superset random_time_series 源码

superset random_time_series 代码

文件路径：/superset/examples/random_time_series.py

# 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 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.utils.core import DatasourceType

from .helpers import (
    get_example_url,
    get_slice_json,
    get_table_connector_registry,
    merge_slice,
)


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()
    engine = database.get_sqla_engine()
    schema = inspect(engine).default_schema_name
    table_exists = database.has_table_by_name(tbl_name)

    if not only_metadata and (not table_exists or force):
        url = get_example_url("random_time_series.json.gz")
        pdf = pd.read_json(url, 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,
        )
        print("Done loading table!")
        print("-" * 80)

    print(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)
    obj.main_dttm_col = "ds"
    obj.database = database
    obj.filter_select_enabled = True
    db.session.merge(obj)
    db.session.commit()
    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",
    }

    print("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)

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