From 2e48f6065a1611925817b3ca2c9b7d83f479fede Mon Sep 17 00:00:00 2001 From: Dmitry Dementev Date: Sun, 8 Feb 2026 00:18:03 +0300 Subject: [PATCH] =?UTF-8?q?feat:=20=D0=B4=D0=BE=D0=B1=D0=B0=D0=B2=D0=BB?= =?UTF-8?q?=D0=B5=D0=BD=20=D0=B4=D0=B0=D1=88=D0=B1=D0=BE=D1=80=D0=B4=20Sup?= =?UTF-8?q?erset=20=D0=B4=D0=BB=D1=8F=20e-commerce=20=D0=B0=D0=BD=D0=B0?= =?UTF-8?q?=D0=BB=D0=B8=D1=82=D0=B8=D0=BA=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 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) --- Makefile | 40 +- docker-compose.yml | 51 +- docs/SUPERSET_DASHBOARD.md | 262 ++++++++++ superset/create_dashboard.py | 471 ++++++++++++++++++ .../dashboards/ecommerce_analytics.zip.json | 201 ++++++++ superset/export_dashboard.py | 107 ++++ superset/init_superset.py | 221 ++++++++ 7 files changed, 1351 insertions(+), 2 deletions(-) create mode 100644 docs/SUPERSET_DASHBOARD.md create mode 100644 superset/create_dashboard.py create mode 100644 superset/dashboards/ecommerce_analytics.zip.json create mode 100644 superset/export_dashboard.py create mode 100644 superset/init_superset.py diff --git a/Makefile b/Makefile index 8b066ef..db375a0 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,13 @@ -.PHONY: up down clean ddl data transform reload-monitoring recover-monitoring +.PHONY: up down clean ddl data transform logs \ + reload-monitoring recover-monitoring \ + superset-init superset-dashboard superset-export superset-ui superset-restart COMPOSE ?= docker compose +# ============================================================================ +# Основные команды +# ============================================================================ + up: $(COMPOSE) up -d @@ -13,6 +19,13 @@ down: clean: $(COMPOSE) down -v --remove-orphans +logs: + $(COMPOSE) logs -f --tail=200 $(service) + +# ============================================================================ +# ETL Pipeline +# ============================================================================ + ddl: bash ./scripts/apply_clickhouse_ddl.sh @@ -49,3 +62,28 @@ recover-monitoring: @echo "=== Проверка targets ===" @curl -s http://localhost:9090/api/v1/targets | grep -o '"job":"[^"]*"' | sort | uniq @curl -s http://localhost:9090/api/v1/targets | grep -o '"health":"[^"]*"' | sort | uniq -c + +# ============================================================================ +# Superset команды +# ============================================================================ + +# Инициализация Superset (подключение к ClickHouse + датасеты) +superset-init: + $(COMPOSE) exec -T superset bash -c "python /app/superset_init/init_superset.py" + +# Создание дашборда с чартами +superset-dashboard: + $(COMPOSE) exec -T superset bash -c "python /app/superset_init/create_dashboard.py" + +# Экспорт дашборда в JSON +superset-export: + $(COMPOSE) exec -T superset bash -c "python /app/superset_init/export_dashboard.py" + +# Открыть Superset UI +superset-ui: + @echo "Superset доступен по адресу: http://localhost:8088" + @echo "Логин: admin / Пароль: admin" + +# Перезапуск Superset +superset-restart: + $(COMPOSE) restart superset diff --git a/docker-compose.yml b/docker-compose.yml index f4dd504..1614d0a 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -216,7 +216,7 @@ services: superset: # image: apache/superset - build: + build: context: . dockerfile: Dockerfile.superset restart: unless-stopped @@ -227,14 +227,63 @@ services: volumes: - superset_data:/var/lib/superset - superset_config:/app/superset_home + - ./superset:/app/superset_init:ro environment: - SUPERSET_SECRET_KEY=9wc5+erMt60+lxrXDf3RjeIR+zONpEFusO00Np7JzfliMTI1e+RXnHcQ - TZ=Europe/Moscow + - DATABASE_DB=superset + - DATABASE_HOST=postgres-metadata + - DATABASE_PASSWORD=airflow + - DATABASE_USER=airflow + - DATABASE_PORT=5432 + - DATABASE_DIALECT=postgresql healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8088/health"] interval: 30s timeout: 10s retries: 5 + depends_on: + superset-init: + condition: service_completed_successfully + + # Superset initialization service + superset-init: + build: + context: . + dockerfile: Dockerfile.superset + user: "0:0" + networks: + - cs_dwh + volumes: + - superset_data:/var/lib/superset + - superset_config:/app/superset_home + - ./superset:/app/superset_init:ro + environment: + - SUPERSET_SECRET_KEY=9wc5+erMt60+lxrXDf3RjeIR+zONpEFusO00Np7JzfliMTI1e+RXnHcQ + - TZ=Europe/Moscow + - DATABASE_DB=superset + - DATABASE_HOST=postgres-metadata + - DATABASE_PASSWORD=airflow + - DATABASE_USER=airflow + - DATABASE_PORT=5432 + - DATABASE_DIALECT=postgresql + command: > + bash -ceuo pipefail " + echo 'Waiting for PostgreSQL...' && + sleep 10 && + echo 'Initializing Superset DB...' && + superset db upgrade && + echo 'Creating admin user...' && + superset fab create-admin --username admin --password admin --firstname Superset --lastname Admin --email admin@example.org || true && + echo 'Initializing roles...' && + superset init && + echo 'Creating ClickHouse connection and datasets...' && + python /app/superset_init/init_superset.py || true && + echo 'Superset initialized successfully' + " + depends_on: + postgres-metadata: + condition: service_healthy # Kafka Exporter для мониторинга через Prometheus kafka-exporter: diff --git a/docs/SUPERSET_DASHBOARD.md b/docs/SUPERSET_DASHBOARD.md new file mode 100644 index 0000000..6248fa8 --- /dev/null +++ b/docs/SUPERSET_DASHBOARD.md @@ -0,0 +1,262 @@ +# Дашборд Superset для E-commerce Analytics + +Документация по настройке и использованию Superset дашборда для анализа кликстрима. + +--- + +## Быстрый старт + +### 1. Запуск инфраструктуры + +```bash +# Запуск всех сервисов +make up + +# Применение DDL в ClickHouse +make ddl + +# Загрузка данных в Kafka +make data + +# Запуск ETL-пайплайна (ODS → DDS → DM) +make transform +``` + +### 2. Инициализация Superset + +```bash +# Автоматическая инициализация (создание подключения и датасетов) +make superset-init + +# Создание дашборда с чартами +make superset-dashboard +``` + +### 3. Доступ к UI + +Откройте в браузере: http://localhost:8088 + +**Логин:** `admin` +**Пароль:** `admin` + +--- + +## Структура дашборда + +### Витрины данных (Datasets) + +| Витрина | Таблица ClickHouse | Описание | +|---------|-------------------|------------| +| **Events Enriched** | `dm.v_events_enriched` | Полная обогащённая витрина событий | +| **Daily Traffic** | `dm.v_daily_traffic` | Агрегаты по дням | +| **UTM Effectiveness** | `dm.v_utm_effectiveness` | Эффективность маркетинговых каналов | +| **Top Pages** | `dm.v_top_pages_daily` | Популярность страниц | +| **Session Overview** | `dm.v_session_overview` | Анализ сессий | +| **DQ Summary** | `dm.dq_summary` | Качество данных | + +### Чарты (Charts) + +#### KPI-блок (верх дашборда) +- **📊 Total Events** — общее количество событий +- **👤 Unique Users** — уникальные пользователи +- **🎯 Unique Sessions** — уникальные сессии (click_id) +- **📈 Avg Events/Session** — среднее количество событий на сессию + +#### Динамика трафика +- **📅 Events by Hour** — линейный график событий по часам +- **📱 Traffic by Device** — pie chart распределения по устройствам + +#### География +- **🌍 Geography Map** — world map с распределением по странам + +#### Маркетинг +- **🔗 UTM Effectiveness Table** — таблица эффективности UTM-меток +- **📄 Top Pages** — bar chart топ-20 страниц + +#### Качество данных +- **🔍 Data Quality Summary** — статистика по слоям STG/ODS/DDS + +### Фильтры (Native Filters) + +| Фильтр | Поле | Тип | Применение | +|--------|------|-----|------------| +| 📅 Date Range | `event_date` | Time Range | Все чарты | +| 🌍 Country | `geo_country` | Multi-select | Все чарты | +| 📱 Device Type | `device_type` | Multi-select | Все чарты | +| 🌐 Browser | `browser_name` | Multi-select | Все чарты | + +--- + +## Команды Makefile + +```bash +# Основные +make up # Запуск всех сервисов +make down # Остановка сервисов +make down-v # Остановка с удалением volumes +make logs service=superset # Логи сервиса + +# ETL +make ddl # Применение DDL в ClickHouse +make data # Загрузка данных в Kafka +make transform # Запуск batch-процесса + +# Superset +make superset-init # Инициализация (подключение + датасеты) +make superset-dashboard # Создание дашборда +make superset-export # Экспорт дашборда в JSON +make superset-ui # Показать URL и логин +make superset-restart # Перезапуск сервиса +``` + +--- + +## Ручная настройка (если автоматика не сработала) + +### Создание подключения к ClickHouse + +1. Откройте **Settings → Database Connections** +2. Нажмите **+ Database** +3. Выберите **ClickHouse** +4. Введите SQLAlchemy URI: + ``` + clickhouse+native://default@clickhouse:9000/default + ``` +5. Установите: + - **Expose in SQL Lab:** ✅ + - **Allow DDL:** ❌ +6. Нажмите **Connect** + +### Импорт датасетов + +```bash +# Внутри контейнера +docker compose exec superset bash +python /app/superset_init/init_superset.py +``` + +### Создание чартов вручную + +1. Перейдите в **Charts → + Chart** +2. Выберите датасет (например, `dm.v_events_enriched`) +3. Настройте визуализацию: + - **Viz Type:** Big Number / Line Chart / Pie Chart / World Map / Table + - **Metrics:** COUNT(*), COUNT(DISTINCT ...) + - **Dimensions:** группировки + - **Filters:** фильтры +4. Нажмите **Create Chart** + +### Создание дашборда + +1. **Dashboards → + Dashboard** +2. Назовите: "E-commerce Analytics Dashboard" +3. Добавьте чарты из списка +4. Настройте layout (drag-and-drop) +5. Добавьте Native Filters (фильтры вверху) +6. Сохраните + +--- + +## Экспорт и импорт дашборда + +### Экспорт + +```bash +# Автоматический экспорт в JSON +make superset-export + +# Результат: superset/dashboards/ecommerce_analytics.json +``` + +### Импорт + +```bash +# Импорт через CLI +docker compose exec superset superset import-dashboards -p /app/superset_init/dashboards/ecommerce_analytics.json + +# Или через UI: Settings → Import Dashboards +``` + +--- + +## Расширение дашборда + +### Добавление нового чарта + +1. Отредактируйте `superset/create_dashboard.py` +2. Добавьте конфигурацию в `CHARTS_CONFIG` +3. Запустите: `make superset-dashboard` + +Пример нового чарта: +```python +{ + "slice_name": "📊 My New Chart", + "viz_type": "echarts_bar", + "dataset_name": "v_events_enriched", + "params": { + "x_axis": "event_type", + "metrics": [{"sqlExpression": "COUNT(*)", "label": "Count"}], + "time_range": "No filter" + } +} +``` + +--- + +## Troubleshooting + +### Superset не стартует + +```bash +# Проверить логи +make logs service=superset + +# Перезапуск +make superset-restart + +# Полная переинициализация +docker compose down -v +docker compose up -d +make superset-init +``` + +### Нет данных в чартах + +```bash +# Проверить данные в ClickHouse +docker compose exec clickhouse clickhouse-client -q "SELECT count() FROM dm.v_events_enriched" + +# Перезапустить ETL +make transform +``` + +### Ошибка подключения к ClickHouse + +```bash +# Проверить доступность ClickHouse +docker compose exec superset bash -c "ping clickhouse" + +# Проверить порт +docker compose exec superset bash -c "curl clickhouse:8123" +``` + +--- + +## Порты сервисов + +| Сервис | URL | Логин/Пароль | +|--------|-----|--------------| +| Superset | http://localhost:8088 | admin / admin | +| ClickHouse HTTP | http://localhost:9123 | default / (пустой) | +| Airflow | http://localhost:8080 | admin / admin | +| Grafana | http://localhost:3000 | admin / admin | +| Prometheus | http://localhost:9090 | - | +| Kafka UI | http://localhost:8082 | - | + +--- + +## Дополнительные ресурсы + +- [Superset Documentation](https://superset.apache.org/docs/intro) +- [ClickHouse SQL Reference](https://clickhouse.com/docs/en/sql-reference) +- [ARCHITECTURE.md](./ARCHITECTURE.md) — архитектура хранилища diff --git a/superset/create_dashboard.py b/superset/create_dashboard.py new file mode 100644 index 0000000..e224237 --- /dev/null +++ b/superset/create_dashboard.py @@ -0,0 +1,471 @@ +#!/usr/bin/env python3 +""" +================================================================================ +Скрипт создания дашборда "E-commerce Analytics" в Superset +================================================================================ +Назначение: + - Создание чартов (Charts) на основе датасетов DM-слоя + - Создание дашборда с布局 и фильтрами + - Настройка native filters + +Запуск: + Внутри контейнера superset: + python /app/superset_init/create_dashboard.py + +Чарты которые создаются: + 1. KPI блок (4 Big Number): Total Events, Unique Users, Sessions, Avg/Sess + 2. Динамика: Events by Hour (Line), Traffic by Device (Pie) + 3. География: World Map по странам + 4. Маркетинг: UTM Source/Medium Table, Top Pages Bar + 5. Качество данных: DQ Summary Bar +================================================================================ +""" + +import os +import sys +import json +import logging +from datetime import datetime + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +sys.path.insert(0, '/app') + +try: + from superset.app import create_app + from superset.extensions import db, security_manager + from superset.connectors.sqla.models import SqlaTable + from superset.charts.data_access_layer import ChartDAO + from superset.dashboards.data_access_layer import DashboardDAO + from superset.charts.schemas import ChartPostSchema + from superset.dashboards.schemas import DashboardPostSchema + from superset.commands.chart.create import CreateChartCommand + from superset.commands.dashboard.create import CreateDashboardCommand + from superset.utils.core import DatasourceType +except ImportError as e: + logger.error(f"Failed to import Superset modules: {e}") + sys.exit(1) + + +# Конфигурация чартов +CHARTS_CONFIG = [ + # KPI блок + { + "slice_name": "📊 Total Events", + "viz_type": "big_number", + "dataset_name": "v_events_enriched", + "params": { + "metric": { + "expressionType": "SQL", + "sqlExpression": "COUNT(*)", + "column": None, + "aggregate": None, + "label": "Total Events", + "optionName": "metric_1" + }, + "y_axis_format": ",d", + "show_trend_line": False, + "time_range": "No filter" + } + }, + { + "slice_name": "👤 Unique Users", + "viz_type": "big_number", + "dataset_name": "v_events_enriched", + "params": { + "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" + } + }, + { + "slice_name": "🎯 Unique Sessions", + "viz_type": "big_number", + "dataset_name": "v_events_enriched", + "params": { + "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" + } + }, + { + "slice_name": "📈 Avg Events/Session", + "viz_type": "big_number", + "dataset_name": "v_events_enriched", + "params": { + "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" + } + }, + # Динамика + { + "slice_name": "📅 Events by Hour", + "viz_type": "echarts_timeseries_line", + "dataset_name": "v_events_enriched", + "params": { + "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 + } + }, + { + "slice_name": "📱 Traffic by Device", + "viz_type": "pie", + "dataset_name": "v_events_enriched", + "params": { + "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" + } + }, + # География + { + "slice_name": "🌍 Geography Map", + "viz_type": "world_map", + "dataset_name": "v_events_enriched", + "params": { + "entity": "geo_country", + "metric": { + "expressionType": "SQL", + "sqlExpression": "COUNT(*)", + "label": "Events" + }, + "row_limit": 500, + "linear_color_scheme": "blue_white_yellow", + "time_range": "No filter" + } + }, + # Маркетинг + { + "slice_name": "🔗 UTM Effectiveness Table", + "viz_type": "table", + "dataset_name": "v_utm_effectiveness", + "params": { + "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": None, + "operator": None, + "comparator": None + } + ] + } + }, + { + "slice_name": "📄 Top Pages", + "viz_type": "echarts_bar", + "dataset_name": "v_top_pages_daily", + "params": { + "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 + } + }, + # Качество данных + { + "slice_name": "🔍 Data Quality Summary", + "viz_type": "echarts_bar", + "dataset_name": "dq_summary", + "params": { + "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": None, + "operator": None, + "comparator": None + } + ], + "row_limit": 100, + "time_range": "No filter", + "show_legend": False + } + } +] + +# Конфигурация дашборда +DASHBOARD_CONFIG = { + "dashboard_title": "🛒 E-commerce Analytics Dashboard", + "description": "Аналитический дашборд для e-commerce кликстрима. Показывает трафик, конверсии, географию и качество данных.", + "published": True, + "slug": "ecommerce-analytics", + "json_metadata": json.dumps({ + "native_filter_configuration": [ + { + "id": "date_filter", + "name": "📅 Date Range", + "filterType": "filter_time", + "targets": [{"datasetId": None, "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": None, "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": None, "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": None, "column": {"name": "browser_name"}}], + "scope": {"root": ["ROOT_ID"], "excluded": []}, + "isInstant": True, + "allowsMultipleValues": True, + "isRequired": False + } + ], + "color_scheme": "supersetColors", + "label_colors": {} + }) +} + + +def get_dataset_by_name(app, dataset_name: str) -> SqlaTable: + """Получение датасета по имени таблицы""" + with app.app_context(): + dataset = db.session.query(SqlaTable).filter_by( + table_name=dataset_name, + schema="dm" + ).first() + return dataset + + +def create_chart(app, chart_config: dict, dataset: SqlaTable) -> Optional[dict]: + """Создание чарта""" + with app.app_context(): + try: + # Проверяем, существует ли чарт + from superset.charts.data_access_layer import ChartDAO + existing = ChartDAO.find_by_title(chart_config["slice_name"]) + + if existing: + logger.info(f"Chart '{chart_config['slice_name']}' already exists") + return {"id": existing.id, "title": existing.slice_name} + + # Подготавливаем параметры + params = chart_config["params"].copy() + params["datasource"] = f"{dataset.id}__{DatasourceType.TABLE.value}" + params["viz_type"] = chart_config["viz_type"] + + # Создаём чарт через команду + chart_data = { + "slice_name": chart_config["slice_name"], + "viz_type": chart_config["viz_type"], + "datasource_id": dataset.id, + "datasource_type": DatasourceType.TABLE.value, + "params": json.dumps(params), + "description": f"Chart created automatically for {chart_config['dataset_name']}" + } + + # Используем прямой SQL для создания + from superset.charts.commands.create import CreateChartCommand + + result = CreateChartCommand(chart_data).run() + logger.info(f"Created chart: {chart_config['slice_name']} (ID: {result.id})") + return {"id": result.id, "title": result.slice_name} + + except Exception as e: + logger.error(f"Failed to create chart '{chart_config['slice_name']}': {e}") + import traceback + traceback.print_exc() + return None + + +def create_dashboard(app, charts: list): + """Создание дашборда с чартами""" + with app.app_context(): + try: + # Проверяем, существует ли дашборд + from superset.dashboards.data_access_layer import DashboardDAO + existing = DashboardDAO.get_by_slug(DASHBOARD_CONFIG["slug"]) + + if existing: + logger.info(f"Dashboard '{DASHBOARD_CONFIG['dashboard_title']}' already exists") + return existing + + # Создаём позиции чартов для layout + positions = { + "DASHBOARD_VERSION_KEY": "v2" + } + + # Добавляем чарты в layout (grid: 12 columns) + # Row 1: KPI блок (4 чарта по 3 колонки) + # Row 2: Events by Hour (8) | Geography (4) + # Row 3: Traffic by Device (4) | Top Pages (8) + # Row 4: UTM Table (12) + # Row 5: DQ Summary (12) + + y_position = 0 + chart_index = 0 + + for chart in charts: + if chart: + positions[f"CHART-{chart['id']}"] = { + "id": f"CHART-{chart['id']}", + "type": "CHART", + "parents": ["ROOT_ID"], + "meta": { + "chartId": chart['id'], + "sliceName": chart['title'], + "height": 50, + "width": 4 if chart_index < 4 else 6, # KPI - по 4, остальные - по 6 + "x": (chart_index % 3) * 4 if chart_index < 4 else (chart_index % 2) * 6, + "y": y_position + } + } + chart_index += 1 + if chart_index % 4 == 0: + y_position += 50 + + # Создаём дашборд + dashboard_data = { + "dashboard_title": DASHBOARD_CONFIG["dashboard_title"], + "slug": DASHBOARD_CONFIG["slug"], + "description": DASHBOARD_CONFIG["description"], + "published": DASHBOARD_CONFIG["published"], + "json_metadata": DASHBOARD_CONFIG["json_metadata"], + "position_json": json.dumps(positions) + } + + from superset.dashboards.commands.create import CreateDashboardCommand + result = CreateDashboardCommand(dashboard_data).run() + + # Добавляем чарты к дашборду + from superset.dashboards.dao import DashboardDAO + dashboard = DashboardDAO.get_by_id(result.id) + + from superset.charts.dao import ChartDAO + for chart_info in charts: + if chart_info: + chart = ChartDAO.find_by_id(chart_info["id"]) + if chart: + dashboard.slices.append(chart) + + db.session.commit() + + logger.info(f"Created dashboard: {DASHBOARD_CONFIG['dashboard_title']} (ID: {result.id})") + return result + + except Exception as e: + logger.error(f"Failed to create dashboard: {e}") + import traceback + traceback.print_exc() + return None + + +def main(): + """Главная функция""" + logger.info("=" * 60) + logger.info("Creating E-commerce Analytics Dashboard") + logger.info("=" * 60) + + app = create_app() + + created_charts = [] + + # Создаём чарты + for chart_config in CHARTS_CONFIG: + dataset = get_dataset_by_name(app, chart_config["dataset_name"]) + if not dataset: + logger.warning(f"Dataset '{chart_config['dataset_name']}' not found, skipping chart") + continue + + chart = create_chart(app, chart_config, dataset) + if chart: + created_charts.append(chart) + + logger.info(f"Created {len(created_charts)} charts") + + # Создаём дашборд + 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() diff --git a/superset/dashboards/ecommerce_analytics.zip.json b/superset/dashboards/ecommerce_analytics.zip.json new file mode 100644 index 0000000..5a1211c --- /dev/null +++ b/superset/dashboards/ecommerce_analytics.zip.json @@ -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": "{}" + } + } + ] +} diff --git a/superset/export_dashboard.py b/superset/export_dashboard.py new file mode 100644 index 0000000..92395cc --- /dev/null +++ b/superset/export_dashboard.py @@ -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) diff --git a/superset/init_superset.py b/superset/init_superset.py new file mode 100644 index 0000000..1d4d3fe --- /dev/null +++ b/superset/init_superset.py @@ -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()