#!/usr/bin/env python3
"""
WBS-9.2: snapshot_admin 성능 벤치마크 도구

목표: 테이블 로드 시간 측정 및 최적화 기준 제시
- P99 < 2초 달성
- 동시 10개 테이블 PASS
"""

import time
import json
import requests
import statistics
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Tuple
import sys

# Config
ADMIN_URL = "http://localhost:5000/api"
TABLES = [
    "positions",
    "data_feed",
    "macro",
    "performance",
    "orders",
    "cash_positions",
    "portfolio_summary",
    "risk_metrics",
    "sector_allocation",
    "sector_flows"
]
NUM_RUNS = 10
CONCURRENT_LIMIT = 10
P99_TARGET_MS = 2000  # 2초


class PerformanceBenchmark:
    def __init__(self, admin_url: str, tables: List[str]):
        self.admin_url = admin_url
        self.tables = tables
        self.results = {
            "timestamp": datetime.now().isoformat(),
            "tables": {},
            "concurrent": {},
            "summary": {}
        }

    def _call_table(self, table_name: str) -> Tuple[str, float, int]:
        """Call a single table API endpoint and return timing."""
        url = f"{self.admin_url}/{table_name}"
        try:
            start = time.time()
            response = requests.get(url, timeout=5)
            elapsed_ms = (time.time() - start) * 1000
            status = response.status_code
            return table_name, elapsed_ms, status
        except Exception as e:
            return table_name, None, 0

    def benchmark_single_table(self, table_name: str, num_runs: int = NUM_RUNS):
        """Benchmark a single table with multiple runs."""
        times = []
        errors = 0

        for i in range(num_runs):
            table, elapsed_ms, status = self._call_table(table_name)
            if status == 200 and elapsed_ms is not None:
                times.append(elapsed_ms)
            else:
                errors += 1

        if not times:
            self.results["tables"][table_name] = {
                "status": "FAILED",
                "errors": errors,
                "runs": num_runs
            }
            return

        sorted_times = sorted(times)
        p99_idx = max(0, int(len(sorted_times) * 0.99) - 1)

        self.results["tables"][table_name] = {
            "status": "PASS" if sorted_times[-1] <= P99_TARGET_MS else "SLOW",
            "runs": num_runs,
            "min_ms": round(min(times), 2),
            "max_ms": round(max(times), 2),
            "mean_ms": round(statistics.mean(times), 2),
            "median_ms": round(statistics.median(times), 2),
            "stdev_ms": round(statistics.stdev(times) if len(times) > 1 else 0, 2),
            "p99_ms": round(sorted_times[p99_idx], 2),
            "errors": errors
        }

    def benchmark_concurrent(self, num_concurrent: int = CONCURRENT_LIMIT):
        """Benchmark concurrent table loads."""
        concurrent_times = []

        with ThreadPoolExecutor(max_workers=num_concurrent) as executor:
            futures = {
                executor.submit(self._call_table, table): table
                for table in self.tables
            }

            start = time.time()
            results_map = {}

            for future in as_completed(futures):
                table_name, elapsed_ms, status = future.result()
                if status == 200 and elapsed_ms is not None:
                    results_map[table_name] = elapsed_ms
                    concurrent_times.append(elapsed_ms)

            total_elapsed = (time.time() - start) * 1000

        if concurrent_times:
            sorted_concurrent = sorted(concurrent_times)
            p99_idx = max(0, int(len(sorted_concurrent) * 0.99) - 1)

            self.results["concurrent"]["parallel_load"] = {
                "num_concurrent": num_concurrent,
                "num_tables": len(results_map),
                "total_wall_time_ms": round(total_elapsed, 2),
                "min_table_ms": round(min(concurrent_times), 2),
                "max_table_ms": round(max(concurrent_times), 2),
                "p99_table_ms": round(sorted_concurrent[p99_idx], 2),
                "per_table_times": {k: round(v, 2) for k, v in results_map.items()},
                "status": "PASS" if sorted_concurrent[p99_idx] <= P99_TARGET_MS else "SLOW"
            }

    def generate_summary(self):
        """Generate summary statistics."""
        table_results = self.results["tables"]
        passed = sum(1 for r in table_results.values() if r.get("status") == "PASS")
        failed = sum(1 for r in table_results.values() if r.get("status") in ["SLOW", "FAILED"])

        all_p99_times = [
            r["p99_ms"] for r in table_results.values()
            if "p99_ms" in r
        ]

        self.results["summary"] = {
            "total_tables": len(table_results),
            "passed": passed,
            "failed": failed,
            "overall_status": "PASS" if failed == 0 else "NEEDS_OPTIMIZATION",
            "max_p99_ms": max(all_p99_times) if all_p99_times else None,
            "p99_target_ms": P99_TARGET_MS,
            "target_met": max(all_p99_times or [0]) <= P99_TARGET_MS
        }

    def print_report(self):
        """Print formatted report."""
        print("\n" + "=" * 70)
        print("SNAPSHOT_ADMIN PERFORMANCE BENCHMARK REPORT")
        print("=" * 70)
        print(f"Timestamp: {self.results['timestamp']}")
        print(f"Target P99: {P99_TARGET_MS}ms (< 2 seconds)\n")

        # Individual table results
        print("TABLE PERFORMANCE:")
        print("-" * 70)
        for table_name in sorted(self.results["tables"].keys()):
            r = self.results["tables"][table_name]
            if r["status"] in ["PASS", "SLOW"]:
                status_marker = "✓" if r["status"] == "PASS" else "⚠"
                print(f"{status_marker} {table_name:25} P99: {r['p99_ms']:7.2f}ms "
                      f"(mean: {r['mean_ms']:7.2f}ms, max: {r['max_ms']:7.2f}ms)")
            else:
                print(f"✗ {table_name:25} FAILED ({r['errors']} errors)")

        # Concurrent performance
        if "parallel_load" in self.results["concurrent"]:
            c = self.results["concurrent"]["parallel_load"]
            print("\nCONCURRENT LOAD ({} tables):".format(c["num_concurrent"]))
            print("-" * 70)
            print(f"Wall time: {c['total_wall_time_ms']:.2f}ms")
            print(f"Table P99: {c['p99_table_ms']:.2f}ms (max single: {c['max_table_ms']:.2f}ms)")
            print(f"Status: {'PASS' if c['status'] == 'PASS' else 'NEEDS_OPTIMIZATION'}")

        # Summary
        s = self.results["summary"]
        print("\nSUMMARY:")
        print("-" * 70)
        print(f"Status: {s['overall_status']}")
        print(f"Passed: {s['passed']}/{s['total_tables']} tables")
        print(f"Max P99: {s['max_p99_ms']:.2f}ms (target: {s['p99_target_ms']}ms)")
        print(f"Target Met: {'YES ✓' if s['target_met'] else 'NO ✗ (optimization needed)'}")
        print("=" * 70 + "\n")

    def save_report(self, output_file: str = None):
        """Save report to JSON file."""
        if not output_file:
            output_file = f"Temp/benchmark_snapshot_admin_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"

        Path(output_file).parent.mkdir(parents=True, exist_ok=True)
        with open(output_file, 'w') as f:
            json.dump(self.results, f, indent=2)
        print(f"Report saved: {output_file}")

    def run_full_benchmark(self):
        """Run complete benchmark suite."""
        print("Starting snapshot_admin performance benchmark...")
        print(f"Target: P99 < {P99_TARGET_MS}ms")
        print(f"Tables: {', '.join(self.tables)}")
        print(f"Runs per table: {NUM_RUNS}\n")

        # Single table benchmark
        print("Phase 1: Single table performance...")
        for table in self.tables:
            self.benchmark_single_table(table, NUM_RUNS)
            print(f"  ✓ {table}")

        # Concurrent benchmark
        print(f"\nPhase 2: Concurrent load ({CONCURRENT_LIMIT} tables)...")
        self.benchmark_concurrent(CONCURRENT_LIMIT)

        # Generate summary
        self.generate_summary()

        # Output
        self.print_report()
        self.save_report()

        return self.results


def optimize_recommendations(results: Dict) -> List[str]:
    """Generate optimization recommendations."""
    recommendations = []
    summary = results.get("summary", {})

    if not summary.get("target_met"):
        max_p99 = summary.get("max_p99_ms")
        if max_p99 and max_p99 > P99_TARGET_MS:
            ratio = max_p99 / P99_TARGET_MS
            if ratio > 2:
                recommendations.append(
                    f"Critical: P99 is {ratio:.1f}x target. "
                    "Consider table indexing, materialized views, or caching."
                )
            elif ratio > 1.2:
                recommendations.append(
                    f"P99 exceeds target by {(ratio-1)*100:.0f}%. "
                    "Optimize database queries or add caching layer."
                )

    # Check specific slow tables
    for table, result in results.get("tables", {}).items():
        if result.get("status") == "SLOW" and "p99_ms" in result:
            p99 = result["p99_ms"]
            if p99 > 3000:
                recommendations.append(
                    f"Table '{table}': {p99:.0f}ms. Consider reducing data volume or adding indexes."
                )

    if not recommendations:
        recommendations.append("✓ Performance meets targets. Continue monitoring.")

    return recommendations


if __name__ == "__main__":
    try:
        # Check server availability
        response = requests.head(f"{ADMIN_URL}/health", timeout=2)
        if response.status_code not in [200, 404]:
            print(f"Error: snapshot_admin not available at {ADMIN_URL}")
            print("Start server: python tools/run_snapshot_admin_server_v1.py")
            sys.exit(1)
    except Exception as e:
        print(f"Error connecting to {ADMIN_URL}: {e}")
        print("Start server: python tools/run_snapshot_admin_server_v1.py")
        sys.exit(1)

    # Run benchmark
    benchmark = PerformanceBenchmark(ADMIN_URL, TABLES)
    results = benchmark.run_full_benchmark()

    # Print recommendations
    print("OPTIMIZATION RECOMMENDATIONS:")
    print("-" * 70)
    for rec in optimize_recommendations(results):
        print(f"• {rec}")
    print()

    # Exit code based on target met
    sys.exit(0 if results["summary"]["target_met"] else 1)