101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import logging
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import pandas as pd
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from sqlalchemy import DateTime, inspect, String
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import superset.utils.database as database_utils
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from superset import app, db
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from superset.models.slice import Slice
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from superset.sql_parse import Table
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from superset.utils.core import DatasourceType
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from .helpers import (
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get_example_url,
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get_slice_json,
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get_table_connector_registry,
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merge_slice,
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)
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logger = logging.getLogger(__name__)
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def load_random_time_series_data(
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only_metadata: bool = False, force: bool = False
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) -> None:
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"""Loading random time series data from a zip file in the repo"""
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tbl_name = "random_time_series"
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database = database_utils.get_example_database()
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with database.get_sqla_engine() as engine:
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schema = inspect(engine).default_schema_name
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table_exists = database.has_table(Table(tbl_name, schema))
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if not only_metadata and (not table_exists or force):
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url = get_example_url("random_time_series.json.gz")
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pdf = pd.read_json(url, compression="gzip")
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if database.backend == "presto":
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pdf.ds = pd.to_datetime(pdf.ds, unit="s")
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pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d %H:%M%:%S")
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else:
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pdf.ds = pd.to_datetime(pdf.ds, unit="s")
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pdf.to_sql(
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tbl_name,
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engine,
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schema=schema,
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if_exists="replace",
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chunksize=500,
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dtype={"ds": DateTime if database.backend != "presto" else String(255)},
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index=False,
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)
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logger.debug("Done loading table!")
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logger.debug("-" * 80)
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logger.debug(f"Creating table [{tbl_name}] reference")
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table = get_table_connector_registry()
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obj = db.session.query(table).filter_by(table_name=tbl_name).first()
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if not obj:
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obj = table(table_name=tbl_name, schema=schema)
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db.session.add(obj)
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obj.main_dttm_col = "ds"
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obj.database = database
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obj.filter_select_enabled = True
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obj.fetch_metadata()
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tbl = obj
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slice_data = {
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"granularity_sqla": "ds",
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"row_limit": app.config["ROW_LIMIT"],
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"since": "2019-01-01",
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"until": "2019-02-01",
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"metrics": ["count"],
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"viz_type": "cal_heatmap",
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"domain_granularity": "month",
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"subdomain_granularity": "day",
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}
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logger.debug("Creating a slice")
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slc = Slice(
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slice_name="Calendar Heatmap",
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viz_type="cal_heatmap",
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datasource_type=DatasourceType.TABLE,
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datasource_id=tbl.id,
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params=get_slice_json(slice_data),
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)
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merge_slice(slc)
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