infra: mirror light per-source Airflow DAGs + generators (GEN_ROWS), fix script path

infra/airflow/per_source_gen_dags.py

@@ -0,0 +1,65 @@
+"""Per-database light data generation DAGs.
+
+Each DAG runs one generator script and accepts a `rows` value via the
+dag_run conf (passed by the Command Center), exported as GEN_ROWS.
+Triggerable independently so every database gets its own button.
+"""
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from datetime import datetime
+import os
+import subprocess
+
+SCRIPTS_DIR = "/opt/airflow/dags/scripts"
+
+SOURCES = {
+    "postgres": "generate_postgres_sales_data.py",
+    "mysql": "generate_mysql_employee_data.py",
+    "mongodb": "generate_mongodb_events_data.py",
+    "cassandra": "generate_cassandra_telemetry_data.py",
+    "neo4j": "generate_neo4j_graph_data.py",
+}
+
+DEFAULT_ROWS = {"neo4j": "2000"}
+
+default_args = {"owner": "airflow", "retries": 0}
+
+
+def make_runner(script: str, default_rows: str):
+    def _run(**context):
+        dag_run = context.get("dag_run")
+        conf = (dag_run.conf if dag_run else {}) or {}
+        rows = str(conf.get("rows") or default_rows)
+        env = dict(os.environ)
+        env["GEN_ROWS"] = rows
+        print(f"Running {script} with GEN_ROWS={rows}")
+        result = subprocess.run(
+            ["python3", os.path.join(SCRIPTS_DIR, script)],
+            capture_output=True, text=True, env=env,
+        )
+        if result.stdout:
+            print(result.stdout[-4000:])
+        if result.stderr:
+            print("STDERR:", result.stderr[-4000:])
+        if result.returncode != 0:
+            raise Exception(f"{script} failed with return code {result.returncode}")
+    return _run
+
+
+for _src, _script in SOURCES.items():
+    _dag_id = f"gen_{_src}"
+    _dag = DAG(
+        dag_id=_dag_id,
+        default_args=default_args,
+        description=f"Generate light data into {_src} (GEN_ROWS via conf.rows)",
+        schedule=None,
+        start_date=datetime(2025, 1, 1),
+        catchup=False,
+        tags=["data", "generation", _src],
+    )
+    PythonOperator(
+        task_id=f"generate_{_src}",
+        python_callable=make_runner(_script, DEFAULT_ROWS.get(_src, "5000")),
+        dag=_dag,
+    )
+    globals()[_dag_id] = _dag

infra/airflow/scripts/generate_cassandra_telemetry_data.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+Script to generate fake telemetry data for Cassandra
+Generates approximately 1GB of data
+"""
+
+from cassandra.cluster import Cluster
+import random
+from datetime import datetime, timedelta
+import uuid
+import sys
+
+# Database connection details
+DB_HOST = "10.0.21.51"
+DB_PORT = "9042"
+KEYSPACE = "telemetry"
+TABLE_NAME = "device_metrics"
+
+# Data generation settings
+import os
+TARGET_ROWS = int(os.getenv("GEN_ROWS", "5000"))  # light, configurable via GEN_ROWS
+BATCH_SIZE = min(5000, max(500, TARGET_ROWS))
+
+# Sample data
+METRIC_TYPES = ["temperature", "humidity", "pressure", "voltage", "current"]
+DEVICE_PREFIX = "device-"
+
+def generate_fake_device_metric():
+    """Generate a single fake device metric"""
+    device_id = f"{DEVICE_PREFIX}{random.randint(1, 50000)}"
+    
+    # Random timestamp within the last year
+    days_ago = random.randint(0, 365)
+    metric_ts = datetime.now() - timedelta(days=days_ago, hours=random.randint(0, 23), 
+                                           minutes=random.randint(0, 59))
+    
+    metric_type = random.choice(METRIC_TYPES)
+    metric_value = round(random.uniform(0.0, 100.0), 4)
+    
+    # Generate a long payload field
+    payload = "X" * 200
+    
+    return (device_id, metric_ts, metric_type, metric_value, payload)
+
+def main():
+    print(f"Connecting to Cassandra at {DB_HOST}:{DB_PORT}...")
+    
+    cluster = Cluster([DB_HOST], port=DB_PORT)
+    session = cluster.connect()
+    
+    print(f"Generating {TARGET_ROWS} device metrics...")
+    print(f"Batch size: {BATCH_SIZE}")
+    
+    total_generated = 0
+    batch = []
+    
+    for i in range(TARGET_ROWS):
+        batch.append(generate_fake_device_metric())
+        
+        if len(batch) >= BATCH_SIZE:
+            session.execute(
+                f"""
+                INSERT INTO {KEYSPACE}.{TABLE_NAME} (device_id, metric_ts, metric_type, metric_value, payload)
+                VALUES (%s, %s, %s, %s, %s)
+                """,
+                batch
+            )
+            total_generated += len(batch)
+            batch = []
+            
+            if total_generated % 100000 == 0:
+                print(f"Generated {total_generated} rows...")
+    
+    # Insert remaining rows
+    if batch:
+        session.execute(
+            f"""
+            INSERT INTO {KEYSPACE}.{TABLE_NAME} (device_id, metric_ts, metric_type, metric_value, payload)
+            VALUES (%s, %s, %s, %s, %s)
+            """,
+            batch
+        )
+        total_generated += len(batch)
+    
+    session.shutdown()
+    cluster.shutdown()
+    
+    print(f"Completed! Generated {total_generated} device metrics.")
+
+if __name__ == "__main__":
+    main()

infra/airflow/scripts/generate_mongodb_events_data.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+"""
+Script to generate fake event data for MongoDB
+Generates approximately 1GB of data
+"""
+
+import pymongo
+import random
+from datetime import datetime, timedelta
+import uuid
+import sys
+
+# Database connection details
+DB_HOST = "10.0.21.51"
+DB_PORT = "27017"
+DB_NAME = "supplychain"
+COLLECTION_NAME = "events"
+
+# Data generation settings
+import os
+TARGET_DOCUMENTS = int(os.getenv("GEN_ROWS", "5000"))  # light, configurable via GEN_ROWS
+BATCH_SIZE = min(5000, max(500, TARGET_DOCUMENTS))
+
+# Sample data
+EVENT_TYPES = ["INSERT", "UPDATE", "DELETE", "CREATE", "MODIFY"]
+REGIONS = ["EU", "APAC", "LATAM", "NA", "EMEA"]
+SOURCES = ["ERP", "WMS", "CRM", "SCM", "TMS"]
+
+def generate_fake_event():
+    """Generate a single fake event"""
+    event_id = uuid.uuid4()
+    event_type = random.choice(EVENT_TYPES)
+    region = random.choice(REGIONS)
+    source = random.choice(SOURCES)
+    
+    # Random timestamp within the last year
+    days_ago = random.randint(0, 365)
+    ts = datetime.now() - timedelta(days=days_ago, hours=random.randint(0, 23), 
+                                     minutes=random.randint(0, 59))
+    
+    amount = random.uniform(100.0, 50000.0)
+    
+    # Generate a long payload field (like the existing data)
+    payload = "X" * 500
+    
+    return {
+        "event_id": event_id,
+        "type": event_type,
+        "region": region,
+        "source": source,
+        "amount": amount,
+        "ts": ts,
+        "payload": payload
+    }
+
+def main():
+    print(f"Connecting to MongoDB at {DB_HOST}:{DB_PORT}...")
+    
+    client = pymongo.MongoClient(f"mongodb://{DB_HOST}:{DB_PORT}/")
+    db = client[DB_NAME]
+    collection = db[COLLECTION_NAME]
+    
+    print(f"Generating {TARGET_DOCUMENTS} events...")
+    print(f"Batch size: {BATCH_SIZE}")
+    
+    total_generated = 0
+    batch = []
+    
+    for i in range(TARGET_DOCUMENTS):
+        batch.append(generate_fake_event())
+        
+        if len(batch) >= BATCH_SIZE:
+            collection.insert_many(batch)
+            total_generated += len(batch)
+            batch = []
+            
+            if total_generated % 100000 == 0:
+                print(f"Generated {total_generated} documents...")
+    
+    # Insert remaining documents
+    if batch:
+        collection.insert_many(batch)
+        total_generated += len(batch)
+    
+    client.close()
+    
+    print(f"Completed! Generated {total_generated} events.")
+
+if __name__ == "__main__":
+    main()

infra/airflow/scripts/generate_mysql_employee_data.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+"""
+Script to generate fake employee event data for MySQL
+Generates approximately 1GB of data
+"""
+
+import mysql.connector
+import random
+from datetime import datetime, timedelta
+import uuid
+import sys
+
+# Database connection details
+DB_HOST = "10.0.21.51"
+DB_PORT = "3306"
+DB_NAME = "hr"
+DB_USER = "mo"
+DB_PASSWORD = "Dell2026!"
+
+# Data generation settings
+import os
+TARGET_ROWS = int(os.getenv("GEN_ROWS", "5000"))  # light, configurable via GEN_ROWS
+BATCH_SIZE = min(10000, max(500, TARGET_ROWS))
+
+# Sample data
+DEPARTMENTS = ["HR", "Operations", "Sales", "Marketing", "Finance", "IT", "Engineering", "Legal"]
+ROLE_NAMES = ["Analyst", "Lead", "Manager", "Consultant", "Director", "Engineer", "Specialist", "Coordinator"]
+REGIONS = ["EU", "APAC", "LATAM", "NA", "EMEA"]
+EVENT_TYPES = ["TRANSFER", "PROMOTION", "TERMINATION", "HIRED", "SALARY_CHANGE", "DEPARTMENT_CHANGE"]
+
+def generate_fake_employee_event():
+    """Generate a single fake employee event"""
+    employee_id = random.randint(1, 100000)
+    department = random.choice(DEPARTMENTS)
+    role_name = random.choice(ROLE_NAMES)
+    region = random.choice(REGIONS)
+    event_type = random.choice(EVENT_TYPES)
+    
+    # Random timestamp within the last 2 years
+    days_ago = random.randint(0, 730)
+    event_ts = datetime.now() - timedelta(days=days_ago, hours=random.randint(0, 23), 
+                                          minutes=random.randint(0, 59))
+    
+    salary_change = round(random.uniform(1000.0, 20000.0), 2) if random.random() > 0.3 else None
+    
+    # Generate a long notes field (like the existing data)
+    notes = str(uuid.uuid4()) * 10
+    
+    return (employee_id, department, role_name, region, event_type, salary_change, event_ts, notes)
+
+def main():
+    print(f"Connecting to MySQL at {DB_HOST}:{DB_PORT}...")
+    
+    conn = mysql.connector.connect(
+        host=DB_HOST,
+        port=DB_PORT,
+        database=DB_NAME,
+        user=DB_USER,
+        password=DB_PASSWORD
+    )
+    cursor = conn.cursor()
+    
+    print(f"Generating {TARGET_ROWS} employee events...")
+    print(f"Batch size: {BATCH_SIZE}")
+    
+    total_generated = 0
+    batch = []
+    
+    for i in range(TARGET_ROWS):
+        batch.append(generate_fake_employee_event())
+        
+        if len(batch) >= BATCH_SIZE:
+            cursor.executemany(
+                """
+                INSERT INTO employee_events (employee_id, department, role_name, region, 
+                                            event_type, salary_change, event_ts, notes)
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+                """,
+                batch
+            )
+            conn.commit()
+            total_generated += len(batch)
+            batch = []
+            
+            if total_generated % 100000 == 0:
+                print(f"Generated {total_generated} rows...")
+    
+    # Insert remaining rows
+    if batch:
+        cursor.executemany(
+            """
+            INSERT INTO employee_events (employee_id, department, role_name, region, 
+                                        event_type, salary_change, event_ts, notes)
+            VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+            """,
+            batch
+        )
+        conn.commit()
+        total_generated += len(batch)
+    
+    cursor.close()
+    conn.close()
+    
+    print(f"Completed! Generated {total_generated} employee events.")
+
+if __name__ == "__main__":
+    main()

infra/airflow/scripts/generate_neo4j_graph_data.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+"""
+Script to generate fake graph data for Neo4j
+Generates approximately 1GB of data with nodes and relationships
+"""
+
+from neo4j import GraphDatabase
+import random
+import uuid
+import sys
+
+# Database connection details
+DB_HOST = "10.0.21.51"
+DB_PORT = "7687"
+DB_USER = "neo4j"
+DB_PASSWORD = "testpwd"
+
+# Data generation settings
+import os
+TARGET_NODES = int(os.getenv("GEN_ROWS", "2000"))  # light, configurable via GEN_ROWS
+BATCH_SIZE = min(1000, max(200, TARGET_NODES))
+
+# Sample data
+PRODUCT_CATEGORIES = ["Electronics", "Clothing", "Food", "Furniture", "Toys", "Books"]
+SUPPLIER_REGIONS = ["EU", "APAC", "LATAM", "NA", "EMEA"]
+RELATIONSHIP_TYPES = ["SUPPLIES", "RELATED_TO", "COMPATIBLE_WITH", "PART_OF"]
+
+def generate_fake_product():
+    """Generate a single fake product node"""
+    product_id = str(uuid.uuid4())
+    name = f"Product-{random.randint(1000, 999999)}"
+    category = random.choice(PRODUCT_CATEGORIES)
+    price = round(random.uniform(10.0, 1000.0), 2)
+    stock = random.randint(0, 1000)
+    
+    # Generate a long description field
+    description = "X" * 200
+    
+    return {
+        "product_id": product_id,
+        "name": name,
+        "category": category,
+        "price": price,
+        "stock": stock,
+        "description": description
+    }
+
+def generate_fake_supplier():
+    """Generate a single fake supplier node"""
+    supplier_id = str(uuid.uuid4())
+    name = f"Supplier-{random.randint(1000, 999999)}"
+    region = random.choice(SUPPLIER_REGIONS)
+    rating = round(random.uniform(1.0, 5.0), 1)
+    
+    # Generate a long address field
+    address = "X" * 150
+    
+    return {
+        "supplier_id": supplier_id,
+        "name": name,
+        "region": region,
+        "rating": rating,
+        "address": address
+    }
+
+def main():
+    print(f"Connecting to Neo4j at {DB_HOST}:{DB_PORT}...")
+    
+    driver = GraphDatabase.driver(f"bolt://{DB_HOST}:{DB_PORT}", 
+                                   auth=(DB_USER, DB_PASSWORD))
+    
+    with driver.session() as session:
+        print(f"Generating {TARGET_NODES} product nodes...")
+        print(f"Batch size: {BATCH_SIZE}")
+        
+        total_products = 0
+        total_suppliers = 0
+        product_ids = []
+        
+        # Generate product nodes
+        batch = []
+        for i in range(TARGET_NODES):
+            product = generate_fake_product()
+            batch.append(product)
+            product_ids.append(product["product_id"])
+            
+            if len(batch) >= BATCH_SIZE:
+                session.run(
+                    """
+                    UNWIND $batch as row
+                    CREATE (p:Product {
+                        product_id: row.product_id,
+                        name: row.name,
+                        category: row.category,
+                        price: row.price,
+                        stock: row.stock,
+                        description: row.description
+                    })
+                    """,
+                    batch=batch
+                )
+                total_products += len(batch)
+                batch = []
+                
+                if total_products % 50000 == 0:
+                    print(f"Generated {total_products} product nodes...")
+        
+        # Insert remaining products
+        if batch:
+            session.run(
+                """
+                UNWIND $batch as row
+                CREATE (p:Product {
+                    product_id: row.product_id,
+                    name: row.name,
+                    category: row.category,
+                    price: row.price,
+                    stock: row.stock,
+                    description: row.description
+                })
+                """,
+                batch=batch
+            )
+            total_products += len(batch)
+        
+        print(f"Generated {total_products} product nodes.")
+        
+        # Generate supplier nodes (fewer than products)
+        print(f"Generating supplier nodes...")
+        target_suppliers = max(20, TARGET_NODES // 10)
+        batch = []
+        supplier_ids = []
+        
+        for i in range(target_suppliers):
+            supplier = generate_fake_supplier()
+            batch.append(supplier)
+            supplier_ids.append(supplier["supplier_id"])
+            
+            if len(batch) >= BATCH_SIZE:
+                session.run(
+                    """
+                    UNWIND $batch as row
+                    CREATE (s:Supplier {
+                        supplier_id: row.supplier_id,
+                        name: row.name,
+                        region: row.region,
+                        rating: row.rating,
+                        address: row.address
+                    })
+                    """,
+                    batch=batch
+                )
+                total_suppliers += len(batch)
+                batch = []
+        
+        if batch:
+            session.run(
+                """
+                UNWIND $batch as row
+                CREATE (s:Supplier {
+                    supplier_id: row.supplier_id,
+                    name: row.name,
+                    region: row.region,
+                    rating: row.rating,
+                    address: row.address
+                })
+                """,
+                batch=batch
+            )
+            total_suppliers += len(batch)
+        
+        print(f"Generated {total_suppliers} supplier nodes.")
+        
+        # Create relationships between products and suppliers
+        print(f"Creating relationships...")
+        batch = []
+        total_relationships = 0
+        
+        for product_id in product_ids:
+            # Each product is supplied by 1-3 random suppliers
+            num_suppliers = random.randint(1, 3)
+            for _ in range(num_suppliers):
+                supplier_id = random.choice(supplier_ids)
+                rel_type = random.choice(RELATIONSHIP_TYPES)
+                
+                batch.append({
+                    "product_id": product_id,
+                    "supplier_id": supplier_id,
+                    "rel_type": rel_type
+                })
+                
+                if len(batch) >= BATCH_SIZE:
+                    session.run(
+                        """
+                        UNWIND $batch as row
+                        MATCH (p:Product {product_id: row.product_id})
+                        MATCH (s:Supplier {supplier_id: row.supplier_id})
+                        CALL apoc.create.relationship(p, row.rel_type, {}, s) YIELD rel
+                        RETURN rel
+                        """,
+                        batch=batch
+                    )
+                    total_relationships += len(batch)
+                    batch = []
+                    
+                    if total_relationships % 50000 == 0:
+                        print(f"Created {total_relationships} relationships...")
+        
+        if batch:
+            session.run(
+                """
+                UNWIND $batch as row
+                MATCH (p:Product {product_id: row.product_id})
+                MATCH (s:Supplier {supplier_id: row.supplier_id})
+                CALL apoc.create.relationship(p, row.rel_type, {}, s) YIELD rel
+                RETURN rel
+                """,
+                batch=batch
+            )
+            total_relationships += len(batch)
+        
+        print(f"Created {total_relationships} relationships.")
+    
+    driver.close()
+    print(f"Completed! Generated {total_products} products, {total_suppliers} suppliers, and {total_relationships} relationships.")
+
+if __name__ == "__main__":
+    main()

infra/airflow/scripts/generate_postgres_sales_data.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+"""
+Script to generate fake sales order data for PostgreSQL
+Generates approximately 1GB of data
+"""
+
+import psycopg2
+import random
+from datetime import datetime, timedelta
+import uuid
+import sys
+import os
+
+# Database connection details
+DB_HOST = "10.0.21.51"
+DB_PORT = "5432"
+DB_NAME = "postgres"
+DB_USER = "mo"
+DB_PASSWORD = "Dell2026!"
+
+# Data generation settings
+TARGET_ROWS = int(os.getenv("GEN_ROWS", "5000"))  # light, configurable via GEN_ROWS
+BATCH_SIZE = min(10000, max(500, TARGET_ROWS))
+
+# Sample data
+REGIONS = ["EU", "APAC", "LATAM", "NA", "EMEA"]
+SALES_CHANNELS = ["STORE", "ONLINE", "MOBILE", "B2B"]
+CURRENCIES = ["EUR", "USD", "GBP", "JPY", "CNY"]
+ORDER_STATUSES = ["SHIPPED", "PENDING", "CANCELLED", "RETURNED", "DELIVERED"]
+
+def generate_fake_order():
+    """Generate a single fake sales order"""
+    customer_id = random.randint(1, 100000)
+    product_id = random.randint(1, 5000)
+    region = random.choice(REGIONS)
+    sales_channel = random.choice(SALES_CHANNELS)
+    
+    # Random timestamp within the last 2 years
+    days_ago = random.randint(0, 730)
+    order_ts = datetime.now() - timedelta(days=days_ago, hours=random.randint(0, 23), 
+                                          minutes=random.randint(0, 59))
+    
+    amount = round(random.uniform(10.0, 10000.0), 2)
+    currency = random.choice(CURRENCIES)
+    order_status = random.choice(ORDER_STATUSES)
+    
+    # Generate a long notes field (like the existing data)
+    notes = str(uuid.uuid4()) * 10
+    
+    return (customer_id, product_id, region, sales_channel, order_ts, 
+            amount, currency, order_status, notes)
+
+def main():
+    print(f"Connecting to PostgreSQL at {DB_HOST}:{DB_PORT}...")
+    
+    conn = psycopg2.connect(
+        host=DB_HOST,
+        port=DB_PORT,
+        database=DB_NAME,
+        user=DB_USER,
+        password=DB_PASSWORD
+    )
+    cursor = conn.cursor()
+    
+    print(f"Generating {TARGET_ROWS} sales orders...")
+    print(f"Batch size: {BATCH_SIZE}")
+    
+    total_generated = 0
+    batch = []
+    
+    for i in range(TARGET_ROWS):
+        batch.append(generate_fake_order())
+        
+        if len(batch) >= BATCH_SIZE:
+            cursor.executemany(
+                """
+                INSERT INTO sales_orders (customer_id, product_id, region, sales_channel, 
+                                         order_ts, amount, currency, order_status, notes)
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+                """,
+                batch
+            )
+            conn.commit()
+            total_generated += len(batch)
+            batch = []
+            
+            if total_generated % 100000 == 0:
+                print(f"Generated {total_generated} rows...")
+    
+    # Insert remaining rows
+    if batch:
+        cursor.executemany(
+            """
+            INSERT INTO sales_orders (customer_id, product_id, region, sales_channel, 
+                                     order_ts, amount, currency, order_status, notes)
+            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+            """,
+            batch
+        )
+        conn.commit()
+        total_generated += len(batch)
+    
+    cursor.close()
+    conn.close()
+    
+    print(f"Completed! Generated {total_generated} sales orders.")
+
+if __name__ == "__main__":
+    main()