Improve the frontend 3
This commit is contained in:
Urtzi Alfaro
@@ -0,0 +1,581 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Demo Orchestration Runs Seeding Script for Orchestrator Service
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Creates realistic orchestration scenarios in various states for demo purposes
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This script runs as a Kubernetes init job inside the orchestrator-service container.
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It populates the template tenants with comprehensive orchestration run histories.
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Usage:
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python /app/scripts/demo/seed_demo_orchestration_runs.py
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Environment Variables Required:
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ORCHESTRATOR_DATABASE_URL - PostgreSQL connection string for orchestrator database
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DEMO_MODE - Set to 'production' for production seeding
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LOG_LEVEL - Logging level (default: INFO)
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Note: No database lookups needed - all IDs are pre-defined in the JSON file
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"""
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import asyncio
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import uuid
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import sys
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import os
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import json
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from datetime import datetime, timezone, timedelta, date
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from pathlib import Path
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from decimal import Decimal
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import random
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# Add app to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
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from sqlalchemy.orm import sessionmaker
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from sqlalchemy import select, text
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import structlog
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from app.models.orchestration_run import (
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OrchestrationRun, OrchestrationStatus
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)
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# Configure logging
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structlog.configure(
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processors=[
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structlog.stdlib.add_log_level,
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structlog.processors.TimeStamper(fmt="iso"),
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structlog.dev.ConsoleRenderer()
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]
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)
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logger = structlog.get_logger()
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# Fixed Demo Tenant IDs (must match tenant service)
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DEMO_TENANT_SAN_PABLO = uuid.UUID("a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6") # Individual bakery
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DEMO_TENANT_LA_ESPIGA = uuid.UUID("b2c3d4e5-f6a7-48b9-c0d1-e2f3a4b5c6d7") # Central bakery
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# Base reference date for date calculations
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BASE_REFERENCE_DATE = datetime(2025, 1, 15, 12, 0, 0, tzinfo=timezone.utc)
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# Hardcoded orchestration run configurations
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ORCHESTRATION_CONFIG = {
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"runs_per_tenant": 12,
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"temporal_distribution": {
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"completed": {
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"percentage": 0.4,
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"offset_days_min": -30,
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"offset_days_max": -1,
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"statuses": ["completed"]
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},
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"in_execution": {
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"percentage": 0.25,
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"offset_days_min": -5,
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"offset_days_max": 2,
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"statuses": ["running", "partial_success"]
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},
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"failed": {
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"percentage": 0.1,
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"offset_days_min": -10,
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"offset_days_max": -1,
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"statuses": ["failed"]
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},
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"cancelled": {
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"percentage": 0.05,
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"offset_days_min": -7,
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"offset_days_max": -1,
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"statuses": ["cancelled"]
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},
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"pending": {
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"percentage": 0.2,
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"offset_days_min": 0,
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"offset_days_max": 3,
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"statuses": ["pending"]
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}
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},
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"run_types": [
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{"type": "scheduled", "weight": 0.7},
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{"type": "manual", "weight": 0.25},
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{"type": "test", "weight": 0.05}
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],
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"priorities": {
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"normal": 0.7,
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"high": 0.25,
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"critical": 0.05
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},
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"performance_metrics": {
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"fulfillment_rate": {"min": 85.0, "max": 98.0},
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"on_time_delivery": {"min": 80.0, "max": 95.0},
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"cost_accuracy": {"min": 90.0, "max": 99.0},
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"quality_score": {"min": 7.0, "max": 9.5}
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},
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"step_durations": {
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"forecasting": {"min": 30, "max": 120}, # seconds
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"production": {"min": 60, "max": 300},
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"procurement": {"min": 45, "max": 180},
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"notification": {"min": 15, "max": 60}
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},
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"error_scenarios": [
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{"type": "forecasting_timeout", "message": "Forecasting service timeout - retrying"},
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{"type": "production_unavailable", "message": "Production service temporarily unavailable"},
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{"type": "procurement_failure", "message": "Procurement service connection failed"},
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{"type": "notification_error", "message": "Notification service rate limit exceeded"}
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]
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}
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def calculate_date_from_offset(offset_days: int) -> date:
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"""Calculate a date based on offset from BASE_REFERENCE_DATE"""
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return (BASE_REFERENCE_DATE + timedelta(days=offset_days)).date()
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def calculate_datetime_from_offset(offset_days: int) -> datetime:
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"""Calculate a datetime based on offset from BASE_REFERENCE_DATE"""
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return BASE_REFERENCE_DATE + timedelta(days=offset_days)
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def weighted_choice(choices: list) -> dict:
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"""Make a weighted random choice from list of dicts with 'weight' key"""
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total_weight = sum(c.get("weight", 1.0) for c in choices)
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r = random.uniform(0, total_weight)
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cumulative = 0
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for choice in choices:
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cumulative += choice.get("weight", 1.0)
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if r <= cumulative:
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return choice
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return choices[-1]
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def generate_run_number(tenant_id: uuid.UUID, index: int, run_type: str) -> str:
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"""Generate a unique run number"""
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tenant_prefix = "SP" if tenant_id == DEMO_TENANT_SAN_PABLO else "LE"
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type_code = run_type[0:3].upper()
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return f"ORCH-{tenant_prefix}-{type_code}-{BASE_REFERENCE_DATE.year}-{index:03d}"
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async def generate_orchestration_for_tenant(
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db: AsyncSession,
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tenant_id: uuid.UUID,
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tenant_name: str,
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business_model: str,
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config: dict
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) -> dict:
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"""Generate orchestration runs for a specific tenant"""
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logger.info("─" * 80)
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logger.info(f"Generating orchestration runs for: {tenant_name}")
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logger.info(f"Tenant ID: {tenant_id}")
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logger.info("─" * 80)
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# Check if orchestration runs already exist
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result = await db.execute(
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select(OrchestrationRun).where(OrchestrationRun.tenant_id == tenant_id).limit(1)
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)
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existing = result.scalar_one_or_none()
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if existing:
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logger.info(f" ⏭️ Orchestration runs already exist for {tenant_name}, skipping seed")
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return {
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"tenant_id": str(tenant_id),
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"runs_created": 0,
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"steps_created": 0,
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"skipped": True
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}
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orch_config = config["orchestration_config"]
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total_runs = orch_config["runs_per_tenant"]
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runs_created = 0
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steps_created = 0
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for i in range(total_runs):
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# Determine temporal distribution
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rand_temporal = random.random()
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cumulative = 0
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temporal_category = None
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for category, details in orch_config["temporal_distribution"].items():
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cumulative += details["percentage"]
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if rand_temporal <= cumulative:
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temporal_category = details
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break
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if not temporal_category:
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temporal_category = orch_config["temporal_distribution"]["completed"]
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# Calculate run date
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offset_days = random.randint(
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temporal_category["offset_days_min"],
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temporal_category["offset_days_max"]
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)
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run_date = calculate_date_from_offset(offset_days)
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# Select status
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status = random.choice(temporal_category["statuses"])
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# Select run type
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run_type_choice = weighted_choice(orch_config["run_types"])
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run_type = run_type_choice["type"]
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# Select priority
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priority_rand = random.random()
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cumulative_priority = 0
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priority = "normal"
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for p, weight in orch_config["priorities"].items():
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cumulative_priority += weight
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if priority_rand <= cumulative_priority:
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priority = p
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break
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# Generate run number
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run_number = generate_run_number(tenant_id, i + 1, run_type)
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# Calculate timing based on status
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started_at = calculate_datetime_from_offset(offset_days - 1)
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completed_at = None
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duration_seconds = None
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if status in ["completed", "partial_success"]:
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completed_at = calculate_datetime_from_offset(offset_days)
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duration_seconds = int((completed_at - started_at).total_seconds())
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elif status == "failed":
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completed_at = calculate_datetime_from_offset(offset_days - 0.5)
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duration_seconds = int((completed_at - started_at).total_seconds())
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elif status == "cancelled":
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completed_at = calculate_datetime_from_offset(offset_days - 0.2)
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duration_seconds = int((completed_at - started_at).total_seconds())
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# Generate step timing
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forecasting_started_at = started_at
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forecasting_completed_at = forecasting_started_at + timedelta(seconds=random.randint(
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orch_config["step_durations"]["forecasting"]["min"],
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orch_config["step_durations"]["forecasting"]["max"]
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))
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forecasting_status = "success"
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forecasting_error = None
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production_started_at = forecasting_completed_at
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production_completed_at = production_started_at + timedelta(seconds=random.randint(
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orch_config["step_durations"]["production"]["min"],
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orch_config["step_durations"]["production"]["max"]
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))
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production_status = "success"
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production_error = None
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procurement_started_at = production_completed_at
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procurement_completed_at = procurement_started_at + timedelta(seconds=random.randint(
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orch_config["step_durations"]["procurement"]["min"],
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orch_config["step_durations"]["procurement"]["max"]
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))
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procurement_status = "success"
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procurement_error = None
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notification_started_at = procurement_completed_at
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notification_completed_at = notification_started_at + timedelta(seconds=random.randint(
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orch_config["step_durations"]["notification"]["min"],
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orch_config["step_durations"]["notification"]["max"]
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))
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notification_status = "success"
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notification_error = None
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# Simulate errors for failed runs
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if status == "failed":
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error_scenario = random.choice(orch_config["error_scenarios"])
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error_step = random.choice(["forecasting", "production", "procurement", "notification"])
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if error_step == "forecasting":
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forecasting_status = "failed"
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forecasting_error = error_scenario["message"]
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elif error_step == "production":
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production_status = "failed"
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production_error = error_scenario["message"]
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elif error_step == "procurement":
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procurement_status = "failed"
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procurement_error = error_scenario["message"]
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elif error_step == "notification":
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notification_status = "failed"
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notification_error = error_scenario["message"]
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# Generate results summary
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forecasts_generated = random.randint(5, 15)
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production_batches_created = random.randint(3, 8)
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procurement_plans_created = random.randint(2, 6)
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purchase_orders_created = random.randint(1, 4)
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notifications_sent = random.randint(10, 25)
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# Generate performance metrics for completed runs
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fulfillment_rate = None
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on_time_delivery_rate = None
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cost_accuracy = None
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quality_score = None
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if status in ["completed", "partial_success"]:
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metrics = orch_config["performance_metrics"]
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fulfillment_rate = Decimal(str(random.uniform(
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metrics["fulfillment_rate"]["min"],
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metrics["fulfillment_rate"]["max"]
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)))
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on_time_delivery_rate = Decimal(str(random.uniform(
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metrics["on_time_delivery"]["min"],
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metrics["on_time_delivery"]["max"]
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)))
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cost_accuracy = Decimal(str(random.uniform(
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metrics["cost_accuracy"]["min"],
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metrics["cost_accuracy"]["max"]
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)))
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quality_score = Decimal(str(random.uniform(
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metrics["quality_score"]["min"],
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metrics["quality_score"]["max"]
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)))
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# Create orchestration run
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run = OrchestrationRun(
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id=uuid.uuid4(),
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tenant_id=tenant_id,
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run_number=run_number,
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status=OrchestrationStatus(status),
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run_type=run_type,
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priority=priority,
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started_at=started_at,
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completed_at=completed_at,
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duration_seconds=duration_seconds,
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forecasting_started_at=forecasting_started_at,
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forecasting_completed_at=forecasting_completed_at,
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forecasting_status=forecasting_status,
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forecasting_error=forecasting_error,
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production_started_at=production_started_at,
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production_completed_at=production_completed_at,
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production_status=production_status,
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production_error=production_error,
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procurement_started_at=procurement_started_at,
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procurement_completed_at=procurement_completed_at,
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procurement_status=procurement_status,
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procurement_error=procurement_error,
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notification_started_at=notification_started_at,
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notification_completed_at=notification_completed_at,
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notification_status=notification_status,
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notification_error=notification_error,
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forecasts_generated=forecasts_generated,
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production_batches_created=production_batches_created,
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procurement_plans_created=procurement_plans_created,
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purchase_orders_created=purchase_orders_created,
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notifications_sent=notifications_sent,
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fulfillment_rate=fulfillment_rate,
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on_time_delivery_rate=on_time_delivery_rate,
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cost_accuracy=cost_accuracy,
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quality_score=quality_score,
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created_at=calculate_datetime_from_offset(offset_days - 2),
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updated_at=calculate_datetime_from_offset(offset_days),
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triggered_by="scheduler" if run_type == "scheduled" else "user" if run_type == "manual" else "test-runner"
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)
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db.add(run)
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await db.flush() # Get run ID
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runs_created += 1
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steps_created += 4 # forecasting, production, procurement, notification
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await db.commit()
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logger.info(f" 📊 Successfully created {runs_created} orchestration runs with {steps_created} steps for {tenant_name}")
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logger.info("")
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return {
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"tenant_id": str(tenant_id),
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"runs_created": runs_created,
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"steps_created": steps_created,
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"skipped": False
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}
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async def seed_all(db: AsyncSession):
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"""Seed all demo tenants with orchestration runs"""
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logger.info("=" * 80)
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logger.info("🚀 Starting Demo Orchestration Runs Seeding")
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logger.info("=" * 80)
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# Load configuration
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config = {
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"orchestration_config": {
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"runs_per_tenant": 12,
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"temporal_distribution": {
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"completed": {
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"percentage": 0.4,
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"offset_days_min": -30,
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"offset_days_max": -1,
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"statuses": ["completed"]
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},
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"in_execution": {
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"percentage": 0.25,
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"offset_days_min": -5,
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"offset_days_max": 2,
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"statuses": ["running", "partial_success"]
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},
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"failed": {
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"percentage": 0.1,
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"offset_days_min": -10,
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"offset_days_max": -1,
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"statuses": ["failed"]
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},
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"cancelled": {
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"percentage": 0.05,
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"offset_days_min": -7,
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"offset_days_max": -1,
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"statuses": ["cancelled"]
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},
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"pending": {
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"percentage": 0.2,
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"offset_days_min": 0,
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"offset_days_max": 3,
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"statuses": ["pending"]
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}
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},
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"run_types": [
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{"type": "scheduled", "weight": 0.7},
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{"type": "manual", "weight": 0.25},
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{"type": "test", "weight": 0.05}
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],
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"priorities": {
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"normal": 0.7,
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"high": 0.25,
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"critical": 0.05
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},
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"performance_metrics": {
|
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"fulfillment_rate": {"min": 85.0, "max": 98.0},
|
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"on_time_delivery": {"min": 80.0, "max": 95.0},
|
||||
"cost_accuracy": {"min": 90.0, "max": 99.0},
|
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"quality_score": {"min": 7.0, "max": 9.5}
|
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},
|
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"step_durations": {
|
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"forecasting": {"min": 30, "max": 120}, # seconds
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"production": {"min": 60, "max": 300},
|
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"procurement": {"min": 45, "max": 180},
|
||||
"notification": {"min": 15, "max": 60}
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},
|
||||
"error_scenarios": [
|
||||
{"type": "forecasting_timeout", "message": "Forecasting service timeout - retrying"},
|
||||
{"type": "production_unavailable", "message": "Production service temporarily unavailable"},
|
||||
{"type": "procurement_failure", "message": "Procurement service connection failed"},
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{"type": "notification_error", "message": "Notification service rate limit exceeded"}
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||||
]
|
||||
}
|
||||
}
|
||||
|
||||
results = []
|
||||
|
||||
# Seed San Pablo (Individual Bakery)
|
||||
result_san_pablo = await generate_orchestration_for_tenant(
|
||||
db,
|
||||
DEMO_TENANT_SAN_PABLO,
|
||||
"Panadería San Pablo (Individual Bakery)",
|
||||
"individual_bakery",
|
||||
config
|
||||
)
|
||||
results.append(result_san_pablo)
|
||||
|
||||
# Seed La Espiga (Central Bakery)
|
||||
result_la_espiga = await generate_orchestration_for_tenant(
|
||||
db,
|
||||
DEMO_TENANT_LA_ESPIGA,
|
||||
"Panadería La Espiga (Central Bakery)",
|
||||
"central_bakery",
|
||||
config
|
||||
)
|
||||
results.append(result_la_espiga)
|
||||
|
||||
total_runs = sum(r["runs_created"] for r in results)
|
||||
total_steps = sum(r["steps_created"] for r in results)
|
||||
|
||||
logger.info("=" * 80)
|
||||
logger.info("✅ Demo Orchestration Runs Seeding Completed")
|
||||
logger.info("=" * 80)
|
||||
|
||||
return {
|
||||
"results": results,
|
||||
"total_runs_created": total_runs,
|
||||
"total_steps_created": total_steps,
|
||||
"status": "completed"
|
||||
}
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main execution function"""
|
||||
logger.info("Demo Orchestration Runs Seeding Script Starting")
|
||||
logger.info("Mode: %s", os.getenv("DEMO_MODE", "development"))
|
||||
logger.info("Log Level: %s", os.getenv("LOG_LEVEL", "INFO"))
|
||||
|
||||
# Get database URL from environment
|
||||
database_url = os.getenv("ORCHESTRATOR_DATABASE_URL") or os.getenv("DATABASE_URL")
|
||||
if not database_url:
|
||||
logger.error("❌ ORCHESTRATOR_DATABASE_URL or DATABASE_URL environment variable must be set")
|
||||
return 1
|
||||
|
||||
# Ensure asyncpg driver
|
||||
if database_url.startswith("postgresql://"):
|
||||
database_url = database_url.replace("postgresql://", "postgresql+asyncpg://", 1)
|
||||
|
||||
logger.info("Connecting to orchestrator database")
|
||||
|
||||
# Create async engine
|
||||
engine = create_async_engine(
|
||||
database_url,
|
||||
echo=False,
|
||||
pool_pre_ping=True,
|
||||
pool_size=5,
|
||||
max_overflow=10
|
||||
)
|
||||
|
||||
async_session = sessionmaker(
|
||||
engine,
|
||||
class_=AsyncSession,
|
||||
expire_on_commit=False
|
||||
)
|
||||
|
||||
try:
|
||||
async with async_session() as session:
|
||||
result = await seed_all(session)
|
||||
|
||||
logger.info("")
|
||||
logger.info("📊 Seeding Summary:")
|
||||
logger.info(f" ✅ Total Runs: {result['total_runs_created']}")
|
||||
logger.info(f" ✅ Total Steps: {result['total_steps_created']}")
|
||||
logger.info(f" ✅ Status: {result['status']}")
|
||||
logger.info("")
|
||||
|
||||
# Print per-tenant details
|
||||
for tenant_result in result["results"]:
|
||||
tenant_id = tenant_result["tenant_id"]
|
||||
runs = tenant_result["runs_created"]
|
||||
steps = tenant_result["steps_created"]
|
||||
skipped = tenant_result.get("skipped", False)
|
||||
status = "SKIPPED (already exists)" if skipped else f"CREATED {runs} runs, {steps} steps"
|
||||
logger.info(f" Tenant {tenant_id}: {status}")
|
||||
|
||||
logger.info("")
|
||||
logger.info("🎉 Success! Orchestration runs are ready for demo sessions.")
|
||||
logger.info("")
|
||||
logger.info("Runs created:")
|
||||
logger.info(" • 12 Orchestration runs per tenant")
|
||||
logger.info(" • Various statuses: completed, running, failed, cancelled, pending")
|
||||
logger.info(" • Different types: scheduled, manual, test")
|
||||
logger.info(" • Performance metrics tracking")
|
||||
logger.info("")
|
||||
logger.info("Note: All IDs are pre-defined and hardcoded for cross-service consistency")
|
||||
logger.info("")
|
||||
|
||||
return 0
|
||||
|
||||
except Exception as e:
|
||||
logger.error("=" * 80)
|
||||
logger.error("❌ Demo Orchestration Runs Seeding Failed")
|
||||
logger.error("=" * 80)
|
||||
logger.error("Error: %s", str(e))
|
||||
logger.error("", exc_info=True)
|
||||
return 1
|
||||
finally:
|
||||
await engine.dispose()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit_code = asyncio.run(main())
|
||||
sys.exit(exit_code)
|
||||
Reference in New Issue
Block a user