commit 977c60e579ac311bbb476c0968a55ea9557a8991 Author: Matteo Benedetto Date: Wed Dec 3 11:17:40 2025 +0100 Initial commit: Grafana + Elasticsearch POC con dashboard Activity e Tasks diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000..edf32ae --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,94 @@ +# Grafana & Elasticsearch Know-How + +## 1. Elasticsearch Datasource Provisioning (YAML) + +Quando si configura il datasource Elasticsearch via provisioning (es. `datasource.yml`), utilizzare il blocco `jsonData` per le configurazioni specifiche. + +**Campi Chiave in `jsonData`:** + +* **`index`**: Definisce il pattern dell'indice (es. `geodata`, `[logs-]YYYY.MM.DD`). **Nota:** Usare questo invece del campo top-level `database` che è deprecato. +* **`timeField`**: Il nome del campo timestamp (es. `timestamp` o `@timestamp`). +* **`esVersion`**: La versione del cluster Elasticsearch (es. `7.10.0`). +* **`interval`**: L'intervallo di tempo predefinito per i raggruppamenti (es. `Daily`, `Hourly`, `1m`). **Attenzione:** Non impostare a `None` o valori non validi, altrimenti Grafana potrebbe crashare all'avvio. +* **`maxConcurrentShardRequests`**: Limita le richieste concorrenti agli shard. + +**Esempio `datasource.yml`:** + +```yaml +apiVersion: 1 + +datasources: + - name: Elasticsearch + type: elasticsearch + uid: elasticsearch-uid + access: proxy + url: http://elasticsearch:9200 + isDefault: true + jsonData: + index: "geodata" + timeField: "timestamp" + esVersion: "7.10.0" + interval: "Daily" + maxConcurrentShardRequests: 5 +``` + +## 2. Grafana Geomap Panel con Elasticsearch + +Per visualizzare punti GeoJSON da Elasticsearch, si utilizza l'aggregazione **Geohash Grid**. + +**Configurazione Query:** + +1. **Query Type:** `Metric`. +2. **Metric:** `Count` (o altra metrica). +3. **Group By:** + * Type: `Geohash Grid` + * Field: Il campo mappato come `geo_point` (es. `location`). + * Precision: Livello di dettaglio (es. `5`). + +**Configurazione Pannello (JSON Model):** + +* **`geohashField`**: Nella configurazione del layer, specifica quale campo contiene l'hash. Solitamente è `key` quando si usa l'aggregazione. +* **`location.mode`**: Deve essere impostato su `geohash`. + +**Troubleshooting "Mappa Vuota":** + +* **Precisione:** Se troppo alta (es. 12) su una mappa zoomata indietro, i punti potrebbero non apparire. +* **Mapping:** Il campo in Elasticsearch DEVE essere di tipo `geo_point`. +* **Geohash Field:** Assicurarsi che nel pannello Geomap, sotto "Layer" -> "Location", il "Geohash field" sia impostato correttamente (spesso `key`). + +**Esempio JSON Dashboard (Snippet):** + +```json +{ + "type": "geomap", + "targets": [ + { + "bucketAggs": [ + { + "type": "geohash_grid", + "field": "location", + "settings": { "precision": "5" } + } + ], + "timeField": "timestamp" + } + ], + "options": { + "layers": [ + { + "location": { + "mode": "geohash", + "geohashField": "key" + } + } + ] + } +} +``` + +## 3. Troubleshooting & Best Practices + +* **Controllare sempre i log:** Prima di terminare un task o assumere che una fix funzioni, controllare sempre i log del container (es. `docker compose logs grafana`). Errori di provisioning o crash all'avvio sono spesso visibili solo lì. +* **Riavvio Container:** Le modifiche ai file di provisioning (`datasource.yml`, `dashboard.yml`) richiedono spesso il riavvio del container Grafana (`docker compose restart grafana`) per essere applicate. +* **Validazione YAML:** Assicurarsi che i file YAML siano validi e che l'indentazione sia corretta. +* **Datasource UID:** Se si riscontrano errori "Datasource provisioning error: data source not found", provare a rimuovere il campo `uid` dal file YAML del datasource e fare riferimento al datasource tramite il suo `name` (stringa) nel JSON della dashboard, invece che tramite l'oggetto `{ type: ..., uid: ... }`. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8259413 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +grafana_data/ +es_data/ +__pycache__/ +*.pyc +.env +*.log diff --git a/DOCUMENTAZIONE_GRAFANA_ES.md b/DOCUMENTAZIONE_GRAFANA_ES.md new file mode 100644 index 0000000..e4301da --- /dev/null +++ b/DOCUMENTAZIONE_GRAFANA_ES.md @@ -0,0 +1,197 @@ +# Dashboard Activity & Tasks su Elasticsearch + +**Autore:** Matteo Benedetto +**Email:** matteo.benedetto@e-geos.it +**Data:** 03/12/2025 + +--- + +## 1. Struttura dei dati in Elasticsearch + +### 1.1 Indice + +Tutti i dati sono indicizzati in Elasticsearch nell'indice: + +- **Nome indice:** `geodata` + +L'indice viene creato automaticamente dal servizio `data_loader` (contenitore Docker) oppure tramite lo script di inizializzazione usato nella fase di sviluppo. + +### 1.2 Mapping principale + +Il mapping dell’indice `geodata` è stato definito in modo esplicito per evitare problemi con i tipi dinamici e per supportare aggregazioni e visualizzazioni in Grafana. + +Campi principali: + +- `location` (`geo_point`) + - Coordinate geografiche del punto (latitudine/longitudine). +- `timestamp` (`date`) + - Data/ora dell’evento. +- `status` (`keyword`) + - Stato del ticket/attività. Valori possibili: `OPEN`, `CLOSED`, `IN_PROGRESS`, `ON_HOLD`. +- `timing_status` (`keyword`) + - Stato di puntualità, definito solo quando `status = CLOSED`. + - Valori possibili: `CLOSED_ON_TIME`, `CLOSED_LATE`. +- `lead_time` (`integer`) + - Tempo di lavorazione (in minuti) per le attività chiuse, usato per le medie nella dashboard. +- `operator` (`keyword`) + - Operatore responsabile (es. `Operator A`, `Operator B`, …). +- `duration` (`integer`) + - Durata generica dell’evento (valore grezzo usato solo come informativo). +- `task_type` (`keyword`) + - Tipo di task, usato nella dashboard **Tasks**. Valori esemplificativi: + - `Create Activity` + - `Close Activity` + - `Create WF` + - `Close WF` + - `Select DTO` + - `Report Generation` + - `Report QC` +- `min_duration` (`integer`) + - Durata minima simulata per il tipo di task (in secondi). +- `max_duration` (`integer`) + - Durata massima simulata per il tipo di task (in secondi). +- `avg_duration` (`integer`) + - Durata media simulata per il tipo di task (in secondi). +- `task_duration` (`integer`) + - Durata effettiva del singolo evento (in secondi). +- `id` (`integer`) + - Identificativo numerico univoco del record nella generazione mock. + +### 1.3 Generazione e caricamento dati + +- **Generazione dati**: file `generate_mock_data.py` + - Genera un file GeoJSON `data/sample.geojson` con 1000 feature. + - Popola per ogni feature: + - coordinate casuali in Europa, + - campi di stato (`status`, `timing_status`, `lead_time`), + - informazioni di task (`task_type`, durate, operatore, timestamp). +- **Caricamento in Elasticsearch**: file `loader/load_data.py` + - Attende che Elasticsearch sia disponibile. + - Crea l’indice `geodata` con il mapping descritto sopra. + - Legge `sample.geojson` e per ogni feature crea un documento con: + - `location.lat` / `location.lon` + - tutti i campi in `properties` (timestamp, status, operator, ecc.). + +--- + +## 2. Dashboard "Activity" + +La prima dashboard si chiama **"Activity"** ed è salvata in `grafana/dashboards/dashboard.json`. + +### 2.1 Pannelli principali + +1. **Activity Status** (table) + - **Datasource:** `Elasticsearch` + - **Query:** aggregazione `terms` sul campo `status` + metrica `count`. + - **Trasformazioni:** + - `organize` per rinominare i campi in `Status`, `Count`, `Avg Lead Time`. + - `calculateField` per calcolare il totale e la colonna `Proportion` (percentuale sul totale). + - **Colori:** override sul campo `Status` con testo colorato (OPEN giallo, CLOSED blu, IN_PROGRESS azzurro, ON_HOLD viola). + - **Uso:** fornisce il riepilogo delle attività per stato con la proporzione e il tempo medio di lavorazione. + +2. **Activity Distribution** (geomap heatmap) + - **Datasource:** `Elasticsearch` + - **Query:** metrica `raw_data` con `query: "*"` e `timeField: "timestamp"`. + - **Layer:** tipo `heatmap` con: + - `location.mode = "coords"` + - `latitude = "location.lat"`, `longitude = "location.lon"` + - **Uso:** visualizza la distribuzione geografica degli eventi come heatmap sull’Europa. + +3. **Timing Status** (table) + - **Datasource:** `Elasticsearch` + - **Query:** aggregazione `terms` su `timing_status` + metrica `count`. + - **Colori:** + - `CLOSED_ON_TIME` con background verde. + - `CLOSED_LATE` con background arancione. + - **Uso:** mostra il numero di attività chiuse in tempo vs in ritardo. + +4. **On-Time vs Late** (pie chart) + - **Datasource:** `Elasticsearch` + - **Query:** identica a `Timing Status` (terms su `timing_status`). + - **Trasformazioni:** + - trasformazione per convertire i risultati dell’aggregazione in valori per il pie chart. + - **Uso:** rappresentazione grafica (torta) della ripartizione CLOSED_ON_TIME vs CLOSED_LATE. + +5. **Activity Over Time** (time series) + - **Datasource:** `Elasticsearch` + - **Query:** `date_histogram` su `timestamp` + metrica `count`. + - **Uso:** andamento temporale del numero di eventi. + +--- + +## 3. Dashboard "Tasks" + +La seconda dashboard si chiama **"Tasks"** ed è salvata in `grafana/dashboards/tasks_dashboard.json`. + +### 3.1 Tasks (tabella in alto a sinistra) + +- **Datasource:** `Elasticsearch` +- **Query:** + - `terms` su `task_type` (raggruppamento per tipo di task). + - Metriche: + - `count` (Total) + - `min` su `task_duration` (Min Duration) + - `max` su `task_duration` (Max Duration) +- **Trasformazioni:** + - `organize` per rinominare i campi: + - `task_type → Task` + - `Count → Total` + - `Min task_duration → Min Duration` + - `Max task_duration → Max Duration` +- **Colori:** testo del campo `Task` colorato in azzurro. +- **Uso:** riepilogo per tipo di task con numero totale e range di durata. + +### 3.2 Tasks by Operator (grafico a barre orizzontali) + +- **Datasource:** `Elasticsearch` +- **Query:** + - Primo livello: `terms` su `operator` (asse Y del grafico). + - Secondo livello: `terms` su `task_type` (serie colorate). + - Metrica: `count`. +- **Trasformazioni:** + - `groupingToMatrix` (o equivalente) per trasformare l’output dell’aggregazione in una matrice con: + - `rowField = operator` + - `columnField = task_type` + - `valueField = Count` +- **Opzioni grafiche:** + - `orientation = horizontal`. + - `stacking = normal` per avere barre impilate per operatore. + - Legenda a destra con un colore per ogni tipo di task (`Create Activity`, `Close Activity`, `Create WF`, `Close WF`, `Select DTO`, `Report Generation`, `Report QC`). +- **Uso:** mostra per ogni operatore il numero di task, suddivisi per tipologia come barre orizzontali impilate. + +### 3.3 Tasks (tabella in basso) + +- **Datasource:** `Elasticsearch` +- **Query:** + - `terms` su `task_type` e `operator` (raggruppamento per Task + Operator). + - Metriche: + - `count` (Total) + - `min`/`max`/`avg` su `task_duration`. +- **Trasformazioni:** + - `organize` per ordinare le colonne e rinominare i campi in: + - `Task`, `Operator`, `Total`, `Min`, `Max`, `Average`. +- **Unità di misura:** + - Durate espresse in secondi, visualizzate in Grafana come secondi/minuti (in base al formato scelto nella colonna). +- **Uso:** tabella dettagliata per tipo di task e operatore, con statistiche di durata. + +--- + +## 4. Note operative + +- **Provisioning Grafana** + - I file delle dashboard sono montati nel container Grafana tramite Docker Compose. + - Ogni modifica ai file JSON (`dashboard.json`, `tasks_dashboard.json`) richiede un **riavvio del container Grafana** (`docker compose restart grafana`). + +- **Datasource Elasticsearch** + - È definito tramite provisioning (file `datasource.yml`) usando: + - `jsonData.index = "geodata"` + - `jsonData.timeField = "timestamp"` + - Non viene usata la `uid` del datasource nelle dashboard: i pannelli si riferiscono al datasource tramite `"datasource": "Elasticsearch"` per evitare problemi di provisioning. + +- **Rigenerazione dati** + - Per rigenerare i dati mock: + 1. Eseguire `python3 generate_mock_data.py` nella root del progetto. + 2. Cancellare l’indice `geodata` in Elasticsearch. + 3. Riavviare il container `data_loader` in modo che ricrei l’indice con il mapping corretto e ricarichi `sample.geojson`. + +Questo documento descrive lo stato corrente della soluzione (stack Docker Elasticsearch + Grafana + loader Python) usata per visualizzare dati geospaziali e metriche di attività/task nelle dashboard **Activity** e **Tasks**. \ No newline at end of file diff --git a/DOCUMENTAZIONE_GRAFANA_ES.pdf b/DOCUMENTAZIONE_GRAFANA_ES.pdf new file mode 100644 index 0000000..9a9ce45 Binary files /dev/null and b/DOCUMENTAZIONE_GRAFANA_ES.pdf differ diff --git a/data/sample.geojson b/data/sample.geojson new file mode 100644 index 0000000..473f37c --- /dev/null +++ b/data/sample.geojson @@ -0,0 +1,22513 @@ +{ + "type": "FeatureCollection", + "features": [ + { + "type": "Feature", + "properties": { + "status": "OPEN", + "operator": "Operator C", + "duration": 248, + "timestamp": "2025-12-03T08:28:57.086047", + "id": 0, + "task_type": "Close Activity", + "min_duration": 23, + "max_duration": 54, + "avg_duration": 48, + "task_duration": 31 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 4.205250329715531, + 40.37926697490205 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "OPEN", + "operator": "Operator A", + "duration": 168, + "timestamp": "2025-11-27T07:18:30.086047", + "id": 1, + "task_type": "Create WF", + "min_duration": 11, + "max_duration": 51, + "avg_duration": 26, + "task_duration": 56 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 5.389664614318711, + 38.50251730068715 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "IN_PROGRESS", + "operator": "Operator C", + "duration": 30, + "timestamp": "2025-12-01T09:25:09.086047", + "id": 2, + "task_type": "Create WF", + "min_duration": 18, + "max_duration": 45, + "avg_duration": 40, + "task_duration": 57 + }, + "geometry": { + "type": "Point", + "coordinates": [ + -9.610107354720894, + 38.58991184712626 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "ON_HOLD", + "operator": "Operator B", + "duration": 125, + "timestamp": "2025-11-30T20:24:28.086047", + "id": 3, + "task_type": "Select DTO", + "min_duration": 25, + "max_duration": 44, + "avg_duration": 34, + "task_duration": 47 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 23.76109509166981, + 58.93836039128866 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "OPEN", + "operator": "Operator C", + "duration": 169, + "timestamp": "2025-11-30T01:13:29.086047", + "id": 4, + "task_type": "Create WF", + "min_duration": 25, + "max_duration": 57, + "avg_duration": 33, + "task_duration": 37 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 21.50269559677445, + 42.00287216974499 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "CLOSED", + "operator": "Operator C", + "duration": 156, + "timestamp": "2025-11-27T18:10:28.086047", + "id": 5, + "task_type": "Create WF", + "min_duration": 12, + 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+ "operator": "Operator D", + "duration": 167, + "timestamp": "2025-12-01T00:51:06.086047", + "id": 997, + "task_type": "Close WF", + "min_duration": 19, + "max_duration": 55, + "avg_duration": 25, + "task_duration": 31 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 23.07873478683517, + 58.08418022246145 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "IN_PROGRESS", + "operator": "Operator A", + "duration": 273, + "timestamp": "2025-11-30T01:24:02.086047", + "id": 998, + "task_type": "Create Activity", + "min_duration": 22, + "max_duration": 44, + "avg_duration": 31, + "task_duration": 27 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 17.51954401762571, + 50.582463134805494 + ] + } + }, + { + "type": "Feature", + "properties": { + "status": "IN_PROGRESS", + "operator": "Operator D", + "duration": 79, + "timestamp": "2025-11-28T12:58:19.086047", + "id": 999, + "task_type": "Close WF", + "min_duration": 15, + "max_duration": 51, + "avg_duration": 45, + "task_duration": 25 + }, + "geometry": { + "type": "Point", + "coordinates": [ + 18.42104514121949, + 51.44773898340696 + ] + } + } + ] +} \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..cc519fe --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,51 @@ +services: + elasticsearch: + image: docker.elastic.co/elasticsearch/elasticsearch:7.17.10 + container_name: elasticsearch + environment: + - node.name=elasticsearch + - discovery.type=single-node + - bootstrap.memory_lock=true + - "ES_JAVA_OPTS=-Xms512m -Xmx512m" + - xpack.security.enabled=false + ulimits: + memlock: + soft: -1 + hard: -1 + volumes: + - ./es_data:/usr/share/elasticsearch/data + ports: + - "9200:9200" + networks: + - grafana-net + + grafana: + image: grafana/grafana:latest + container_name: grafana + volumes: + - ./grafana_data:/var/lib/grafana + - ./grafana/provisioning:/etc/grafana/provisioning + - ./grafana/dashboards:/var/lib/grafana/dashboards + ports: + - "3000:3000" + environment: + - GF_SECURITY_ADMIN_USER=admin + - GF_SECURITY_ADMIN_PASSWORD=admin + depends_on: + - elasticsearch + networks: + - grafana-net + + data_loader: + build: ./loader + container_name: data_loader + volumes: + - ./data:/app/data + depends_on: + - elasticsearch + networks: + - grafana-net + +networks: + grafana-net: + driver: bridge diff --git a/fix_dashboard.py b/fix_dashboard.py new file mode 100644 index 0000000..4faa4ef --- /dev/null +++ b/fix_dashboard.py @@ -0,0 +1,23 @@ +import json + +file_path = 'grafana/dashboards/dashboard.json' + +with open(file_path, 'r') as f: + data = json.load(f) + +def fix_datasource(obj): + if isinstance(obj, dict): + if 'datasource' in obj and isinstance(obj['datasource'], dict): + if obj['datasource'].get('type') == 'elasticsearch': + obj['datasource'] = 'Elasticsearch' + + for key, value in obj.items(): + fix_datasource(value) + elif isinstance(obj, list): + for item in obj: + fix_datasource(item) + +fix_datasource(data) + +with open(file_path, 'w') as f: + json.dump(data, f, indent=2) diff --git a/generate_mock_data.py b/generate_mock_data.py new file mode 100644 index 0000000..9f0e44b --- /dev/null +++ b/generate_mock_data.py @@ -0,0 +1,87 @@ +import json +import random +from datetime import datetime, timedelta + +def generate_random_point(): + # Coordinate approssimative dell'Europa + lon = random.uniform(-10.0, 30.0) + lat = random.uniform(35.0, 60.0) + return [lon, lat] + +def generate_data(num_points=500): + features = [] + operators = ["Operator A", "Operator B", "Operator C", "Operator D", "Operator E"] + # Status principali (senza timing) + statuses = ["OPEN", "CLOSED", "IN_PROGRESS", "ON_HOLD"] + # Timing status solo per CLOSED + timing_statuses = ["CLOSED_ON_TIME", "CLOSED_LATE"] + # Task types + task_types = ["Create Activity", "Close Activity", "Create WF", "Close WF", "Select DTO", "Report Generation", "Report QC"] + + base_time = datetime.now() + + for i in range(num_points): + status = random.choice(statuses) + operator = random.choice(operators) + duration = random.randint(10, 300) + task_type = random.choice(task_types) + + # Durations per task (in secondi) - min, max, avg variano per task + min_duration = random.randint(10, 25) + max_duration = random.randint(40, 60) + avg_duration = random.randint(min_duration + 5, max_duration - 5) + task_duration = random.randint(min_duration, max_duration + 20) + + # Lead time in minuti (per Avg Lead Time) - solo per CLOSED + lead_time = None + timing_status = None + if status == "CLOSED": + lead_time = random.randint(5, 120) # minuti + # 70% on time, 30% late (come in figura ~24 vs 11) + timing_status = random.choices(timing_statuses, weights=[70, 30])[0] + + # Random timestamp negli ultimi 7 giorni + time_offset = random.randint(0, 7 * 24 * 60 * 60) + timestamp = (base_time - timedelta(seconds=time_offset)).isoformat() + + properties = { + "status": status, + "operator": operator, + "duration": duration, + "timestamp": timestamp, + "id": i, + "task_type": task_type, + "min_duration": min_duration, + "max_duration": max_duration, + "avg_duration": avg_duration, + "task_duration": task_duration + } + + # Aggiungi lead_time e timing_status solo se CLOSED + if lead_time is not None: + properties["lead_time"] = lead_time + if timing_status is not None: + properties["timing_status"] = timing_status + + feature = { + "type": "Feature", + "properties": properties, + "geometry": { + "type": "Point", + "coordinates": generate_random_point() + } + } + features.append(feature) + + geojson = { + "type": "FeatureCollection", + "features": features + } + + with open('data/sample.geojson', 'w') as f: + json.dump(geojson, f, indent=2) + + print(f"Generated {num_points} data points in data/sample.geojson") + +if __name__ == "__main__": + generate_data(1000) diff --git a/grafana/dashboards/dashboard.json b/grafana/dashboards/dashboard.json new file mode 100644 index 0000000..5440841 --- /dev/null +++ b/grafana/dashboards/dashboard.json @@ -0,0 +1,716 @@ +{ + "annotations": { + "list": [ + { + "builtIn": 1, + "datasource": { + "type": "grafana", + "uid": "-- Grafana --" + }, + "enable": true, + "hide": true, + "iconColor": "rgba(0, 211, 255, 1)", + "name": "Annotations & Alerts", + "type": "dashboard" + } + ] + }, + "editable": true, + "fiscalYearStartMonth": 0, + "graphTooltip": 0, + "id": 1, + "links": [], + "panels": [ + { + "datasource": "Elasticsearch", + "fieldConfig": { + "defaults": { + "color": { + "mode": "thresholds" + }, + "custom": { + "align": "auto", + "cellOptions": { + "type": "auto" + }, + "inspect": false + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": 0 + } + ] + } + }, + "overrides": [ + { + "matcher": { + "id": "byName", + "options": "Status" + }, + "properties": [ + { + "id": "mappings", + "value": [ + { + "options": { + "CLOSED": { + "color": "blue", + "index": 1, + "text": "CLOSED" + }, + "IN_PROGRESS": { + "color": "light-blue", + "index": 2, + "text": "IN_PROGRESS" + }, + "ON_HOLD": { + "color": "purple", + "index": 3, + "text": "ON_HOLD" + }, + "OPEN": { + "color": "yellow", + "index": 0, + "text": "OPEN" + } + }, + "type": "value" + } + ] + }, + { + "id": "custom.cellOptions", + "value": { + "type": "color-text" + } + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Proportion" + }, + "properties": [ + { + "id": "unit", + "value": "percentunit" + }, + { + "id": "decimals", + "value": 1 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "Avg Lead Time" + }, + "properties": [ + { + "id": "unit", + "value": "m" + }, + { + "id": "decimals", + "value": 0 + } + ] + } + ] + }, + "gridPos": { + "h": 8, + "w": 8, + "x": 0, + "y": 0 + }, + "id": 1, + "options": { + "cellHeight": "sm", + "footer": { + "countRows": false, + "fields": "", + "reducer": [ + "sum" + ], + "show": false + }, + "showHeader": true + }, + 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b/grafana/provisioning/datasources/datasource.yml new file mode 100644 index 0000000..1b539b6 --- /dev/null +++ b/grafana/provisioning/datasources/datasource.yml @@ -0,0 +1,12 @@ +apiVersion: 1 + +datasources: + - name: Elasticsearch + type: elasticsearch + access: proxy + url: http://elasticsearch:9200 + isDefault: true + jsonData: + index: "geodata" + timeField: "timestamp" + esVersion: "7.10.0" diff --git a/loader/Dockerfile b/loader/Dockerfile new file mode 100644 index 0000000..9c2d97e --- /dev/null +++ b/loader/Dockerfile @@ -0,0 +1,10 @@ +FROM python:3.9-slim + +WORKDIR /app + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY load_data.py . + +CMD ["python", "load_data.py"] diff --git a/loader/load_data.py b/loader/load_data.py new file mode 100644 index 0000000..8ba8d5d --- /dev/null +++ b/loader/load_data.py @@ -0,0 +1,143 @@ +import json +import os +import time +import logging +from datetime import datetime +from elasticsearch import Elasticsearch, helpers + +# Configurazione logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +# Configurazione Elasticsearch +ES_HOST = os.environ.get('ES_HOST', 'elasticsearch') +ES_PORT = os.environ.get('ES_PORT', '9200') +INDEX_NAME = 'geodata' +GEOJSON_FILE = '/app/data/sample.geojson' + +def wait_for_elasticsearch(es): + """Attende che Elasticsearch sia pronto.""" + while True: + try: + if es.ping(): + logging.info("Elasticsearch è pronto!") + break + except Exception: + pass + logging.info("In attesa di Elasticsearch...") + time.sleep(5) + +def create_index(es): + """Crea l'indice con il mapping corretto per i dati geospaziali.""" + mapping = { + "mappings": { + "properties": { + "location": { + "type": "geo_point" + }, + "timestamp": { + "type": "date" + }, + "status": { + "type": "keyword" + }, + "operator": { + "type": "keyword" + }, + "duration": { + "type": "integer" + }, + "timing_status": { + "type": "keyword" + }, + "lead_time": { + "type": "integer" + }, + "task_type": { + "type": "keyword" + }, + "min_duration": { + "type": "integer" + }, + "max_duration": { + "type": "integer" + }, + "avg_duration": { + "type": "integer" + }, + "task_duration": { + "type": "integer" + }, + "id": { + "type": "integer" + } + } + } + } + + if es.indices.exists(index=INDEX_NAME): + logging.info(f"L'indice '{INDEX_NAME}' esiste già.") + else: + es.indices.create(index=INDEX_NAME, body=mapping) + logging.info(f"Indice '{INDEX_NAME}' creato con successo.") + +def process_geojson(file_path): + """Legge il file GeoJSON e prepara i documenti per Elasticsearch.""" + with open(file_path, 'r') as f: + data = json.load(f) + + actions = [] + for feature in data.get('features', []): + geometry = feature.get('geometry') + properties = feature.get('properties', {}) + + # Assicuriamoci che ci sia una geometria di tipo Point + if geometry and geometry.get('type') == 'Point': + lon, lat = geometry.get('coordinates') + + # Creiamo il documento + doc = { + "_index": INDEX_NAME, + "_source": { + "location": { + "lat": lat, + "lon": lon + }, + # Usa il timestamp presente o quello attuale + "timestamp": properties.get('timestamp', datetime.now().isoformat()), + "status": properties.get('status', 'UNKNOWN'), + "operator": properties.get('operator', 'Unknown Operator'), + "duration": properties.get('duration', 0), + **properties # Includi tutte le altre proprietà + } + } + actions.append(doc) + return actions + +def main(): + es = Elasticsearch([f"http://{ES_HOST}:{ES_PORT}"]) + + wait_for_elasticsearch(es) + create_index(es) + + # Controllo se ci sono già dati per evitare duplicati + try: + count = es.count(index=INDEX_NAME)['count'] + if count > 0: + logging.info(f"L'indice '{INDEX_NAME}' contiene già {count} documenti. Salto il caricamento.") + return + except Exception: + pass + + if os.path.exists(GEOJSON_FILE): + logging.info(f"Caricamento dati da {GEOJSON_FILE}...") + actions = process_geojson(GEOJSON_FILE) + if actions: + helpers.bulk(es, actions) + logging.info(f"Caricati {len(actions)} documenti in Elasticsearch.") + else: + logging.warning("Nessun dato valido trovato nel file GeoJSON.") + else: + logging.error(f"File {GEOJSON_FILE} non trovato. Assicurati di montare il volume correttamente.") + +if __name__ == "__main__": + main() diff --git a/loader/requirements.txt b/loader/requirements.txt new file mode 100644 index 0000000..32a418e --- /dev/null +++ b/loader/requirements.txt @@ -0,0 +1,2 @@ +elasticsearch==7.17.0 +requests