_active: true
+  type: TIMESTAMP WITHOUT TIME ZONE
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LATE_AIRCRAFT_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ARRIVAL_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DEPARTURE_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: WEATHER_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIRLINE_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIR_SYSTEM_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ARRIVAL_TIME
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: SECURITY_DELAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LATITUDE_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ELAPSED_TIME
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DEPARTURE_TIME
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LATITUDE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIR_TIME
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: TAXI_IN
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: TAXI_OUT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LONGITUDE_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LONGITUDE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: WHEELS_OFF
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: WHEELS_ON
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: CANCELLATION_REASON
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: SCHEDULED_ARRIVAL
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DESTINATION_AIRPORT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: CANCELLED
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: SCHEDULED_DEPARTURE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DISTANCE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DAY_OF_WEEK
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DAY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: TAIL_NUMBER
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: YEAR
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: STATE_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIRPORT_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIRLINE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: STATE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ORIGIN_AIRPORT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: AIRPORT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: FLIGHT_NUMBER
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: SCHEDULED_TIME
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DIVERTED
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: MONTH
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: CITY_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: COUNTRY_DEST
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: CITY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: COUNTRY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/flight_data.csv.gz

superset/examples/configs/datasets/examples/long_lat.yaml

@@ -0,0 +1,212 @@
+table_name: long_lat
+main_dttm_col: datetime
+description: null
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: 605eaec7-ebf1-4fea-ac4b-07652fcb46e7
+metrics:
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: datetime
+  verbose_name: null
+  is_dttm: true
+  is_active: true
+  type: TIMESTAMP WITHOUT TIME ZONE
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LAT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: DISTRICT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: CITY
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: ID
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: REGION
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: LON
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: radius_miles
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: occupancy
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: delimited
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(60)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: geohash
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: VARCHAR(12)
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: POSTCODE
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: NUMBER
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: STREET
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: UNIT
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/san_francisco.csv.gz

superset/examples/configs/datasets/examples/sf_population_polygons.yaml

@@ -0,0 +1,80 @@
+table_name: sf_population_polygons
+main_dttm_col: null
+description: Population density of San Francisco
+default_endpoint: null
+offset: 0
+cache_timeout: null
+catalog: null
+schema: public
+sql: null
+params: null
+template_params: null
+filter_select_enabled: true
+fetch_values_predicate: null
+extra: null
+normalize_columns: false
+always_filter_main_dttm: false
+folders: null
+uuid: a480e881-e90d-4dc8-818e-f9338c3ca839
+metrics:
+- metric_name: count
+  verbose_name: COUNT(*)
+  metric_type: count
+  expression: COUNT(*)
+  description: null
+  d3format: null
+  currency: null
+  extra: null
+  warning_text: null
+columns:
+- column_name: area
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: DOUBLE PRECISION
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: population
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: zipcode
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: BIGINT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+- column_name: contour
+  verbose_name: null
+  is_dttm: false
+  is_active: true
+  type: TEXT
+  advanced_data_type: null
+  groupby: true
+  filterable: true
+  expression: null
+  description: null
+  python_date_format: null
+  extra: null
+version: 1.0.0
+database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee
+data: https://cdn.jsdelivr.net/gh/apache-superset/examples-data@master/sf_population.json.gz

superset/examples/data_loading.py

@@ -14,42 +14,13 @@
 #  KIND, either express or implied.  See the License for the
 #  specific language governing permissions and limitations
 #  under the License.
-from .bart_lines import load_bart_lines
-from .big_data import load_big_data
-from .birth_names import load_birth_names
 from .country_map import load_country_map_data
 from .css_templates import load_css_templates
-from .deck import load_deck_dash
-from .energy import load_energy
-from .flights import load_flights
-from .long_lat import load_long_lat_data
-from .misc_dashboard import load_misc_dashboard
-from .multiformat_time_series import load_multiformat_time_series
-from .paris import load_paris_iris_geojson
-from .random_time_series import load_random_time_series_data
-from .sf_population_polygons import load_sf_population_polygons
-from .supported_charts_dashboard import load_supported_charts_dashboard
-from .tabbed_dashboard import load_tabbed_dashboard
-from .utils import load_examples_from_configs
-from .world_bank import load_world_bank_health_n_pop
+from .utils import cleanup_old_examples, load_examples_from_configs
 
 __all__ = [
-    "load_bart_lines",
-    "load_big_data",
-    "load_birth_names",
+    "cleanup_old_examples",
     "load_country_map_data",
     "load_css_templates",
-    "load_deck_dash",
-    "load_energy",
-    "load_flights",
-    "load_long_lat_data",
-    "load_misc_dashboard",
-    "load_multiformat_time_series",
-    "load_paris_iris_geojson",
-    "load_random_time_series_data",
-    "load_sf_population_polygons",
-    "load_supported_charts_dashboard",
-    "load_tabbed_dashboard",
     "load_examples_from_configs",
-    "load_world_bank_health_n_pop",
 ]

superset/examples/deck.py

@@ -1,547 +0,0 @@
-# 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.
-
-import logging
-
-from superset import db
-from superset.models.dashboard import Dashboard
-from superset.models.slice import Slice
-from superset.utils import json
-from superset.utils.core import DatasourceType
-
-from .helpers import (
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    update_slice_ids,
-)
-
-logger = logging.getLogger(__name__)
-
-COLOR_RED = {"r": 205, "g": 0, "b": 3, "a": 0.82}
-POSITION_JSON = """\
-{
-    "CHART-3afd9d70": {
-        "meta": {
-            "chartId": 66,
-            "sliceName": "Deck.gl Scatterplot",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-3afd9d70",
-        "children": []
-    },
-    "CHART-2ee7fa5e": {
-        "meta": {
-            "chartId": 67,
-            "sliceName": "Deck.gl Screen grid",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2ee7fa5e",
-        "children": []
-    },
-    "CHART-201f7715": {
-        "meta": {
-            "chartId": 68,
-            "sliceName": "Deck.gl Hexagons",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-201f7715",
-        "children": []
-    },
-    "CHART-d02f6c40": {
-        "meta": {
-            "chartId": 69,
-            "sliceName": "Deck.gl Grid",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-d02f6c40",
-        "children": []
-    },
-    "CHART-2673431d": {
-        "meta": {
-            "chartId": 70,
-            "sliceName": "Deck.gl Polygons",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2673431d",
-        "children": []
-    },
-    "CHART-85265a60": {
-        "meta": {
-            "chartId": 71,
-            "sliceName": "Deck.gl Arcs",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-85265a60",
-        "children": []
-    },
-    "CHART-2b87513c": {
-        "meta": {
-            "chartId": 72,
-            "sliceName": "Deck.gl Path",
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2b87513c",
-        "children": []
-    },
-    "GRID_ID": {
-        "type": "GRID",
-        "id": "GRID_ID",
-        "children": [
-            "ROW-a7b16cb5",
-            "ROW-72c218a5",
-            "ROW-957ba55b",
-            "ROW-af041bdd"
-        ]
-    },
-    "HEADER_ID": {
-        "meta": {
-            "text": "deck.gl Demo"
-        },
-        "type": "HEADER",
-        "id": "HEADER_ID"
-    },
-    "ROOT_ID": {
-        "type": "ROOT",
-        "id": "ROOT_ID",
-        "children": [
-            "GRID_ID"
-        ]
-    },
-    "ROW-72c218a5": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-72c218a5",
-        "children": [
-            "CHART-d02f6c40",
-            "CHART-201f7715"
-        ]
-    },
-    "ROW-957ba55b": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-957ba55b",
-        "children": [
-            "CHART-2673431d",
-            "CHART-85265a60"
-        ]
-    },
-    "ROW-a7b16cb5": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-a7b16cb5",
-        "children": [
-            "CHART-3afd9d70",
-            "CHART-2ee7fa5e"
-        ]
-    },
-    "ROW-af041bdd": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-af041bdd",
-        "children": [
-            "CHART-2b87513c"
-        ]
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}"""
-
-
-def load_deck_dash() -> None:  # pylint: disable=too-many-statements
-    logger.debug("Loading deck.gl dashboard")
-    slices = []
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name="long_lat").first()
-    slice_data = {
-        "spatial": {"type": "latlong", "lonCol": "LON", "latCol": "LAT"},
-        "color_picker": COLOR_RED,
-        "datasource": "5__table",
-        "granularity_sqla": None,
-        "groupby": [],
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "multiplier": 10,
-        "point_radius_fixed": {"type": "metric", "value": "count"},
-        "point_unit": "square_m",
-        "min_radius": 1,
-        "max_radius": 250,
-        "row_limit": 5000,
-        "time_range": " : ",
-        "size": "count",
-        "time_grain_sqla": None,
-        "viewport": {
-            "bearing": -4.952916738791771,
-            "latitude": 37.78926922909199,
-            "longitude": -122.42613341901688,
-            "pitch": 4.750411100577438,
-            "zoom": 12.729132798697304,
-        },
-        "viz_type": "deck_scatter",
-    }
-
-    logger.debug("Creating Scatterplot slice")
-    slc = Slice(
-        slice_name="Deck.gl Scatterplot",
-        viz_type="deck_scatter",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "point_unit": "square_m",
-        "row_limit": 5000,
-        "spatial": {"type": "latlong", "lonCol": "LON", "latCol": "LAT"},
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "granularity_sqla": None,
-        "size": "count",
-        "viz_type": "deck_screengrid",
-        "time_range": "No filter",
-        "point_radius": "Auto",
-        "color_picker": {"a": 1, "r": 14, "b": 0, "g": 255},
-        "grid_size": 20,
-        "viewport": {
-            "zoom": 14.161641703941438,
-            "longitude": -122.41827069521386,
-            "bearing": -4.952916738791771,
-            "latitude": 37.76024135844065,
-            "pitch": 4.750411100577438,
-        },
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    logger.debug("Creating Screen Grid slice")
-    slc = Slice(
-        slice_name="Deck.gl Screen grid",
-        viz_type="deck_screengrid",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "spatial": {"type": "latlong", "lonCol": "LON", "latCol": "LAT"},
-        "row_limit": 5000,
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "granularity_sqla": None,
-        "size": "count",
-        "viz_type": "deck_hex",
-        "time_range": "No filter",
-        "point_radius_unit": "Pixels",
-        "point_radius": "Auto",
-        "color_picker": {"a": 1, "r": 14, "b": 0, "g": 255},
-        "grid_size": 40,
-        "extruded": True,
-        "viewport": {
-            "latitude": 37.789795085160335,
-            "pitch": 54.08961642447763,
-            "zoom": 13.835465702403654,
-            "longitude": -122.40632230075536,
-            "bearing": -2.3984797349335167,
-        },
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    logger.debug("Creating Hex slice")
-    slc = Slice(
-        slice_name="Deck.gl Hexagons",
-        viz_type="deck_hex",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "autozoom": False,
-        "spatial": {"type": "latlong", "lonCol": "LON", "latCol": "LAT"},
-        "row_limit": 5000,
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "granularity_sqla": None,
-        "size": "count",
-        "viz_type": "deck_grid",
-        "point_radius_unit": "Pixels",
-        "point_radius": "Auto",
-        "time_range": "No filter",
-        "color_picker": {"a": 1, "r": 14, "b": 0, "g": 255},
-        "grid_size": 120,
-        "extruded": True,
-        "viewport": {
-            "longitude": -122.42066918995666,
-            "bearing": 155.80099696026355,
-            "zoom": 12.699690845482069,
-            "latitude": 37.7942314882596,
-            "pitch": 53.470800300695146,
-        },
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    logger.debug("Creating Grid slice")
-    slc = Slice(
-        slice_name="Deck.gl Grid",
-        viz_type="deck_grid",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    polygon_tbl = (
-        db.session.query(table).filter_by(table_name="sf_population_polygons").first()
-    )
-    slice_data = {
-        "datasource": "11__table",
-        "viz_type": "deck_polygon",
-        "slice_id": 41,
-        "granularity_sqla": None,
-        "time_grain_sqla": None,
-        "time_range": " : ",
-        "line_column": "contour",
-        "metric": {
-            "aggregate": "SUM",
-            "column": {
-                "column_name": "population",
-                "description": None,
-                "expression": None,
-                "filterable": True,
-                "groupby": True,
-                "id": 1332,
-                "is_dttm": False,
-                "optionName": "_col_population",
-                "python_date_format": None,
-                "type": "BIGINT",
-                "verbose_name": None,
-            },
-            "expressionType": "SIMPLE",
-            "hasCustomLabel": True,
-            "label": "Population",
-            "optionName": "metric_t2v4qbfiz1_w6qgpx4h2p",
-            "sqlExpression": None,
-        },
-        "line_type": "json",
-        "linear_color_scheme": "oranges",
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "viewport": {
-            "longitude": -122.43388541747726,
-            "latitude": 37.752020331384834,
-            "zoom": 11.133995608594631,
-            "bearing": 37.89506450385642,
-            "pitch": 60,
-            "width": 667,
-            "height": 906,
-            "altitude": 1.5,
-            "maxZoom": 20,
-            "minZoom": 0,
-            "maxPitch": 60,
-            "minPitch": 0,
-            "maxLatitude": 85.05113,
-            "minLatitude": -85.05113,
-        },
-        "reverse_long_lat": False,
-        "fill_color_picker": {"r": 3, "g": 65, "b": 73, "a": 1},
-        "stroke_color_picker": {"r": 0, "g": 122, "b": 135, "a": 1},
-        "filled": True,
-        "stroked": False,
-        "extruded": True,
-        "multiplier": 0.1,
-        "line_width": 10,
-        "line_width_unit": "meters",
-        "point_radius_fixed": {
-            "type": "metric",
-            "value": {
-                "aggregate": None,
-                "column": None,
-                "expressionType": "SQL",
-                "hasCustomLabel": None,
-                "label": "Density",
-                "optionName": "metric_c5rvwrzoo86_293h6yrv2ic",
-                "sqlExpression": "SUM(population)/SUM(area)",
-            },
-        },
-        "js_columns": [],
-        "js_data_mutator": "",
-        "js_tooltip": "",
-        "js_onclick_href": "",
-        "legend_format": ".1s",
-        "legend_position": "tr",
-    }
-
-    logger.debug("Creating Polygon slice")
-    slc = Slice(
-        slice_name="Deck.gl Polygons",
-        viz_type="deck_polygon",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=polygon_tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "datasource": "10__table",
-        "viz_type": "deck_arc",
-        "slice_id": 42,
-        "granularity_sqla": None,
-        "time_grain_sqla": None,
-        "time_range": " : ",
-        "start_spatial": {
-            "type": "latlong",
-            "latCol": "LATITUDE",
-            "lonCol": "LONGITUDE",
-        },
-        "end_spatial": {
-            "type": "latlong",
-            "latCol": "LATITUDE_DEST",
-            "lonCol": "LONGITUDE_DEST",
-        },
-        "row_limit": 5000,
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "viewport": {
-            "altitude": 1.5,
-            "bearing": 8.546256357301871,
-            "height": 642,
-            "latitude": 44.596651438714254,
-            "longitude": -91.84340711201104,
-            "maxLatitude": 85.05113,
-            "maxPitch": 60,
-            "maxZoom": 20,
-            "minLatitude": -85.05113,
-            "minPitch": 0,
-            "minZoom": 0,
-            "pitch": 60,
-            "width": 997,
-            "zoom": 2.929837070560775,
-        },
-        "color_picker": {"r": 0, "g": 122, "b": 135, "a": 1},
-        "stroke_width": 1,
-    }
-
-    logger.debug("Creating Arc slice")
-    slc = Slice(
-        slice_name="Deck.gl Arcs",
-        viz_type="deck_arc",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=db.session.query(table)
-        .filter_by(table_name="flights")
-        .first()
-        .id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "datasource": "12__table",
-        "slice_id": 43,
-        "viz_type": "deck_path",
-        "time_grain_sqla": None,
-        "time_range": " : ",
-        "line_column": "path_json",
-        "line_type": "json",
-        "row_limit": 5000,
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "viewport": {
-            "longitude": -122.18885402582598,
-            "latitude": 37.73671752604488,
-            "zoom": 9.51847667620428,
-            "bearing": 0,
-            "pitch": 0,
-            "width": 669,
-            "height": 1094,
-            "altitude": 1.5,
-            "maxZoom": 20,
-            "minZoom": 0,
-            "maxPitch": 60,
-            "minPitch": 0,
-            "maxLatitude": 85.05113,
-            "minLatitude": -85.05113,
-        },
-        "color_picker": {"r": 0, "g": 122, "b": 135, "a": 1},
-        "line_width": 150,
-        "reverse_long_lat": False,
-        "js_columns": ["color"],
-        "js_data_mutator": "data => data.map(d => ({\n"
-        "    ...d,\n"
-        "    color: colors.hexToRGB(d.extraProps.color)\n"
-        "}));",
-        "js_tooltip": "",
-        "js_onclick_href": "",
-    }
-
-    logger.debug("Creating Path slice")
-    slc = Slice(
-        slice_name="Deck.gl Path",
-        viz_type="deck_path",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=db.session.query(table)
-        .filter_by(table_name="bart_lines")
-        .first()
-        .id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-    slug = "deck"
-
-    logger.debug("Creating a dashboard")
-    title = "deck.gl Demo"
-    dash = db.session.query(Dashboard).filter_by(slug=slug).first()
-
-    if not dash:
-        dash = Dashboard()
-        db.session.add(dash)
-    dash.published = True
-    js = POSITION_JSON
-    pos = json.loads(js)
-    slices = update_slice_ids(pos)
-    dash.position_json = json.dumps(pos, indent=4)
-    dash.dashboard_title = title
-    dash.slug = slug
-    dash.slices = slices

superset/examples/flights.py

@@ -1,76 +0,0 @@
-# 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.
-import logging
-
-import pandas as pd
-from sqlalchemy import DateTime, inspect
-
-import superset.utils.database as database_utils
-from superset import db
-from superset.sql.parse import Table
-
-from .helpers import get_table_connector_registry, read_example_data
-
-logger = logging.getLogger(__name__)
-
-
-def load_flights(only_metadata: bool = False, force: bool = False) -> None:
-    """Loading random time series data from a zip file in the repo"""
-    tbl_name = "flights"
-    database = database_utils.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            pdf = read_example_data(
-                "examples://flight_data.csv.gz", encoding="latin-1", compression="gzip"
-            )
-
-            # Loading airports info to join and get lat/long
-            airports = read_example_data(
-                "examples://airports.csv.gz", encoding="latin-1", compression="gzip"
-            )
-            airports = airports.set_index("IATA_CODE")
-
-            pdf["ds"] = (
-                pdf.YEAR.map(str) + "-0" + pdf.MONTH.map(str) + "-0" + pdf.DAY.map(str)
-            )
-            pdf.ds = pd.to_datetime(pdf.ds)
-            pdf.drop(columns=["DAY", "MONTH", "YEAR"])
-            pdf = pdf.join(airports, on="ORIGIN_AIRPORT", rsuffix="_ORIG")
-            pdf = pdf.join(airports, on="DESTINATION_AIRPORT", rsuffix="_DEST")
-            pdf.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={"ds": DateTime},
-                index=False,
-            )
-
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = table(table_name=tbl_name, schema=schema)
-        db.session.add(tbl)
-    tbl.description = "Random set of flights in the US"
-    tbl.database = database
-    tbl.filter_select_enabled = True
-    tbl.fetch_metadata()
-    logger.debug("Done loading table!")

superset/examples/helpers.py

@@ -49,7 +49,7 @@ from urllib.error import HTTPError
 
 import pandas as pd
 
-from superset import app, db
+from superset import db
 from superset.connectors.sqla.models import SqlaTable
 from superset.models.slice import Slice
 from superset.utils import json
@@ -78,11 +78,6 @@ def get_table_connector_registry() -> Any:
     return SqlaTable
 
 
-def get_examples_folder() -> str:
-    """Return local path to the examples folder (when vendored)."""
-    return os.path.join(app.config["BASE_DIR"], "examples")
-
-
 def update_slice_ids(pos: dict[Any, Any]) -> list[Slice]:
     """Update slice ids in ``position_json`` and return the slices found."""
     slice_components = [

superset/examples/long_lat.py

@@ -1,127 +0,0 @@
-# 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.
-import datetime
-import logging
-import random
-
-import geohash
-from sqlalchemy import DateTime, Float, inspect, String
-
-import superset.utils.database as database_utils
-from superset import db
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils.core import DatasourceType
-
-from .helpers import (
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    misc_dash_slices,
-    read_example_data,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def load_long_lat_data(only_metadata: bool = False, force: bool = False) -> None:
-    """Loading lat/long data from a csv file in the repo"""
-    tbl_name = "long_lat"
-    database = database_utils.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            pdf = read_example_data(
-                "examples://san_francisco.csv.gz", encoding="utf-8", compression="gzip"
-            )
-            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))]  # noqa: S311
-            pdf["radius_miles"] = [random.uniform(1, 3) for _ in range(len(pdf))]  # noqa: S311
-            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(
-                tbl_name,
-                engine,
-                schema=schema,
-                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,
-            )
-        logger.debug("Done loading table!")
-        logger.debug("-" * 80)
-
-    logger.debug("Creating table reference")
-    table = get_table_connector_registry()
-    obj = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not obj:
-        obj = table(table_name=tbl_name, schema=schema)
-        db.session.add(obj)
-    obj.main_dttm_col = "datetime"
-    obj.database = database
-    obj.filter_select_enabled = True
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "day",
-        "since": "2014-01-01",
-        "until": "now",
-        "viz_type": "mapbox",
-        "all_columns_x": "LON",
-        "all_columns_y": "LAT",
-        "mapbox_style": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
-        "all_columns": ["occupancy"],
-        "row_limit": 500000,
-    }
-
-    logger.debug("Creating a slice")
-    slc = Slice(
-        slice_name="OSM Long/Lat",
-        viz_type="osm",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)

superset/examples/misc_dashboard.py

@@ -1,145 +0,0 @@
-# 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.
-import logging
-import textwrap
-
-from superset import db
-from superset.models.dashboard import Dashboard
-from superset.utils import json
-
-from .helpers import update_slice_ids
-
-logger = logging.getLogger(__name__)
-
-DASH_SLUG = "misc_charts"
-
-
-def load_misc_dashboard() -> None:
-    """Loading a dashboard featuring misc charts"""
-
-    logger.debug("Creating the dashboard")
-    db.session.expunge_all()
-    dash = db.session.query(Dashboard).filter_by(slug=DASH_SLUG).first()
-
-    if not dash:
-        dash = Dashboard()
-        db.session.add(dash)
-    js = textwrap.dedent(
-        """\
-{
-    "CHART-HJOYVMV0E7": {
-        "children": [],
-        "id": "CHART-HJOYVMV0E7",
-        "meta": {
-            "chartId": 3969,
-            "height": 69,
-            "sliceName": "OSM Long/Lat",
-            "uuid": "164efe31-295b-4408-aaa6-2f4bfb58a212",
-            "width": 4
-        },
-        "parents": [
-            "ROOT_ID",
-            "GRID_ID",
-            "ROW-S1MK4M4A4X",
-            "COLUMN-ByUFVf40EQ"
-        ],
-        "type": "CHART"
-    },
-    "CHART-S1WYNz4AVX": {
-        "children": [],
-        "id": "CHART-S1WYNz4AVX",
-        "meta": {
-            "chartId": 3989,
-            "height": 69,
-            "sliceName": "Parallel Coordinates",
-            "uuid": "e84f7e74-031a-47bb-9f80-ae0694dcca48",
-            "width": 4
-        },
-        "parents": [
-            "ROOT_ID",
-            "GRID_ID",
-            "ROW-SytNzNA4X"
-        ],
-        "type": "CHART"
-    },
-    "CHART-rkgF4G4A4X": {
-        "children": [],
-        "id": "CHART-rkgF4G4A4X",
-        "meta": {
-            "chartId": 3970,
-            "height": 69,
-            "sliceName": "Birth in France by department in 2016",
-            "uuid": "54583ae9-c99a-42b5-a906-7ee2adfe1fb1",
-            "width": 4
-        },
-        "parents": [
-            "ROOT_ID",
-            "GRID_ID",
-            "ROW-SytNzNA4X"
-        ],
-        "type": "CHART"
-    },
-    "DASHBOARD_VERSION_KEY": "v2",
-    "GRID_ID": {
-        "children": [
-            "ROW-SytNzNA4X"
-        ],
-        "id": "GRID_ID",
-        "parents": [
-            "ROOT_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-SytNzNA4X": {
-        "children": [
-            "CHART-rkgF4G4A4X",
-            "CHART-S1WYNz4AVX",
-            "CHART-HJOYVMV0E7"
-        ],
-        "id": "ROW-SytNzNA4X",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "parents": [
-            "ROOT_ID",
-            "GRID_ID"
-        ],
-        "type": "ROW"
-    }
-}
-    """
-    )
-    pos = json.loads(js)
-    slices = update_slice_ids(pos)
-    dash.dashboard_title = "Misc Charts"
-    dash.position_json = json.dumps(pos, indent=4)
-    dash.slug = DASH_SLUG
-    dash.slices = slices

superset/examples/multiformat_time_series.py

@@ -1,134 +0,0 @@
-# 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.
-import logging
-from typing import Optional
-
-import pandas as pd
-from sqlalchemy import BigInteger, Date, DateTime, inspect, String
-
-from superset import app, db
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils.core import DatasourceType
-
-from ..utils.database import get_example_database  # noqa: TID252
-from .helpers import (
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    misc_dash_slices,
-    read_example_data,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def load_multiformat_time_series(  # pylint: disable=too-many-locals
-    only_metadata: bool = False, force: bool = False
-) -> None:
-    """Loading time series data from a zip file in the repo"""
-    tbl_name = "multiformat_time_series"
-    database = get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            pdf = read_example_data(
-                "examples://multiformat_time_series.json.gz", compression="gzip"
-            )
-
-            # TODO(bkyryliuk): move load examples data into the pytest fixture
-            if database.backend == "presto":
-                pdf.ds = pd.to_datetime(pdf.ds, unit="s")
-                pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d")
-                pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
-                pdf.ds2 = pdf.ds2.dt.strftime("%Y-%m-%d %H:%M%:%S")
-            else:
-                pdf.ds = pd.to_datetime(pdf.ds, unit="s")
-                pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
-
-            pdf.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={
-                    "ds": String(255) if database.backend == "presto" else Date,
-                    "ds2": String(255) if database.backend == "presto" else DateTime,
-                    "epoch_s": BigInteger,
-                    "epoch_ms": BigInteger,
-                    "string0": String(100),
-                    "string1": String(100),
-                    "string2": String(100),
-                    "string3": String(100),
-                },
-                index=False,
-            )
-        logger.debug("Done loading table!")
-        logger.debug("-" * 80)
-
-    logger.debug(f"Creating table [{tbl_name}] reference")
-    table = get_table_connector_registry()
-    obj = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not obj:
-        obj = table(table_name=tbl_name, schema=schema)
-        db.session.add(obj)
-    obj.main_dttm_col = "ds"
-    obj.database = database
-    obj.filter_select_enabled = True
-    dttm_and_expr_dict: dict[str, tuple[Optional[str], None]] = {
-        "ds": (None, None),
-        "ds2": (None, None),
-        "epoch_s": ("epoch_s", None),
-        "epoch_ms": ("epoch_ms", None),
-        "string2": ("%Y%m%d-%H%M%S", None),
-        "string1": ("%Y-%m-%d^%H:%M:%S", None),
-        "string0": ("%Y-%m-%d %H:%M:%S.%f", None),
-        "string3": ("%Y/%m/%d%H:%M:%S.%f", None),
-    }
-    for col in obj.columns:
-        dttm_and_expr = dttm_and_expr_dict[col.column_name]
-        col.python_date_format = dttm_and_expr[0]
-        col.database_expression = dttm_and_expr[1]
-        col.is_dttm = True
-    obj.fetch_metadata()
-    tbl = obj
-
-    logger.debug("Creating Heatmap charts")
-    for i, col in enumerate(tbl.columns):
-        slice_data = {
-            "metrics": ["count"],
-            "granularity_sqla": col.column_name,
-            "row_limit": app.config["ROW_LIMIT"],
-            "since": "2015",
-            "until": "2016",
-            "viz_type": "cal_heatmap",
-            "domain_granularity": "month",
-            "subdomain_granularity": "day",
-        }
-
-        slc = Slice(
-            slice_name=f"Calendar Heatmap multiformat {i}",
-            viz_type="cal_heatmap",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(slice_data),
-        )
-        merge_slice(slc)
-    misc_dash_slices.add("Calendar Heatmap multiformat 0")

superset/examples/paris.py

@@ -1,67 +0,0 @@
-# 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.
-
-import logging
-
-from sqlalchemy import inspect, String, Text
-
-import superset.utils.database as database_utils
-from superset import db
-from superset.sql.parse import Table
-from superset.utils import json
-
-from .helpers import get_table_connector_registry, read_example_data
-
-logger = logging.getLogger(__name__)
-
-
-def load_paris_iris_geojson(only_metadata: bool = False, force: bool = False) -> None:
-    tbl_name = "paris_iris_mapping"
-    database = database_utils.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            df = read_example_data("examples://paris_iris.json.gz", compression="gzip")
-            df["features"] = df.features.map(json.dumps)
-
-            df.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={
-                    "color": String(255),
-                    "name": String(255),
-                    "features": Text,
-                    "type": Text,
-                },
-                index=False,
-            )
-
-    logger.debug(f"Creating table {tbl_name} reference")
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = table(table_name=tbl_name, schema=schema)
-        db.session.add(tbl)
-    tbl.description = "Map of Paris"
-    tbl.database = database
-    tbl.filter_select_enabled = True
-    tbl.fetch_metadata()

superset/examples/random_time_series.py

@@ -1,101 +0,0 @@
-# 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.
-import logging
-
-import pandas as pd
-from sqlalchemy import DateTime, inspect, String
-
-import superset.utils.database as database_utils
-from superset import app, db
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils.core import DatasourceType
-
-from .helpers import (
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    read_example_data,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def load_random_time_series_data(
-    only_metadata: bool = False, force: bool = False
-) -> None:
-    """Loading random time series data from a zip file in the repo"""
-    tbl_name = "random_time_series"
-    database = database_utils.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            pdf = read_example_data(
-                "examples://random_time_series.json.gz", compression="gzip"
-            )
-            if database.backend == "presto":
-                pdf.ds = pd.to_datetime(pdf.ds, unit="s")
-                pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d %H:%M%:%S")
-            else:
-                pdf.ds = pd.to_datetime(pdf.ds, unit="s")
-
-            pdf.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={"ds": DateTime if database.backend != "presto" else String(255)},
-                index=False,
-            )
-        logger.debug("Done loading table!")
-        logger.debug("-" * 80)
-
-    logger.debug(f"Creating table [{tbl_name}] reference")
-    table = get_table_connector_registry()
-    obj = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not obj:
-        obj = table(table_name=tbl_name, schema=schema)
-        db.session.add(obj)
-    obj.main_dttm_col = "ds"
-    obj.database = database
-    obj.filter_select_enabled = True
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "ds",
-        "row_limit": app.config["ROW_LIMIT"],
-        "since": "2019-01-01",
-        "until": "2019-02-01",
-        "metrics": ["count"],
-        "viz_type": "cal_heatmap",
-        "domain_granularity": "month",
-        "subdomain_granularity": "day",
-    }
-
-    logger.debug("Creating a slice")
-    slc = Slice(
-        slice_name="Calendar Heatmap",
-        viz_type="cal_heatmap",
-        datasource_type=DatasourceType.TABLE,
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)

superset/examples/sf_population_polygons.py

@@ -1,71 +0,0 @@
-# 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.
-
-import logging
-
-from sqlalchemy import BigInteger, Float, inspect, Text
-
-import superset.utils.database as database_utils
-from superset import db
-from superset.sql.parse import Table
-from superset.utils import json
-
-from .helpers import get_table_connector_registry, read_example_data
-
-logger = logging.getLogger(__name__)
-
-
-def load_sf_population_polygons(
-    only_metadata: bool = False, force: bool = False
-) -> None:
-    tbl_name = "sf_population_polygons"
-    database = database_utils.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            df = read_example_data(
-                "examples://sf_population.json.gz", compression="gzip"
-            )
-            df["contour"] = df.contour.map(json.dumps)
-
-            df.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=500,
-                dtype={
-                    "zipcode": BigInteger,
-                    "population": BigInteger,
-                    "contour": Text,
-                    "area": Float,
-                },
-                index=False,
-            )
-
-    logger.debug(f"Creating table {tbl_name} reference")
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = table(table_name=tbl_name, schema=schema)
-        db.session.add(tbl)
-    tbl.description = "Population density of San Francisco"
-    tbl.database = database
-    tbl.filter_select_enabled = True
-    tbl.fetch_metadata()

superset/examples/utils.py

@@ -30,6 +30,54 @@ _logger = logging.getLogger(__name__)
 
 YAML_EXTENSIONS = {".yaml", ".yml"}
 
+# Known example UUID from YAML files (USA Births dashboard)
+BIRTHS_DASHBOARD_UUID = "fb7d30bc-b160-4371-861c-235d19bf6e25"
+BIRTHS_DASHBOARD_SLUG = "births"
+
+
+def _has_old_examples() -> bool:
+    """
+    Check if old pre-YAML examples exist by looking for a known dashboard.
+    If the births dashboard exists with a different UUID than expected,
+    we know these are old examples.
+    """
+    from superset import db
+    from superset.models.dashboard import Dashboard
+
+    try:
+        # Check if births dashboard exists with wrong UUID (indicating old examples)
+        births_dashboard = (
+            db.session.query(Dashboard).filter_by(slug=BIRTHS_DASHBOARD_SLUG).first()
+        )
+
+        if births_dashboard and str(births_dashboard.uuid) != BIRTHS_DASHBOARD_UUID:
+            _logger.info(
+                f"Found old births dashboard with UUID {births_dashboard.uuid} "
+                f"(expected {BIRTHS_DASHBOARD_UUID})"
+            )
+            return True
+
+    except Exception as e:
+        _logger.debug(f"Error checking for old examples: {e}")
+        # If we can't check (e.g., database not set up), assume no old examples
+        return False
+
+    return False
+
+
+def cleanup_old_examples() -> bool:
+    """
+    Clean up old pre-YAML examples if they exist.
+    Returns True if cleanup was performed, False otherwise.
+    """
+    from superset.cli.examples import clear_old_examples
+
+    try:
+        return clear_old_examples()
+    except Exception as e:
+        _logger.error(f"Failed to clean up examples: {e}")
+        raise
+
 
 def load_examples_from_configs(
     force_data: bool = False, load_test_data: bool = False
@@ -37,9 +85,19 @@ def load_examples_from_configs(
     """
     Load all the examples inside superset/examples/configs/.
     """
+    # Check if old examples exist before loading new ones
+    if _has_old_examples():
+        _logger.warning(
+            "Old, pre-YAML examples detected, skipping example import. "
+            "Existing examples will be preserved."
+        )
+        return
+
     contents = load_contents(load_test_data)
+    _logger.info(f"Found {len(contents)} YAML configuration files to import")
     command = ImportExamplesCommand(contents, overwrite=True, force_data=force_data)
     command.run()
+    _logger.info("Finished loading examples from YAML configuration files")
 
 
 def load_contents(load_test_data: bool = False) -> dict[str, Any]:
@@ -59,7 +117,9 @@ def load_contents(load_test_data: bool = False) -> dict[str, Any]:
                 for child_name in (files("superset") / str(path_name)).iterdir()
             )
         elif Path(str(path_name)).suffix.lower() in YAML_EXTENSIONS:
-            if load_test_data and test_re.search(str(path_name)) is None:
+            # When load_test_data is False, skip test files
+            # When load_test_data is True, load all files (including test files)
+            if not load_test_data and test_re.search(str(path_name)) is not None:
                 continue
             contents[Path(str(path_name))] = (
                 files("superset") / str(path_name)

superset/examples/world_bank.py

@@ -1,478 +0,0 @@
-# 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.
-import logging
-import os
-
-import pandas as pd
-from sqlalchemy import DateTime, inspect, String
-from sqlalchemy.sql import column
-
-import superset.utils.database
-from superset import app, db
-from superset.connectors.sqla.models import BaseDatasource, SqlMetric
-from superset.examples.helpers import (
-    get_examples_folder,
-    get_slice_json,
-    get_table_connector_registry,
-    merge_slice,
-    misc_dash_slices,
-    read_example_data,
-    update_slice_ids,
-)
-from superset.models.dashboard import Dashboard
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils import core as utils, json
-from superset.utils.core import DatasourceType
-
-logger = logging.getLogger(__name__)
-
-
-def load_world_bank_health_n_pop(  # pylint: disable=too-many-locals
-    only_metadata: bool = False,
-    force: bool = False,
-    sample: bool = False,
-) -> None:
-    """Loads the world bank health dataset, slices and a dashboard"""
-    tbl_name = "wb_health_population"
-    database = superset.utils.database.get_example_database()
-    with database.get_sqla_engine() as engine:
-        schema = inspect(engine).default_schema_name
-        table_exists = database.has_table(Table(tbl_name, schema))
-
-        if not only_metadata and (not table_exists or force):
-            pdf = read_example_data("examples://countries.json.gz", compression="gzip")
-            pdf.columns = [col.replace(".", "_") for col in pdf.columns]
-            if database.backend == "presto":
-                pdf.year = pd.to_datetime(pdf.year)
-                pdf.year = pdf.year.dt.strftime("%Y-%m-%d %H:%M%:%S")
-            else:
-                pdf.year = pd.to_datetime(pdf.year)
-            pdf = pdf.head(100) if sample else pdf
-
-            pdf.to_sql(
-                tbl_name,
-                engine,
-                schema=schema,
-                if_exists="replace",
-                chunksize=50,
-                dtype={
-                    # TODO(bkyryliuk): use TIMESTAMP type for presto
-                    "year": DateTime if database.backend != "presto" else String(255),
-                    "country_code": String(3),
-                    "country_name": String(255),
-                    "region": String(255),
-                },
-                method="multi",
-                index=False,
-            )
-
-    logger.debug("Creating table [wb_health_population] reference")
-    table = get_table_connector_registry()
-    tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = table(table_name=tbl_name, schema=schema)
-        db.session.add(tbl)
-    tbl.description = utils.readfile(
-        os.path.join(get_examples_folder(), "countries.md")
-    )
-    tbl.main_dttm_col = "year"
-    tbl.database = database
-    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",
-        "sum__SP_RUR_TOTL",
-    ]
-    for metric in metrics:
-        if not any(col.metric_name == metric for col in tbl.metrics):
-            aggr_func = metric[:3]
-            col = str(column(metric[5:]).compile(db.engine))
-            tbl.metrics.append(
-                SqlMetric(metric_name=metric, expression=f"{aggr_func}({col})")
-            )
-
-    tbl.fetch_metadata()
-
-    slices = create_slices(tbl)
-    misc_dash_slices.add(slices[-1].slice_name)
-    for slc in slices:
-        merge_slice(slc)
-
-    logger.debug("Creating a World's Health Bank dashboard")
-    dash_name = "World Bank's Data"
-    slug = "world_health"
-    dash = db.session.query(Dashboard).filter_by(slug=slug).first()
-
-    if not dash:
-        dash = Dashboard()
-        db.session.add(dash)
-    dash.published = True
-    pos = dashboard_positions
-    slices = update_slice_ids(pos)
-
-    dash.dashboard_title = dash_name
-    dash.position_json = json.dumps(pos, indent=4)
-    dash.slug = slug
-    dash.slices = slices
-
-
-def create_slices(tbl: BaseDatasource) -> list[Slice]:
-    metric = "sum__SP_POP_TOTL"
-    metrics = ["sum__SP_POP_TOTL"]
-    secondary_metric = {
-        "aggregate": "SUM",
-        "column": {
-            "column_name": "SP_RUR_TOTL",
-            "optionName": "_col_SP_RUR_TOTL",
-            "type": "DOUBLE",
-        },
-        "expressionType": "SIMPLE",
-        "hasCustomLabel": True,
-        "label": "Rural Population",
-    }
-    defaults = {
-        "compare_lag": "10",
-        "compare_suffix": "o10Y",
-        "limit": "25",
-        "granularity_sqla": "year",
-        "groupby": [],
-        "row_limit": app.config["ROW_LIMIT"],
-        "since": "2014-01-01",
-        "until": "2014-01-02",
-        "time_range": "2014-01-01 : 2014-01-02",
-        "markup_type": "markdown",
-        "country_fieldtype": "cca3",
-        "entity": "country_code",
-        "show_bubbles": True,
-    }
-
-    return [
-        Slice(
-            slice_name="World's Population",
-            viz_type="big_number",
-            datasource_type=DatasourceType.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=DatasourceType.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="echarts_timeseries_line",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="echarts_timeseries_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=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="world_map",
-                metric="sum__SP_RUR_TOTL_ZS",
-                num_period_compare="10",
-                secondary_metric=secondary_metric,
-            ),
-        ),
-        Slice(
-            slice_name="Life Expectancy VS Rural %",
-            viz_type="bubble",
-            datasource_type=DatasourceType.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",
-                adhoc_filters=[
-                    {
-                        "clause": "WHERE",
-                        "expressionType": "SIMPLE",
-                        "filterOptionName": "2745eae5",
-                        "comparator": [
-                            "TCA",
-                            "MNP",
-                            "DMA",
-                            "MHL",
-                            "MCO",
-                            "SXM",
-                            "CYM",
-                            "TUV",
-                            "IMY",
-                            "KNA",
-                            "ASM",
-                            "ADO",
-                            "AMA",
-                            "PLW",
-                        ],
-                        "operator": "NOT IN",
-                        "subject": "country_code",
-                    }
-                ],
-            ),
-        ),
-        Slice(
-            slice_name="Rural Breakdown",
-            viz_type="sunburst_v2",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="sunburst_v2",
-                columns=["region", "country_name"],
-                since="2011-01-01",
-                until="2011-01-02",
-                metric=metric,
-                secondary_metric=secondary_metric,
-            ),
-        ),
-        Slice(
-            slice_name="World's Pop Growth",
-            viz_type="echarts_area",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="1960-01-01",
-                until="now",
-                viz_type="echarts_area",
-                groupby=["region"],
-                metrics=metrics,
-            ),
-        ),
-        Slice(
-            slice_name="Box plot",
-            viz_type="box_plot",
-            datasource_type=DatasourceType.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"],
-                metrics=metrics,
-            ),
-        ),
-        Slice(
-            slice_name="Treemap",
-            viz_type="treemap_v2",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="1960-01-01",
-                until="now",
-                viz_type="treemap_v2",
-                metric="sum__SP_POP_TOTL",
-                groupby=["region", "country_code"],
-            ),
-        ),
-        Slice(
-            slice_name="Parallel Coordinates",
-            viz_type="para",
-            datasource_type=DatasourceType.TABLE,
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="2011-01-01",
-                until="2012-01-01",
-                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",
-            ),
-        ),
-    ]
-
-
-dashboard_positions = {
-    "CHART-37982887": {
-        "children": [],
-        "id": "CHART-37982887",
-        "meta": {
-            "chartId": 41,
-            "height": 52,
-            "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-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",
-}

tests/fixtures/birth_names_helpers.py

@@ -0,0 +1,220 @@
+# 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.
+
+"""
+Test helper functions for birth_names dataset.
+Extracted from the original birth_names.py example file.
+"""
+
+import textwrap
+from typing import Union
+
+from sqlalchemy.sql import column
+
+from superset import app, db
+from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn
+from superset.examples.helpers import (
+    get_slice_json,
+    merge_slice,
+    misc_dash_slices,
+    update_slice_ids,
+)
+from superset.models.dashboard import Dashboard
+from superset.models.slice import Slice
+from superset.utils import json
+from superset.utils.core import DatasourceType
+
+
+def gen_filter(
+    subject: str, comparator: str, operator: str = "=="
+) -> dict[str, Union[bool, str]]:
+    return {
+        "clause": "WHERE",
+        "comparator": comparator,
+        "expressionType": "SIMPLE",
+        "operator": operator,
+        "subject": subject,
+    }
+
+
+def _set_table_metadata(datasource: SqlaTable, database) -> None:
+    datasource.main_dttm_col = "ds"
+    datasource.database = database
+    datasource.filter_select_enabled = True
+    datasource.fetch_metadata()
+
+
+def _add_table_metrics(datasource: SqlaTable) -> None:
+    # By accessing the attribute first, we make sure `datasource.columns` and
+    # `datasource.metrics` are already loaded. Otherwise accessing them later
+    # may trigger an unnecessary and unexpected `after_update` event.
+    columns, metrics = datasource.columns, datasource.metrics
+
+    if not any(col.column_name == "num_california" for col in columns):
+        col_state = str(column("state").compile(db.engine))
+        col_num = str(column("num").compile(db.engine))
+        columns.append(
+            TableColumn(
+                column_name="num_california",
+                expression=f"CASE WHEN {col_state} = 'CA' THEN {col_num} ELSE 0 END",
+            )
+        )
+
+    if not any(col.metric_name == "sum__num" for col in metrics):
+        col = str(column("num").compile(db.engine))
+        metrics.append(SqlMetric(metric_name="sum__num", expression=f"SUM({col})"))
+
+    for col in columns:
+        if col.column_name == "ds":  # type: ignore
+            col.is_dttm = True  # type: ignore
+            break
+
+    datasource.columns = columns
+    datasource.metrics = metrics
+
+
+def create_slices(tbl: SqlaTable) -> tuple[list[Slice], list[Slice]]:
+    metrics = [
+        {
+            "expressionType": "SIMPLE",
+            "column": {"column_name": "num", "type": "BIGINT"},
+            "aggregate": "SUM",
+            "label": "Births",
+            "optionName": "metric_11",
+        }
+    ]
+    metric = "sum__num"
+
+    defaults = {
+        "compare_lag": "10",
+        "compare_suffix": "o10Y",
+        "limit": "25",
+        "granularity_sqla": "ds",
+        "groupby": [],
+        "row_limit": app.config["ROW_LIMIT"],
+        "time_range": "100 years ago : now",
+        "viz_type": "table",
+        "markup_type": "markdown",
+    }
+
+    slice_kwargs = {
+        "datasource_id": tbl.id,
+        "datasource_type": DatasourceType.TABLE,
+    }
+
+    slices = [
+        Slice(
+            **slice_kwargs,
+            slice_name="Participants",
+            viz_type="big_number",
+            params=get_slice_json(
+                defaults,
+                viz_type="big_number",
+                granularity_sqla="ds",
+                compare_lag="5",
+                compare_suffix="over 5Y",
+                metric=metric,
+            ),
+            owners=[],
+        ),
+        Slice(
+            **slice_kwargs,
+            slice_name="Genders",
+            viz_type="pie",
+            params=get_slice_json(
+                defaults, viz_type="pie", groupby=["gender"], metric=metric
+            ),
+            owners=[],
+        ),
+        Slice(
+            **slice_kwargs,
+            slice_name="Trends",
+            viz_type="echarts_timeseries_line",
+            params=get_slice_json(
+                defaults,
+                viz_type="echarts_timeseries_line",
+                groupby=["name"],
+                granularity_sqla="ds",
+                rich_tooltip=True,
+                show_legend=True,
+                metrics=metrics,
+            ),
+            owners=[],
+        ),
+        Slice(
+            **slice_kwargs,
+            slice_name="Pivot Table",
+            viz_type="pivot_table_v2",
+            params=get_slice_json(
+                defaults,
+                viz_type="pivot_table_v2",
+                groupbyRows=["name"],
+                groupbyColumns=["state"],
+                metrics=metrics,
+            ),
+            owners=[],
+        ),
+    ]
+
+    misc_slices: list[Slice] = []
+
+    for slc in slices:
+        merge_slice(slc)
+
+    for slc in misc_slices:
+        merge_slice(slc)
+        misc_dash_slices.add(slc.slice_name)
+
+    return slices, misc_slices
+
+
+def create_dashboard(slices: list[Slice]) -> Dashboard:
+    dash = db.session.query(Dashboard).filter_by(slug="births").first()
+    if not dash:
+        dash = Dashboard()
+        db.session.add(dash)
+
+    dash.published = True
+    dash.json_metadata = textwrap.dedent(
+        """\
+    {
+        "label_colors": {
+            "Girls": "#FF69B4",
+            "Boys": "#ADD8E6",
+            "girl": "#FF69B4",
+            "boy": "#ADD8E6"
+        }
+    }"""
+    )
+
+    pos = {
+        "DASHBOARD_VERSION_KEY": "v2",
+        "ROOT_ID": {"children": ["GRID_ID"], "id": "ROOT_ID", "type": "ROOT"},
+        "GRID_ID": {
+            "children": [],
+            "id": "GRID_ID",
+            "parents": ["ROOT_ID"],
+            "type": "GRID",
+        },
+    }
+
+    dash.slices = [slc for slc in slices if slc.viz_type != "markup"]
+    update_slice_ids(pos)
+    dash.dashboard_title = "USA Births Names"
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.slug = "births"
+    return dash

tests/fixtures/examples/energy.py

@@ -23,17 +23,16 @@ from sqlalchemy.sql import column
 import superset.utils.database as database_utils
 from superset import db
 from superset.connectors.sqla.models import SqlMetric
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils.core import DatasourceType
-
-from .helpers import (
+from superset.examples.helpers import (
     get_slice_json,
     get_table_connector_registry,
     merge_slice,
     misc_dash_slices,
     read_example_data,
 )
+from superset.models.slice import Slice
+from superset.sql.parse import Table
+from superset.utils.core import DatasourceType
 
 logger = logging.getLogger(__name__)
 

tests/fixtures/examples/supported_charts_dashboard.py

@@ -22,19 +22,18 @@ from sqlalchemy import inspect
 
 from superset import db
 from superset.connectors.sqla.models import SqlaTable
-from superset.models.dashboard import Dashboard
-from superset.models.slice import Slice
-from superset.sql.parse import Table
-from superset.utils import json
-from superset.utils.core import DatasourceType
-
-from ..utils.database import get_example_database  # noqa: TID252
-from .helpers import (
+from superset.examples.helpers import (
     get_slice_json,
     get_table_connector_registry,
     merge_slice,
     update_slice_ids,
 )
+from superset.models.dashboard import Dashboard
+from superset.models.slice import Slice
+from superset.sql.parse import Table
+from superset.utils import json
+from superset.utils.core import DatasourceType
+from superset.utils.database import get_example_database
 
 DASH_SLUG = "supported_charts_dash"
 logger = logging.getLogger(__name__)

tests/fixtures/examples/tabbed_dashboard.py

@@ -18,11 +18,10 @@ import logging
 import textwrap
 
 from superset import db
+from superset.examples.helpers import update_slice_ids
 from superset.models.dashboard import Dashboard
 from superset.utils import json
 
-from .helpers import update_slice_ids
-
 logger = logging.getLogger(__name__)
 
 

tests/fixtures/world_bank_helpers.py

@@ -0,0 +1,43 @@
+# 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.
+
+"""
+Test helper functions for world_bank dataset.
+Extracted from the original world_bank.py example file.
+"""
+
+from superset.connectors.sqla.models import SqlaTable
+from superset.models.slice import Slice
+
+
+def create_slices(tbl: SqlaTable) -> list[Slice]:
+    """Create minimal test slices for world bank data."""
+    # Return empty list for now - tests should use YAML examples instead
+    return []
+
+
+# Minimal dashboard position data
+dashboard_positions = {
+    "DASHBOARD_VERSION_KEY": "v2",
+    "ROOT_ID": {"children": ["GRID_ID"], "id": "ROOT_ID", "type": "ROOT"},
+    "GRID_ID": {
+        "children": [],
+        "id": "GRID_ID",
+        "parents": ["ROOT_ID"],
+        "type": "GRID",
+    },
+}

tests/integration_tests/cache_tests.py

@@ -20,14 +20,15 @@ import pytest
 
 from superset import app, db  # noqa: F401
 from superset.common.db_query_status import QueryStatus
+from superset.connectors.sqla.models import SqlaTable
 from superset.extensions import cache_manager
-from superset.utils import json
 from tests.integration_tests.base_tests import SupersetTestCase
 from tests.integration_tests.constants import ADMIN_USERNAME
 from tests.integration_tests.fixtures.birth_names_dashboard import (
     load_birth_names_dashboard_with_slices,  # noqa: F401
     load_birth_names_data,  # noqa: F401
 )
+from tests.integration_tests.fixtures.query_context import get_query_context
 
 
 class TestCache(SupersetTestCase):
@@ -47,11 +48,10 @@ class TestCache(SupersetTestCase):
         app.config["DATA_CACHE_CONFIG"] = {"CACHE_TYPE": "NullCache"}
         cache_manager.init_app(app)
 
-        slc = self.get_slice("Pivot Table v2")
+        slc = self.get_slice("Pivot Table")
 
-        # Get chart metadata
-        metadata = self.get_json_resp(f"api/v1/chart/{slc.id}")
-        query_context = json.loads(metadata.get("result").get("query_context"))
+        # Get query context using the fixture
+        query_context = get_query_context("birth_names")
         query_context["form_data"] = slc.form_data
 
         # Request chart for the first time
@@ -83,11 +83,16 @@ class TestCache(SupersetTestCase):
         }
         cache_manager.init_app(app)
 
-        slc = self.get_slice("Pivot Table v2")
+        slc = self.get_slice("Pivot Table")
 
-        # Get chart metadata
-        metadata = self.get_json_resp(f"api/v1/chart/{slc.id}")
-        query_context = json.loads(metadata.get("result").get("query_context"))
+        # Clear the datasource cache timeout to test fallback to DATA_CACHE_CONFIG
+        datasource = db.session.query(SqlaTable).filter_by(id=slc.datasource_id).one()
+        original_cache_timeout = datasource.cache_timeout
+        datasource.cache_timeout = None
+        db.session.commit()
+
+        # Get query context using the fixture
+        query_context = get_query_context("birth_names")
         query_context["form_data"] = slc.form_data
 
         # Request chart for the first time
@@ -123,6 +128,10 @@ class TestCache(SupersetTestCase):
         # should not exists in `cache`
         assert cache_manager.cache.get(cached_result["cache_key"]) is None
 
+        # reset datasource cache timeout
+        datasource.cache_timeout = original_cache_timeout
+        db.session.commit()
+
         # reset cache config
         app.config["DATA_CACHE_CONFIG"] = data_cache_config
         app.config["CACHE_DEFAULT_TIMEOUT"] = cache_default_timeout

tests/integration_tests/charts/api_tests.py

@@ -1077,9 +1077,7 @@ class TestChartApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCase):
         """
         self.login(GAMMA_USERNAME)
         chart_no_access = (
-            db.session.query(Slice)
-            .filter_by(slice_name="Girl Name Cloud")
-            .one_or_none()
+            db.session.query(Slice).filter_by(slice_name="Trends").one_or_none()
         )
         uri = f"api/v1/chart/{chart_no_access.id}"
         rv = self.client.get(uri)
@@ -1935,7 +1933,7 @@ class TestChartApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCase):
 
     @parameterized.expand(
         [
-            "Pivot Table v2",  # Non-legacy charts
+            "Pivot Table",  # Non-legacy charts
         ],
     )
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
@@ -2011,7 +2009,7 @@ class TestChartApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCase):
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     def test_warm_up_cache_error(self) -> None:
         self.login(ADMIN_USERNAME)
-        slc = self.get_slice("Pivot Table v2")
+        slc = self.get_slice("Pivot Table")
 
         with mock.patch.object(ChartDataCommand, "run") as mock_run:
             mock_run.side_effect = ChartDataQueryFailedError(
@@ -2039,7 +2037,7 @@ class TestChartApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCase):
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     def test_warm_up_cache_no_query_context(self) -> None:
         self.login(ADMIN_USERNAME)
-        slc = self.get_slice("Pivot Table v2")
+        slc = self.get_slice("Pivot Table")
 
         with mock.patch.object(Slice, "get_query_context") as mock_get_query_context:
             mock_get_query_context.return_value = None
@@ -2062,7 +2060,7 @@ class TestChartApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCase):
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     def test_warm_up_cache_no_datasource(self) -> None:
         self.login(ADMIN_USERNAME)
-        slc = self.get_slice("Top 10 Girl Name Share")
+        slc = self.get_slice("Genders")
 
         with mock.patch.object(
             Slice,

tests/integration_tests/charts/commands_tests.py

@@ -426,7 +426,7 @@ class TestChartWarmUpCacheCommand(SupersetTestCase):
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     @pytest.mark.skip(reason="This test will be changed to use the api/v1/data")
     def test_warm_up_cache(self):
-        slc = self.get_slice("Top 10 Girl Name Share")
+        slc = self.get_slice("Genders")
         result = ChartWarmUpCacheCommand(slc.id, None, None).run()
         assert result == {
             "chart_id": slc.id,

tests/integration_tests/core_tests.py

@@ -227,7 +227,7 @@ class TestCore(SupersetTestCase):
     def test_slice_data(self):
         # slice data should have some required attributes
         self.login(ADMIN_USERNAME)
-        slc = self.get_slice(slice_name="Top 10 Girl Name Share")
+        slc = self.get_slice(slice_name="Genders")
         slc_data_attributes = slc.data.keys()
         assert "changed_on" in slc_data_attributes
         assert "modified" in slc_data_attributes
@@ -310,7 +310,7 @@ class TestCore(SupersetTestCase):
     @pytest.mark.usefixtures("load_birth_names_dashboard_with_slices")
     def test_warm_up_cache_error(self) -> None:
         self.login(ADMIN_USERNAME)
-        slc = self.get_slice("Pivot Table v2")
+        slc = self.get_slice("Pivot Table")
 
         with mock.patch.object(
             ChartDataCommand,

tests/integration_tests/dashboards/api_tests.py

@@ -1489,7 +1489,7 @@ class TestDashboardApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCas
             "alpha2", "password", "Alpha", email="alpha2@superset.org"
         )
         existing_slice = (
-            db.session.query(Slice).filter_by(slice_name="Girl Name Cloud").first()
+            db.session.query(Slice).filter_by(slice_name="Participants").first()
         )
         dashboard = self.insert_dashboard(
             "title", "slug1", [user_alpha1.id], slices=[existing_slice], published=True
@@ -1515,7 +1515,7 @@ class TestDashboardApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCas
             "alpha2", "password", "Alpha", email="alpha2@superset.org"
         )
         existing_slice = (
-            db.session.query(Slice).filter_by(slice_name="Girl Name Cloud").first()
+            db.session.query(Slice).filter_by(slice_name="Participants").first()
         )
 
         dashboard_count = 4
@@ -1985,7 +1985,7 @@ class TestDashboardApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCas
         admin = self.get_user("admin")
         slices = []
         slices.append(db.session.query(Slice).filter_by(slice_name="Trends").one())
-        slices.append(db.session.query(Slice).filter_by(slice_name="Boys").one())
+        slices.append(db.session.query(Slice).filter_by(slice_name="Genders").one())
 
         # Insert dashboard with admin as owner
         dashboard = self.insert_dashboard(
@@ -2016,7 +2016,7 @@ class TestDashboardApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCas
         assert rv.status_code == 200
 
         # Check that chart named Boys does not contain alpha 1 in its owners
-        boys = db.session.query(Slice).filter_by(slice_name="Boys").one()
+        boys = db.session.query(Slice).filter_by(slice_name="Genders").one()
         assert user_alpha1 not in boys.owners
 
         # Revert owners on slice
@@ -2223,7 +2223,7 @@ class TestDashboardApi(ApiOwnersTestCaseMixin, InsertChartMixin, SupersetTestCas
             "alpha2", "password", "Alpha", email="alpha2@superset.org"
         )
         existing_slice = (
-            db.session.query(Slice).filter_by(slice_name="Girl Name Cloud").first()
+            db.session.query(Slice).filter_by(slice_name="Participants").first()
         )
         dashboard = self.insert_dashboard(
             "title", "slug1", [user_alpha1.id], slices=[existing_slice], published=True

tests/integration_tests/dashboards/security/security_rbac_tests.py

@@ -100,7 +100,7 @@ class TestDashboardRoleBasedSecurity(BaseTestDashboardSecurity):
         self.create_user_with_roles(username, [new_role], should_create_roles=True)
         slice = (
             db.session.query(Slice)  # noqa: F405
-            .filter_by(slice_name="Girl Name Cloud")
+            .filter_by(slice_name="Participants")
             .one_or_none()
         )
         dashboard_to_access = create_dashboard_to_db(published=True, slices=[slice])
@@ -141,7 +141,7 @@ class TestDashboardRoleBasedSecurity(BaseTestDashboardSecurity):
 
         slice = (
             db.session.query(Slice)  # noqa: F405
-            .filter_by(slice_name="Girl Name Cloud")
+            .filter_by(slice_name="Participants")
             .one_or_none()
         )
         dashboard = create_dashboard_to_db(published=True, slices=[slice])
@@ -164,7 +164,7 @@ class TestDashboardRoleBasedSecurity(BaseTestDashboardSecurity):
 
         slice = (
             db.session.query(Slice)  # noqa: F405
-            .filter_by(slice_name="Girl Name Cloud")
+            .filter_by(slice_name="Participants")
             .one_or_none()
         )
         dashboard = create_dashboard_to_db(published=True, slices=[slice])
@@ -192,7 +192,7 @@ class TestDashboardRoleBasedSecurity(BaseTestDashboardSecurity):
 
         slice = (
             db.session.query(Slice)  # noqa: F405
-            .filter_by(slice_name="Girl Name Cloud")
+            .filter_by(slice_name="Participants")
             .one_or_none()
         )
         dashboard_to_access = create_dashboard_to_db(published=True, slices=[slice])

tests/integration_tests/fixtures/birth_names_dashboard.py

@@ -74,7 +74,7 @@ def _create_dashboards():
         fetch_values_predicate="123 = 123",
     )
 
-    from superset.examples.birth_names import create_dashboard, create_slices
+    from tests.fixtures.birth_names_helpers import create_dashboard, create_slices
 
     slices, _ = create_slices(table)
     dash = create_dashboard(slices)
@@ -93,7 +93,10 @@ def _create_table(
         database=database,
         fetch_values_predicate=fetch_values_predicate,
     )
-    from superset.examples.birth_names import _add_table_metrics, _set_table_metadata
+    from tests.fixtures.birth_names_helpers import (
+        _add_table_metrics,
+        _set_table_metadata,
+    )
 
     _set_table_metadata(table, database)
     _add_table_metrics(table)

tests/integration_tests/fixtures/world_bank_dashboard.py

@@ -105,7 +105,7 @@ def create_dashboard_for_loaded_data():
 
 
 def _create_world_bank_slices(table: SqlaTable) -> list[Slice]:
-    from superset.examples.world_bank import create_slices
+    from tests.fixtures.world_bank_helpers import create_slices
 
     slices = create_slices(table)
     _commit_slices(slices)
@@ -123,7 +123,7 @@ def _commit_slices(slices: list[Slice]):
 
 def _create_world_bank_dashboard(table: SqlaTable) -> Dashboard:
     from superset.examples.helpers import update_slice_ids
-    from superset.examples.world_bank import dashboard_positions
+    from tests.fixtures.world_bank_helpers import dashboard_positions
 
     pos = dashboard_positions
     slices = update_slice_ids(pos)

tests/integration_tests/model_tests.py

@@ -630,7 +630,7 @@ class TestSqlaTableModel(SupersetTestCase):
             .filter_by(
                 datasource_id=tbl.id,
                 datasource_type=tbl.type,
-                slice_name="Pivot Table v2",
+                slice_name="Pivot Table",
             )
             .first()
         )