feat: добавлен дашборд Superset для e-commerce аналитики
- Добавлен сервис superset-init в docker-compose для автоматической инициализации - Созданы Python-скрипты для инициализации подключения ClickHouse и создания датасетов - Создан скрипт для автоматического создания дашборда с 10 чартами - Создан скрипт экспорта дашборда в JSON - Добавлен экспортируемый JSON дашборда (ecommerce_analytics.zip.json) - Обновлен Makefile с командами superset-init, superset-dashboard, superset-export - Добавлена документация docs/SUPERSET_DASHBOARD.md Дашборд включает: - KPI блок (Total Events, Unique Users, Sessions, Avg/Session) - Динамика трафика (Events by Hour, Traffic by Device) - География (World Map) - Маркетинг (UTM Effectiveness Table, Top Pages) - Качество данных (DQ Summary) - Native Filters (Date Range, Country, Device, Browser)
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#!/usr/bin/env python3
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"""
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================================================================================
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Скрипт создания дашборда "E-commerce Analytics" в Superset
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================================================================================
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Назначение:
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- Создание чартов (Charts) на основе датасетов DM-слоя
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- Создание дашборда с布局 и фильтрами
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- Настройка native filters
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Запуск:
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Внутри контейнера superset:
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python /app/superset_init/create_dashboard.py
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Чарты которые создаются:
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1. KPI блок (4 Big Number): Total Events, Unique Users, Sessions, Avg/Sess
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2. Динамика: Events by Hour (Line), Traffic by Device (Pie)
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3. География: World Map по странам
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4. Маркетинг: UTM Source/Medium Table, Top Pages Bar
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5. Качество данных: DQ Summary Bar
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================================================================================
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"""
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import os
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import sys
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import json
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import logging
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from datetime import datetime
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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sys.path.insert(0, '/app')
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try:
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from superset.app import create_app
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from superset.extensions import db, security_manager
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from superset.connectors.sqla.models import SqlaTable
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from superset.charts.data_access_layer import ChartDAO
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from superset.dashboards.data_access_layer import DashboardDAO
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from superset.charts.schemas import ChartPostSchema
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from superset.dashboards.schemas import DashboardPostSchema
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from superset.commands.chart.create import CreateChartCommand
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from superset.commands.dashboard.create import CreateDashboardCommand
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from superset.utils.core import DatasourceType
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except ImportError as e:
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logger.error(f"Failed to import Superset modules: {e}")
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sys.exit(1)
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# Конфигурация чартов
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CHARTS_CONFIG = [
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# KPI блок
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{
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"slice_name": "📊 Total Events",
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"viz_type": "big_number",
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"dataset_name": "v_events_enriched",
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"params": {
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(*)",
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"column": None,
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"aggregate": None,
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"label": "Total Events",
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"optionName": "metric_1"
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},
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"y_axis_format": ",d",
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"show_trend_line": False,
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"time_range": "No filter"
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}
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},
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{
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"slice_name": "👤 Unique Users",
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"viz_type": "big_number",
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"dataset_name": "v_events_enriched",
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"params": {
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(DISTINCT user_domain_id)",
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"label": "Unique Users",
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"optionName": "metric_2"
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},
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"y_axis_format": ",d",
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"show_trend_line": False,
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"time_range": "No filter"
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}
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},
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{
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"slice_name": "🎯 Unique Sessions",
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"viz_type": "big_number",
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"dataset_name": "v_events_enriched",
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"params": {
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(DISTINCT click_id)",
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"label": "Unique Sessions",
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"optionName": "metric_3"
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},
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"y_axis_format": ",d",
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"show_trend_line": False,
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"time_range": "No filter"
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}
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},
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{
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"slice_name": "📈 Avg Events/Session",
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"viz_type": "big_number",
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"dataset_name": "v_events_enriched",
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"params": {
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(*) / COUNT(DISTINCT click_id)",
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"label": "Avg Events/Session",
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"optionName": "metric_4"
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},
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"y_axis_format": ".2f",
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"show_trend_line": False,
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"time_range": "No filter"
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}
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},
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# Динамика
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{
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"slice_name": "📅 Events by Hour",
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"viz_type": "echarts_timeseries_line",
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"dataset_name": "v_events_enriched",
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"params": {
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"granularity_sqla": "event_ts",
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"time_grain_sqla": "PT1H",
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"metrics": [
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{
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"expressionType": "SQL",
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"sqlExpression": "COUNT(*)",
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"label": "Events"
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}
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],
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"groupby": [],
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"time_range": "Last week",
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"adhoc_filters": [],
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"row_limit": 10000
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}
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},
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{
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"slice_name": "📱 Traffic by Device",
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"viz_type": "pie",
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"dataset_name": "v_events_enriched",
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"params": {
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"groupby": ["device_type"],
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(*)",
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"label": "Count"
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},
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"row_limit": 100,
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"donut": True,
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"show_legend": True,
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"labels_outside": True,
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"time_range": "No filter"
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}
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},
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# География
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{
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"slice_name": "🌍 Geography Map",
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"viz_type": "world_map",
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"dataset_name": "v_events_enriched",
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"params": {
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"entity": "geo_country",
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"metric": {
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"expressionType": "SQL",
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"sqlExpression": "COUNT(*)",
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"label": "Events"
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},
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"row_limit": 500,
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"linear_color_scheme": "blue_white_yellow",
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"time_range": "No filter"
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}
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},
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# Маркетинг
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{
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"slice_name": "🔗 UTM Effectiveness Table",
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"viz_type": "table",
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"dataset_name": "v_utm_effectiveness",
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"params": {
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"groupby": ["utm_source", "utm_medium", "utm_campaign"],
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"metrics": [
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{"expressionType": "SQL", "sqlExpression": "SUM(clicks)", "label": "Clicks"},
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{"expressionType": "SQL", "sqlExpression": "SUM(uniq_users)", "label": "Users"},
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{"expressionType": "SQL", "sqlExpression": "SUM(uniq_sessions)", "label": "Sessions"}
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],
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"row_limit": 100,
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"time_range": "No filter",
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"adhoc_filters": [
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{
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"clause": "WHERE",
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"expressionType": "SQL",
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"sqlExpression": "utm_source IS NOT NULL",
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"subject": None,
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"operator": None,
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"comparator": None
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}
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]
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}
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},
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{
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"slice_name": "📄 Top Pages",
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"viz_type": "echarts_bar",
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"dataset_name": "v_top_pages_daily",
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"params": {
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"x_axis": "page_url_path",
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"metrics": [
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{"expressionType": "SQL", "sqlExpression": "SUM(pageviews)", "label": "Pageviews"}
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],
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"row_limit": 20,
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"order_by_cols": [["SUM(pageviews)", False]],
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"time_range": "No filter",
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"orientation": "vertical",
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"show_legend": False
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}
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},
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# Качество данных
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{
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"slice_name": "🔍 Data Quality Summary",
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"viz_type": "echarts_bar",
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"dataset_name": "dq_summary",
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"params": {
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"x_axis": "layer",
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"metrics": [
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{"expressionType": "SQL", "sqlExpression": "SUM(check_value)", "label": "Row Count"}
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],
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"adhoc_filters": [
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{
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"clause": "WHERE",
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"expressionType": "SQL",
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"sqlExpression": "check_name = 'total_rows'",
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"subject": None,
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"operator": None,
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"comparator": None
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}
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],
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"row_limit": 100,
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"time_range": "No filter",
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"show_legend": False
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}
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}
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]
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# Конфигурация дашборда
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DASHBOARD_CONFIG = {
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"dashboard_title": "🛒 E-commerce Analytics Dashboard",
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"description": "Аналитический дашборд для e-commerce кликстрима. Показывает трафик, конверсии, географию и качество данных.",
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"published": True,
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"slug": "ecommerce-analytics",
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"json_metadata": json.dumps({
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"native_filter_configuration": [
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{
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"id": "date_filter",
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"name": "📅 Date Range",
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"filterType": "filter_time",
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"targets": [{"datasetId": None, "column": {"name": "event_date"}}],
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"defaultValue": "Last week",
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"scope": {"root": ["ROOT_ID"], "excluded": []},
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"cascadeParentIds": [],
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"isInstant": True
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},
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{
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"id": "country_filter",
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"name": "🌍 Country",
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"filterType": "filter_select",
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"targets": [{"datasetId": None, "column": {"name": "geo_country"}}],
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"scope": {"root": ["ROOT_ID"], "excluded": []},
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"isInstant": True,
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"allowsMultipleValues": True,
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"isRequired": False
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},
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{
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"id": "device_filter",
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"name": "📱 Device Type",
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"filterType": "filter_select",
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"targets": [{"datasetId": None, "column": {"name": "device_type"}}],
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"scope": {"root": ["ROOT_ID"], "excluded": []},
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"isInstant": True,
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"allowsMultipleValues": True,
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"isRequired": False
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},
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{
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"id": "browser_filter",
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"name": "🌐 Browser",
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"filterType": "filter_select",
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"targets": [{"datasetId": None, "column": {"name": "browser_name"}}],
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"scope": {"root": ["ROOT_ID"], "excluded": []},
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"isInstant": True,
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"allowsMultipleValues": True,
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"isRequired": False
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}
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],
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"color_scheme": "supersetColors",
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"label_colors": {}
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})
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}
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def get_dataset_by_name(app, dataset_name: str) -> SqlaTable:
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"""Получение датасета по имени таблицы"""
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with app.app_context():
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dataset = db.session.query(SqlaTable).filter_by(
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table_name=dataset_name,
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schema="dm"
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).first()
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return dataset
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def create_chart(app, chart_config: dict, dataset: SqlaTable) -> Optional[dict]:
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"""Создание чарта"""
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with app.app_context():
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try:
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# Проверяем, существует ли чарт
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from superset.charts.data_access_layer import ChartDAO
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existing = ChartDAO.find_by_title(chart_config["slice_name"])
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if existing:
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logger.info(f"Chart '{chart_config['slice_name']}' already exists")
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return {"id": existing.id, "title": existing.slice_name}
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# Подготавливаем параметры
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params = chart_config["params"].copy()
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params["datasource"] = f"{dataset.id}__{DatasourceType.TABLE.value}"
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params["viz_type"] = chart_config["viz_type"]
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# Создаём чарт через команду
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chart_data = {
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"slice_name": chart_config["slice_name"],
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"viz_type": chart_config["viz_type"],
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"datasource_id": dataset.id,
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"datasource_type": DatasourceType.TABLE.value,
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"params": json.dumps(params),
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"description": f"Chart created automatically for {chart_config['dataset_name']}"
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}
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# Используем прямой SQL для создания
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from superset.charts.commands.create import CreateChartCommand
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result = CreateChartCommand(chart_data).run()
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logger.info(f"Created chart: {chart_config['slice_name']} (ID: {result.id})")
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return {"id": result.id, "title": result.slice_name}
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except Exception as e:
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logger.error(f"Failed to create chart '{chart_config['slice_name']}': {e}")
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import traceback
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traceback.print_exc()
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return None
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def create_dashboard(app, charts: list):
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"""Создание дашборда с чартами"""
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with app.app_context():
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try:
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# Проверяем, существует ли дашборд
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from superset.dashboards.data_access_layer import DashboardDAO
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existing = DashboardDAO.get_by_slug(DASHBOARD_CONFIG["slug"])
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if existing:
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logger.info(f"Dashboard '{DASHBOARD_CONFIG['dashboard_title']}' already exists")
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return existing
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# Создаём позиции чартов для layout
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positions = {
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"DASHBOARD_VERSION_KEY": "v2"
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}
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# Добавляем чарты в layout (grid: 12 columns)
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# Row 1: KPI блок (4 чарта по 3 колонки)
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# Row 2: Events by Hour (8) | Geography (4)
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# Row 3: Traffic by Device (4) | Top Pages (8)
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# Row 4: UTM Table (12)
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# Row 5: DQ Summary (12)
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y_position = 0
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chart_index = 0
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for chart in charts:
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if chart:
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positions[f"CHART-{chart['id']}"] = {
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"id": f"CHART-{chart['id']}",
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"type": "CHART",
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"parents": ["ROOT_ID"],
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"meta": {
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"chartId": chart['id'],
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"sliceName": chart['title'],
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"height": 50,
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"width": 4 if chart_index < 4 else 6, # KPI - по 4, остальные - по 6
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"x": (chart_index % 3) * 4 if chart_index < 4 else (chart_index % 2) * 6,
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"y": y_position
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}
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}
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chart_index += 1
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if chart_index % 4 == 0:
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y_position += 50
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# Создаём дашборд
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dashboard_data = {
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"dashboard_title": DASHBOARD_CONFIG["dashboard_title"],
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"slug": DASHBOARD_CONFIG["slug"],
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"description": DASHBOARD_CONFIG["description"],
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"published": DASHBOARD_CONFIG["published"],
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"json_metadata": DASHBOARD_CONFIG["json_metadata"],
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"position_json": json.dumps(positions)
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}
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from superset.dashboards.commands.create import CreateDashboardCommand
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result = CreateDashboardCommand(dashboard_data).run()
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# Добавляем чарты к дашборду
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from superset.dashboards.dao import DashboardDAO
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dashboard = DashboardDAO.get_by_id(result.id)
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from superset.charts.dao import ChartDAO
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for chart_info in charts:
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if chart_info:
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chart = ChartDAO.find_by_id(chart_info["id"])
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if chart:
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dashboard.slices.append(chart)
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db.session.commit()
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logger.info(f"Created dashboard: {DASHBOARD_CONFIG['dashboard_title']} (ID: {result.id})")
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return result
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except Exception as e:
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logger.error(f"Failed to create dashboard: {e}")
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import traceback
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traceback.print_exc()
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return None
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def main():
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"""Главная функция"""
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logger.info("=" * 60)
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logger.info("Creating E-commerce Analytics Dashboard")
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logger.info("=" * 60)
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app = create_app()
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created_charts = []
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# Создаём чарты
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for chart_config in CHARTS_CONFIG:
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dataset = get_dataset_by_name(app, chart_config["dataset_name"])
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if not dataset:
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logger.warning(f"Dataset '{chart_config['dataset_name']}' not found, skipping chart")
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continue
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chart = create_chart(app, chart_config, dataset)
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if chart:
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created_charts.append(chart)
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logger.info(f"Created {len(created_charts)} charts")
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# Создаём дашборд
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if created_charts:
|
||||
dashboard = create_dashboard(app, created_charts)
|
||||
if dashboard:
|
||||
logger.info("=" * 60)
|
||||
logger.info("Dashboard created successfully!")
|
||||
logger.info(f"Dashboard URL: /superset/dashboard/{dashboard.id}/")
|
||||
logger.info("=" * 60)
|
||||
else:
|
||||
logger.error("Failed to create dashboard")
|
||||
else:
|
||||
logger.error("No charts created, cannot create dashboard")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,201 @@
|
||||
{
|
||||
"dashboards": [
|
||||
{
|
||||
"__Dashboard__": {
|
||||
"dashboard_title": "🛒 E-commerce Analytics Dashboard",
|
||||
"description": "Аналитический дашборд для e-commerce кликстрима: трафик, конверсии, география и качество данных.",
|
||||
"slug": "ecommerce-analytics",
|
||||
"published": true,
|
||||
"json_metadata": "{\"native_filter_configuration\": [{\"id\": \"date_filter\", \"name\": \"📅 Date Range\", \"filterType\": \"filter_time\", \"targets\": [{\"datasetId\": null, \"column\": {\"name\": \"event_date\"}}], \"defaultValue\": \"Last week\", \"scope\": {\"root\": [\"ROOT_ID\"], \"excluded\": []}, \"cascadeParentIds\": [], \"isInstant\": true}, {\"id\": \"country_filter\", \"name\": \"🌍 Country\", \"filterType\": \"filter_select\", \"targets\": [{\"datasetId\": null, \"column\": {\"name\": \"geo_country\"}}], \"scope\": {\"root\": [\"ROOT_ID\"], \"excluded\": []}, \"isInstant\": true, \"allowsMultipleValues\": true, \"isRequired\": false}, {\"id\": \"device_filter\", \"name\": \"📱 Device Type\", \"filterType\": \"filter_select\", \"targets\": [{\"datasetId\": null, \"column\": {\"name\": \"device_type\"}}], \"scope\": {\"root\": [\"ROOT_ID\"], \"excluded\": []}, \"isInstant\": true, \"allowsMultipleValues\": true, \"isRequired\": false}, {\"id\": \"browser_filter\", \"name\": \"🌐 Browser\", \"filterType\": \"filter_select\", \"targets\": [{\"datasetId\": null, \"column\": {\"name\": \"browser_name\"}}], \"scope\": {\"root\": [\"ROOT_ID\"], \"excluded\": []}, \"isInstant\": true, \"allowsMultipleValues\": true, \"isRequired\": false}], \"color_scheme\": \"supersetColors\", \"label_colors\": {}}",
|
||||
"position_json": "{\"DASHBOARD_VERSION_KEY\": \"v2\", \"CHART-1\": {\"id\": \"CHART-1\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 1, \"sliceName\": \"📊 Total Events\", \"height\": 50, \"width\": 4, \"x\": 0, \"y\": 0}}, \"CHART-2\": {\"id\": \"CHART-2\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 2, \"sliceName\": \"👤 Unique Users\", \"height\": 50, \"width\": 4, \"x\": 4, \"y\": 0}}, \"CHART-3\": {\"id\": \"CHART-3\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 3, \"sliceName\": \"🎯 Unique Sessions\", \"height\": 50, \"width\": 4, \"x\": 8, \"y\": 0}}, \"CHART-4\": {\"id\": \"CHART-4\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 4, \"sliceName\": \"📈 Avg Events/Session\", \"height\": 50, \"width\": 4, \"x\": 0, \"y\": 50}}, \"CHART-5\": {\"id\": \"CHART-5\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 5, \"sliceName\": \"📅 Events by Hour\", \"height\": 50, \"width\": 8, \"x\": 0, \"y\": 100}}, \"CHART-6\": {\"id\": \"CHART-6\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 6, \"sliceName\": \"📱 Traffic by Device\", \"height\": 50, \"width\": 4, \"x\": 8, \"y\": 100}}, \"CHART-7\": {\"id\": \"CHART-7\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 7, \"sliceName\": \"🌍 Geography Map\", \"height\": 50, \"width\": 6, \"x\": 0, \"y\": 150}}, \"CHART-8\": {\"id\": \"CHART-8\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 8, \"sliceName\": \"🔗 UTM Effectiveness Table\", \"height\": 50, \"width\": 6, \"x\": 6, \"y\": 150}}, \"CHART-9\": {\"id\": \"CHART-9\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 9, \"sliceName\": \"📄 Top Pages\", \"height\": 50, \"width\": 6, \"x\": 0, \"y\": 200}}, \"CHART-10\": {\"id\": \"CHART-10\", \"type\": \"CHART\", \"parents\": [\"ROOT_ID\"], \"meta\": {\"chartId\": 10, \"sliceName\": \"🔍 Data Quality Summary\", \"height\": 50, \"width\": 6, \"x\": 6, \"y\": 200}}}",
|
||||
"slices": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||||
}
|
||||
}
|
||||
],
|
||||
"charts": [
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "📊 Total Events",
|
||||
"viz_type": "big_number",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"big_number\", \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(*)\", \"column\": null, \"aggregate\": null, \"label\": \"Total Events\", \"optionName\": \"metric_1\"}, \"y_axis_format\": \",d\", \"show_trend_line\": false, \"time_range\": \"No filter\"}",
|
||||
"description": "Общее количество событий"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "👤 Unique Users",
|
||||
"viz_type": "big_number",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"big_number\", \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(DISTINCT user_domain_id)\", \"label\": \"Unique Users\", \"optionName\": \"metric_2\"}, \"y_axis_format\": \",d\", \"show_trend_line\": false, \"time_range\": \"No filter\"}",
|
||||
"description": "Уникальные пользователи"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "🎯 Unique Sessions",
|
||||
"viz_type": "big_number",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"big_number\", \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(DISTINCT click_id)\", \"label\": \"Unique Sessions\", \"optionName\": \"metric_3\"}, \"y_axis_format\": \",d\", \"show_trend_line\": false, \"time_range\": \"No filter\"}",
|
||||
"description": "Уникальные сессии"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "📈 Avg Events/Session",
|
||||
"viz_type": "big_number",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"big_number\", \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(*) / COUNT(DISTINCT click_id)\", \"label\": \"Avg Events/Session\", \"optionName\": \"metric_4\"}, \"y_axis_format\": \".2f\", \"show_trend_line\": false, \"time_range\": \"No filter\"}",
|
||||
"description": "Среднее количество событий на сессию"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "📅 Events by Hour",
|
||||
"viz_type": "echarts_timeseries_line",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"echarts_timeseries_line\", \"granularity_sqla\": \"event_ts\", \"time_grain_sqla\": \"PT1H\", \"metrics\": [{\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(*)\", \"label\": \"Events\"}], \"groupby\": [], \"time_range\": \"Last week\", \"adhoc_filters\": [], \"row_limit\": 10000}",
|
||||
"description": "События по часам"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "📱 Traffic by Device",
|
||||
"viz_type": "pie",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"pie\", \"groupby\": [\"device_type\"], \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(*)\", \"label\": \"Count\"}, \"row_limit\": 100, \"donut\": true, \"show_legend\": true, \"labels_outside\": true, \"time_range\": \"No filter\"}",
|
||||
"description": "Распределение трафика по устройствам"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "🌍 Geography Map",
|
||||
"viz_type": "world_map",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_events_enriched",
|
||||
"params": "{\"datasource\": \"1__table\", \"viz_type\": \"world_map\", \"entity\": \"geo_country\", \"metric\": {\"expressionType\": \"SQL\", \"sqlExpression\": \"COUNT(*)\", \"label\": \"Events\"}, \"row_limit\": 500, \"linear_color_scheme\": \"blue_white_yellow\", \"time_range\": \"No filter\"}",
|
||||
"description": "География посетителей"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "🔗 UTM Effectiveness Table",
|
||||
"viz_type": "table",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_utm_effectiveness",
|
||||
"params": "{\"datasource\": \"2__table\", \"viz_type\": \"table\", \"groupby\": [\"utm_source\", \"utm_medium\", \"utm_campaign\"], \"metrics\": [{\"expressionType\": \"SQL\", \"sqlExpression\": \"SUM(clicks)\", \"label\": \"Clicks\"}, {\"expressionType\": \"SQL\", \"sqlExpression\": \"SUM(uniq_users)\", \"label\": \"Users\"}, {\"expressionType\": \"SQL\", \"sqlExpression\": \"SUM(uniq_sessions)\", \"label\": \"Sessions\"}], \"row_limit\": 100, \"time_range\": \"No filter\", \"adhoc_filters\": [{\"clause\": \"WHERE\", \"expressionType\": \"SQL\", \"sqlExpression\": \"utm_source IS NOT NULL\", \"subject\": null, \"operator\": null, \"comparator\": null}]}\n",
|
||||
"description": "Эффективность UTM-кампаний"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "📄 Top Pages",
|
||||
"viz_type": "echarts_bar",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.v_top_pages_daily",
|
||||
"params": "{\"datasource\": \"3__table\", \"viz_type\": \"echarts_bar\", \"x_axis\": \"page_url_path\", \"metrics\": [{\"expressionType\": \"SQL\", \"sqlExpression\": \"SUM(pageviews)\", \"label\": \"Pageviews\"}], \"row_limit\": 20, \"order_by_cols\": [[\"SUM(pageviews)\", false]], \"time_range\": \"No filter\", \"orientation\": \"vertical\", \"show_legend\": false}",
|
||||
"description": "Топ страниц по просмотрам"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__Slice__": {
|
||||
"slice_name": "🔍 Data Quality Summary",
|
||||
"viz_type": "echarts_bar",
|
||||
"datasource_type": "table",
|
||||
"datasource_name": "dm.dq_summary",
|
||||
"params": "{\"datasource\": \"4__table\", \"viz_type\": \"echarts_bar\", \"x_axis\": \"layer\", \"metrics\": [{\"expressionType\": \"SQL\", \"sqlExpression\": \"SUM(check_value)\", \"label\": \"Row Count\"}], \"adhoc_filters\": [{\"clause\": \"WHERE\", \"expressionType\": \"SQL\", \"sqlExpression\": \"check_name = 'total_rows'\", \"subject\": null, \"operator\": null, \"comparator\": null}], \"row_limit\": 100, \"time_range\": \"No filter\", \"show_legend\": false}",
|
||||
"description": "Сводка по качеству данных"
|
||||
}
|
||||
}
|
||||
],
|
||||
"datasets": [
|
||||
{
|
||||
"__SqlaTable__": {
|
||||
"table_name": "v_events_enriched",
|
||||
"schema": "dm",
|
||||
"database": "clickhouse_dwh",
|
||||
"description": "Полная обогащённая витрина событий (event + click)",
|
||||
"columns": [
|
||||
{"column_name": "event_id", "type": "UUID", "description": "UUID события"},
|
||||
{"column_name": "event_ts", "type": "DateTime64(6)", "description": "Время события"},
|
||||
{"column_name": "event_date", "type": "Date", "description": "Дата события"},
|
||||
{"column_name": "event_type", "type": "String", "description": "Тип события"},
|
||||
{"column_name": "click_id", "type": "UUID", "description": "ID сессии"},
|
||||
{"column_name": "user_domain_id", "type": "UUID", "description": "ID пользователя"},
|
||||
{"column_name": "device_type", "type": "String", "description": "Тип устройства"},
|
||||
{"column_name": "geo_country", "type": "String", "description": "Страна"},
|
||||
{"column_name": "browser_name", "type": "String", "description": "Браузер"},
|
||||
{"column_name": "utm_source", "type": "String", "description": "UTM Source"},
|
||||
{"column_name": "utm_medium", "type": "String", "description": "UTM Medium"},
|
||||
{"column_name": "page_url_path", "type": "String", "description": "Путь URL"}
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"__SqlaTable__": {
|
||||
"table_name": "v_utm_effectiveness",
|
||||
"schema": "dm",
|
||||
"database": "clickhouse_dwh",
|
||||
"description": "Эффективность UTM-кампаний",
|
||||
"columns": [
|
||||
{"column_name": "event_date", "type": "Date", "description": "Дата"},
|
||||
{"column_name": "utm_source", "type": "String", "description": "UTM Source"},
|
||||
{"column_name": "utm_medium", "type": "String", "description": "UTM Medium"},
|
||||
{"column_name": "utm_campaign", "type": "String", "description": "UTM Campaign"},
|
||||
{"column_name": "clicks", "type": "UInt64", "description": "Клики"},
|
||||
{"column_name": "uniq_users", "type": "UInt64", "description": "Уникальные пользователи"},
|
||||
{"column_name": "uniq_sessions", "type": "UInt64", "description": "Уникальные сессии"}
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"__SqlaTable__": {
|
||||
"table_name": "v_top_pages_daily",
|
||||
"schema": "dm",
|
||||
"database": "clickhouse_dwh",
|
||||
"description": "Популярность страниц по дням",
|
||||
"columns": [
|
||||
{"column_name": "event_date", "type": "Date", "description": "Дата"},
|
||||
{"column_name": "page_url_path", "type": "String", "description": "Путь URL"},
|
||||
{"column_name": "pageviews", "type": "UInt64", "description": "Просмотры"},
|
||||
{"column_name": "uniq_clicks", "type": "UInt64", "description": "Уникальные клики"}
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"__SqlaTable__": {
|
||||
"table_name": "dq_summary",
|
||||
"schema": "dm",
|
||||
"database": "clickhouse_dwh",
|
||||
"description": "Сводка по качеству данных",
|
||||
"columns": [
|
||||
{"column_name": "check_date", "type": "Date", "description": "Дата проверки"},
|
||||
{"column_name": "layer", "type": "String", "description": "Слой (stg/ods/dds)"},
|
||||
{"column_name": "table_name", "type": "String", "description": "Имя таблицы"},
|
||||
{"column_name": "check_name", "type": "String", "description": "Тип проверки"},
|
||||
{"column_name": "check_value", "type": "UInt64", "description": "Значение"}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"databases": [
|
||||
{
|
||||
"__Database__": {
|
||||
"database_name": "clickhouse_dwh",
|
||||
"sqlalchemy_uri": "clickhouse+native://default@clickhouse:9000/default",
|
||||
"expose_in_sqllab": true,
|
||||
"allow_ctas": false,
|
||||
"allow_cvas": false,
|
||||
"allow_dml": false,
|
||||
"allow_file_upload": false,
|
||||
"extra": "{}"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,107 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
================================================================================
|
||||
Экспорт дашборда Superset в JSON-формат
|
||||
================================================================================
|
||||
Назначение:
|
||||
- Экспорт созданного дашборда в JSON для версионирования
|
||||
- Формат совместимый с superset import-dashboards
|
||||
|
||||
Запуск:
|
||||
docker compose exec superset python /app/superset_init/export_dashboard.py
|
||||
================================================================================
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, '/app')
|
||||
|
||||
try:
|
||||
from superset.app import create_app
|
||||
from superset.dashboards.data_access_layer import DashboardDAO
|
||||
from superset.charts.data_access_layer import ChartDAO
|
||||
except ImportError as e:
|
||||
print(f"Error importing: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def export_dashboard(slug: str, output_path: str):
|
||||
"""Экспорт дашборда в JSON"""
|
||||
app = create_app()
|
||||
|
||||
with app.app_context():
|
||||
dashboard = DashboardDAO.get_by_slug(slug)
|
||||
|
||||
if not dashboard:
|
||||
print(f"Dashboard with slug '{slug}' not found")
|
||||
return False
|
||||
|
||||
# Собираем данные дашборда
|
||||
dashboard_data = {
|
||||
"dashboards": [
|
||||
{
|
||||
"__Dashboard__": {
|
||||
"dashboard_title": dashboard.dashboard_title,
|
||||
"description": dashboard.description,
|
||||
"slug": dashboard.slug,
|
||||
"json_metadata": dashboard.json_metadata,
|
||||
"position_json": dashboard.position_json,
|
||||
"published": dashboard.published,
|
||||
"slices": []
|
||||
}
|
||||
}
|
||||
],
|
||||
"charts": [],
|
||||
"datasets": []
|
||||
}
|
||||
|
||||
# Добавляем чарты
|
||||
for slice_obj in dashboard.slices:
|
||||
chart_data = {
|
||||
"__Slice__": {
|
||||
"slice_name": slice_obj.slice_name,
|
||||
"viz_type": slice_obj.viz_type,
|
||||
"params": slice_obj.params,
|
||||
"description": slice_obj.description,
|
||||
"datasource_type": slice_obj.datasource_type,
|
||||
"datasource_name": slice_obj.datasource.name if slice_obj.datasource else None
|
||||
}
|
||||
}
|
||||
dashboard_data["dashboards"][0]["__Dashboard__"]["slices"].append(slice_obj.id)
|
||||
dashboard_data["charts"].append(chart_data)
|
||||
|
||||
# Добавляем датасет
|
||||
if slice_obj.datasource:
|
||||
ds = slice_obj.datasource
|
||||
dataset_data = {
|
||||
"__SqlaTable__": {
|
||||
"table_name": ds.table_name,
|
||||
"schema": ds.schema,
|
||||
"database": ds.database.database_name if ds.database else None,
|
||||
"description": ds.description,
|
||||
"columns": [
|
||||
{
|
||||
"column_name": col.column_name,
|
||||
"type": col.type,
|
||||
"description": col.description
|
||||
}
|
||||
for col in ds.columns
|
||||
]
|
||||
}
|
||||
}
|
||||
# Добавляем уникальные датасеты
|
||||
if dataset_data not in dashboard_data["datasets"]:
|
||||
dashboard_data["datasets"].append(dataset_data)
|
||||
|
||||
# Сохраняем в файл
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(dashboard_data, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print(f"Dashboard exported to: {output_path}")
|
||||
return True
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
output_file = "/app/superset_init/dashboards/ecommerce_analytics.json"
|
||||
export_dashboard("ecommerce-analytics", output_file)
|
||||
@@ -0,0 +1,221 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
================================================================================
|
||||
Скрипт инициализации Superset для проекта ClickHouse Mini DWH
|
||||
================================================================================
|
||||
Назначение:
|
||||
- Создание подключения к ClickHouse (Database connection)
|
||||
- Импорт датасетов из витрин DM-слоя
|
||||
- Импорт чартов и дашбордов
|
||||
|
||||
Запуск:
|
||||
Внутри контейнера superset:
|
||||
python /app/superset_init/init_superset.py
|
||||
|
||||
Требования:
|
||||
- Запущенный ClickHouse с созданными витринами в схеме dm
|
||||
- Superset инициализирован (superset db upgrade, admin создан)
|
||||
================================================================================
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
# Настройка логирования
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Добавляем путь к superset
|
||||
sys.path.insert(0, '/app')
|
||||
|
||||
try:
|
||||
from superset.app import create_app
|
||||
from superset.extensions import db
|
||||
from superset.models.core import Database
|
||||
from superset.connectors.sqla.models import SqlaTable, TableColumn
|
||||
from superset.charts.data_access_layer import ChartDAO
|
||||
from superset.dashboards.data_access_layer import DashboardDAO
|
||||
from superset.commands.dataset.create import CreateDatasetCommand
|
||||
from sqlalchemy.exc import IntegrityError
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to import Superset modules: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Конфигурация подключения к ClickHouse
|
||||
CLICKHOUSE_CONFIG = {
|
||||
"database_name": "clickhouse_dwh",
|
||||
"sqlalchemy_uri": "clickhouse+native://default@clickhouse:9000/default",
|
||||
"expose_in_sqllab": True,
|
||||
"allow_ctas": False,
|
||||
"allow_cvas": False,
|
||||
"allow_dml": False,
|
||||
"allow_file_upload": False,
|
||||
"extra": json.dumps({
|
||||
"engine_params": {},
|
||||
"metadata_params": {},
|
||||
"schemas_allowed_for_file_upload": []
|
||||
})
|
||||
}
|
||||
|
||||
# Датасеты для импорта из DM-слоя
|
||||
DATASETS = [
|
||||
{
|
||||
"table_name": "v_events_enriched",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Полная обогащённая витрина событий (event + click)"
|
||||
},
|
||||
{
|
||||
"table_name": "v_daily_traffic",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Агрегация трафика по дням и измерениям"
|
||||
},
|
||||
{
|
||||
"table_name": "v_utm_effectiveness",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Эффективность UTM-кампаний"
|
||||
},
|
||||
{
|
||||
"table_name": "v_top_pages_daily",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Популярность страниц по дням"
|
||||
},
|
||||
{
|
||||
"table_name": "v_session_overview",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Обзор сессий пользователей"
|
||||
},
|
||||
{
|
||||
"table_name": "dq_summary",
|
||||
"schema": "dm",
|
||||
"database_name": "clickhouse_dwh",
|
||||
"description": "Сводка по качеству данных"
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
def create_clickhouse_connection(app) -> Optional[Database]:
|
||||
"""Создание подключения к ClickHouse"""
|
||||
with app.app_context():
|
||||
logger.info("Creating ClickHouse database connection...")
|
||||
|
||||
# Проверяем, существует ли уже подключение
|
||||
existing = db.session.query(Database).filter_by(
|
||||
database_name=CLICKHOUSE_CONFIG["database_name"]
|
||||
).first()
|
||||
|
||||
if existing:
|
||||
logger.info(f"Database connection '{CLICKHOUSE_CONFIG['database_name']}' already exists")
|
||||
return existing
|
||||
|
||||
try:
|
||||
database = Database(**CLICKHOUSE_CONFIG)
|
||||
db.session.add(database)
|
||||
db.session.commit()
|
||||
logger.info(f"Successfully created database connection: {CLICKHOUSE_CONFIG['database_name']}")
|
||||
return database
|
||||
except Exception as e:
|
||||
db.session.rollback()
|
||||
logger.error(f"Failed to create database connection: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def import_datasets(app):
|
||||
"""Импорт датасетов из DM-слоя"""
|
||||
with app.app_context():
|
||||
logger.info("Importing datasets...")
|
||||
|
||||
# Получаем ID базы данных
|
||||
database = db.session.query(Database).filter_by(
|
||||
database_name="clickhouse_dwh"
|
||||
).first()
|
||||
|
||||
if not database:
|
||||
logger.error("ClickHouse database connection not found")
|
||||
return False
|
||||
|
||||
imported_count = 0
|
||||
for dataset_config in DATASETS:
|
||||
try:
|
||||
# Проверяем, существует ли датасет
|
||||
existing = db.session.query(SqlaTable).filter_by(
|
||||
table_name=dataset_config["table_name"],
|
||||
schema=dataset_config["schema"]
|
||||
).first()
|
||||
|
||||
if existing:
|
||||
logger.info(f"Dataset '{dataset_config['table_name']}' already exists")
|
||||
continue
|
||||
|
||||
# Создаём датасет
|
||||
dataset = SqlaTable(
|
||||
table_name=dataset_config["table_name"],
|
||||
schema=dataset_config["schema"],
|
||||
database_id=database.id,
|
||||
database=database,
|
||||
description=dataset_config["description"],
|
||||
is_sqllab_view=False
|
||||
)
|
||||
|
||||
db.session.add(dataset)
|
||||
db.session.flush()
|
||||
|
||||
# Fetch columns from database
|
||||
dataset.fetch_metadata()
|
||||
|
||||
db.session.commit()
|
||||
logger.info(f"Successfully imported dataset: {dataset_config['table_name']}")
|
||||
imported_count += 1
|
||||
|
||||
except IntegrityError:
|
||||
db.session.rollback()
|
||||
logger.warning(f"Dataset '{dataset_config['table_name']}' already exists (integrity error)")
|
||||
except Exception as e:
|
||||
db.session.rollback()
|
||||
logger.error(f"Failed to import dataset '{dataset_config['table_name']}': {e}")
|
||||
|
||||
logger.info(f"Imported {imported_count} new datasets")
|
||||
return True
|
||||
|
||||
|
||||
def main():
|
||||
"""Главная функция инициализации"""
|
||||
logger.info("=" * 60)
|
||||
logger.info("Superset Initialization for ClickHouse Mini DWH")
|
||||
logger.info("=" * 60)
|
||||
|
||||
# Создаём приложение Superset
|
||||
app = create_app()
|
||||
|
||||
# Создаём подключение к ClickHouse
|
||||
database = create_clickhouse_connection(app)
|
||||
if not database:
|
||||
logger.error("Failed to create ClickHouse connection")
|
||||
sys.exit(1)
|
||||
|
||||
# Импортируем датасеты
|
||||
if not import_datasets(app):
|
||||
logger.error("Failed to import datasets")
|
||||
sys.exit(1)
|
||||
|
||||
logger.info("=" * 60)
|
||||
logger.info("Superset initialization completed successfully!")
|
||||
logger.info("=" * 60)
|
||||
logger.info("Available datasets:")
|
||||
for ds in DATASETS:
|
||||
logger.info(f" - {ds['schema']}.{ds['table_name']}")
|
||||
logger.info("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user