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62b873e3da
* Add presto to the CI Sample test data Datetime conversion Sample test data Fix tests * TODO to switch to timestamps * Address feedback * Update requirements * Add TODOs Co-authored-by: bogdan kyryliuk <bogdankyryliuk@dropbox.com>
775 lines
23 KiB
Python
775 lines
23 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 json
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import textwrap
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from typing import Dict, Union
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import pandas as pd
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from sqlalchemy import DateTime, String
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from sqlalchemy.sql import column
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from superset import db, security_manager
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from superset.connectors.sqla.models import SqlMetric, TableColumn
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from superset.models.core import Database
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from superset.models.dashboard import Dashboard
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from superset.models.slice import Slice
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from superset.utils.core import get_example_database
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from .helpers import (
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config,
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get_example_data,
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get_slice_json,
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merge_slice,
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misc_dash_slices,
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TBL,
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update_slice_ids,
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)
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def gen_filter(
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subject: str, comparator: str, operator: str = "=="
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) -> Dict[str, Union[bool, str]]:
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return {
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"clause": "WHERE",
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"comparator": comparator,
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"expressionType": "SIMPLE",
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"operator": operator,
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"subject": subject,
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}
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def load_data(tbl_name: str, database: Database, sample: bool = False) -> None:
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pdf = pd.read_json(get_example_data("birth_names.json.gz"))
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# TODO(bkyryliuk): move load examples data into the pytest fixture
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if database.backend == "presto":
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pdf.ds = pd.to_datetime(pdf.ds, unit="ms")
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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="ms")
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pdf = pdf.head(100) if sample else pdf
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pdf.to_sql(
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tbl_name,
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database.get_sqla_engine(),
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if_exists="replace",
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chunksize=500,
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dtype={
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# TODO(bkyryliuk): use TIMESTAMP type for presto
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"ds": DateTime if database.backend != "presto" else String(255),
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"gender": String(16),
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"state": String(10),
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"name": String(255),
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},
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method="multi",
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index=False,
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)
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print("Done loading table!")
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print("-" * 80)
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def load_birth_names(
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only_metadata: bool = False, force: bool = False, sample: bool = False
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) -> None:
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"""Loading birth name dataset from a zip file in the repo"""
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# pylint: disable=too-many-locals
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tbl_name = "birth_names"
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database = get_example_database()
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table_exists = database.has_table_by_name(tbl_name)
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if not only_metadata and (not table_exists or force):
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load_data(tbl_name, database, sample=sample)
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obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
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if not obj:
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print(f"Creating table [{tbl_name}] reference")
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obj = TBL(table_name=tbl_name)
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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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if not any(col.column_name == "num_california" for col in obj.columns):
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col_state = str(column("state").compile(db.engine))
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col_num = str(column("num").compile(db.engine))
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obj.columns.append(
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TableColumn(
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column_name="num_california",
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expression=f"CASE WHEN {col_state} = 'CA' THEN {col_num} ELSE 0 END",
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)
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)
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if not any(col.metric_name == "sum__num" for col in obj.metrics):
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col = str(column("num").compile(db.engine))
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obj.metrics.append(SqlMetric(metric_name="sum__num", expression=f"SUM({col})"))
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db.session.commit()
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obj.fetch_metadata()
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tbl = obj
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metrics = [
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{
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"expressionType": "SIMPLE",
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"column": {"column_name": "num", "type": "BIGINT"},
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"aggregate": "SUM",
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"label": "Births",
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"optionName": "metric_11",
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}
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]
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metric = "sum__num"
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defaults = {
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"compare_lag": "10",
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"compare_suffix": "o10Y",
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"limit": "25",
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"granularity_sqla": "ds",
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"groupby": [],
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"row_limit": config["ROW_LIMIT"],
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"since": "100 years ago",
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"until": "now",
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"viz_type": "table",
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"markup_type": "markdown",
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}
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admin = security_manager.find_user("admin")
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print("Creating some slices")
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slices = [
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Slice(
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slice_name="Participants",
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viz_type="big_number",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="big_number",
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granularity_sqla="ds",
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compare_lag="5",
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compare_suffix="over 5Y",
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metric=metric,
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),
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),
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Slice(
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slice_name="Genders",
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viz_type="pie",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults, viz_type="pie", groupby=["gender"], metric=metric
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),
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),
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Slice(
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slice_name="Trends",
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viz_type="line",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="line",
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groupby=["name"],
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granularity_sqla="ds",
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rich_tooltip=True,
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show_legend=True,
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Genders by State",
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viz_type="dist_bar",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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adhoc_filters=[
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{
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"clause": "WHERE",
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"expressionType": "SIMPLE",
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"filterOptionName": "2745eae5",
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"comparator": ["other"],
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"operator": "NOT IN",
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"subject": "state",
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}
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],
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viz_type="dist_bar",
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metrics=[
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{
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"expressionType": "SIMPLE",
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"column": {"column_name": "sum_boys", "type": "BIGINT(20)"},
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"aggregate": "SUM",
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"label": "Boys",
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"optionName": "metric_11",
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},
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{
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"expressionType": "SIMPLE",
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"column": {"column_name": "sum_girls", "type": "BIGINT(20)"},
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"aggregate": "SUM",
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"label": "Girls",
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"optionName": "metric_12",
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},
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],
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groupby=["state"],
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),
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),
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Slice(
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slice_name="Girls",
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viz_type="table",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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groupby=["name"],
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adhoc_filters=[gen_filter("gender", "girl")],
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row_limit=50,
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timeseries_limit_metric="sum__num",
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Girl Name Cloud",
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viz_type="word_cloud",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="word_cloud",
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size_from="10",
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series="name",
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size_to="70",
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rotation="square",
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limit="100",
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adhoc_filters=[gen_filter("gender", "girl")],
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metric=metric,
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),
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),
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Slice(
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slice_name="Boys",
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viz_type="table",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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groupby=["name"],
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adhoc_filters=[gen_filter("gender", "boy")],
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row_limit=50,
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Boy Name Cloud",
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viz_type="word_cloud",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="word_cloud",
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size_from="10",
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series="name",
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size_to="70",
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rotation="square",
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limit="100",
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adhoc_filters=[gen_filter("gender", "boy")],
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metric=metric,
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),
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),
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Slice(
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slice_name="Top 10 Girl Name Share",
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viz_type="area",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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adhoc_filters=[gen_filter("gender", "girl")],
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comparison_type="values",
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groupby=["name"],
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limit=10,
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stacked_style="expand",
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time_grain_sqla="P1D",
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viz_type="area",
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x_axis_forma="smart_date",
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Top 10 Boy Name Share",
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viz_type="area",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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adhoc_filters=[gen_filter("gender", "boy")],
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comparison_type="values",
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groupby=["name"],
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limit=10,
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stacked_style="expand",
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time_grain_sqla="P1D",
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viz_type="area",
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x_axis_forma="smart_date",
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metrics=metrics,
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),
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),
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]
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misc_slices = [
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Slice(
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slice_name="Average and Sum Trends",
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viz_type="dual_line",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="dual_line",
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metric={
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"expressionType": "SIMPLE",
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"column": {"column_name": "num", "type": "BIGINT(20)"},
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"aggregate": "AVG",
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"label": "AVG(num)",
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"optionName": "metric_vgops097wej_g8uff99zhk7",
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},
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metric_2="sum__num",
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granularity_sqla="ds",
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Num Births Trend",
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viz_type="line",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(defaults, viz_type="line", metrics=metrics),
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),
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Slice(
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slice_name="Daily Totals",
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viz_type="table",
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datasource_type="table",
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datasource_id=tbl.id,
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created_by=admin,
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params=get_slice_json(
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defaults,
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groupby=["ds"],
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since="40 years ago",
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until="now",
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viz_type="table",
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metrics=metrics,
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),
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),
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Slice(
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slice_name="Number of California Births",
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viz_type="big_number_total",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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metric={
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"expressionType": "SIMPLE",
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"column": {
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"column_name": "num_california",
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"expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
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},
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"aggregate": "SUM",
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"label": "SUM(num_california)",
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},
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viz_type="big_number_total",
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granularity_sqla="ds",
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),
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),
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Slice(
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slice_name="Top 10 California Names Timeseries",
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viz_type="line",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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metrics=[
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{
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"expressionType": "SIMPLE",
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"column": {
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"column_name": "num_california",
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"expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
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},
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"aggregate": "SUM",
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"label": "SUM(num_california)",
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}
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],
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viz_type="line",
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granularity_sqla="ds",
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groupby=["name"],
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timeseries_limit_metric={
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"expressionType": "SIMPLE",
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"column": {
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"column_name": "num_california",
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"expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
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},
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"aggregate": "SUM",
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"label": "SUM(num_california)",
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},
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limit="10",
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),
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),
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Slice(
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slice_name="Names Sorted by Num in California",
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viz_type="table",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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metrics=metrics,
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groupby=["name"],
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row_limit=50,
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timeseries_limit_metric={
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"expressionType": "SIMPLE",
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"column": {
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"column_name": "num_california",
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"expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
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},
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"aggregate": "SUM",
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"label": "SUM(num_california)",
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},
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),
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),
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Slice(
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slice_name="Number of Girls",
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viz_type="big_number_total",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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metric=metric,
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viz_type="big_number_total",
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granularity_sqla="ds",
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adhoc_filters=[gen_filter("gender", "girl")],
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subheader="total female participants",
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),
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),
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Slice(
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slice_name="Pivot Table",
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viz_type="pivot_table",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(
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defaults,
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viz_type="pivot_table",
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groupby=["name"],
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columns=["state"],
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metrics=metrics,
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),
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),
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]
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for slc in slices:
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merge_slice(slc)
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for slc in misc_slices:
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merge_slice(slc)
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misc_dash_slices.add(slc.slice_name)
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print("Creating a dashboard")
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dash = db.session.query(Dashboard).filter_by(slug="births").first()
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if not dash:
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dash = Dashboard()
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db.session.add(dash)
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dash.published = True
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dash.json_metadata = textwrap.dedent(
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"""\
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{
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"label_colors": {
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"Girls": "#FF69B4",
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"Boys": "#ADD8E6",
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"girl": "#FF69B4",
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"boy": "#ADD8E6"
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}
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}"""
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)
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js = textwrap.dedent(
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# pylint: disable=line-too-long
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"""\
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{
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"CHART-6GdlekVise": {
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"children": [],
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"id": "CHART-6GdlekVise",
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"meta": {
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"chartId": 5547,
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"height": 50,
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"sliceName": "Top 10 Girl Name Share",
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"width": 5
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},
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|
"parents": [
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"ROOT_ID",
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"GRID_ID",
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"ROW-eh0w37bWbR"
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],
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|
"type": "CHART"
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},
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|
"CHART-6n9jxb30JG": {
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|
"children": [],
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"id": "CHART-6n9jxb30JG",
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"meta": {
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"chartId": 5540,
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"height": 36,
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"sliceName": "Genders by State",
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"width": 5
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},
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|
"parents": [
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"ROOT_ID",
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"GRID_ID",
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"ROW--EyBZQlDi"
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],
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"type": "CHART"
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},
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|
"CHART-Jj9qh1ol-N": {
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|
"children": [],
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|
"id": "CHART-Jj9qh1ol-N",
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"meta": {
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"chartId": 5545,
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"height": 50,
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"sliceName": "Boy Name Cloud",
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"width": 4
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},
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"parents": [
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"ROOT_ID",
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"GRID_ID",
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"ROW-kzWtcvo8R1"
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],
|
|
"type": "CHART"
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|
},
|
|
"CHART-ODvantb_bF": {
|
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"children": [],
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"id": "CHART-ODvantb_bF",
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"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"
|
|
}
|
|
}
|
|
""" # pylint: enable=line-too-long
|
|
)
|
|
pos = json.loads(js)
|
|
# 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.slices)
|
|
dash.dashboard_title = "USA Births Names"
|
|
dash.position_json = json.dumps(pos, indent=4)
|
|
dash.slug = "births"
|
|
db.session.commit()
|