Industry Standards Benchmarks

import json
import numpy as np
from typing import List, Dict, Tuple, Set
from dataclasses import dataclass
from collections import defaultdict
 
@dataclass
class TRECResult:
    query_id: str
    doc_id: str
    rank: int
    score: float
    run_id: str
 
class TRECBenchmark:
    def __init__(self, collection_name: str = "trec_memory"):
        self.collection_name = collection_name
        self.official_metrics = [
            'map', 'gm_map', 'bpref', 'recip_rank', 'P_5', 'P_10', 'P_15', 'P_20', 'P_30',
            'P_100', 'P_200', 'P_500', 'P_1000', 'recall_5', 'recall_10', 'recall_15',
            'recall_20', 'recall_30', 'recall_100', 'recall_200', 'recall_500', 'recall_1000',
            'ndcg', 'ndcg_cut_5', 'ndcg_cut_10', 'ndcg_cut_15', 'ndcg_cut_20', 'ndcg_cut_30',
            'ndcg_cut_100', 'ndcg_cut_200', 'ndcg_cut_500', 'ndcg_cut_1000'
        ]
 
    def run_official_trec_evaluation(self, retrieval_system, topic_set: str = "2023"):
        """
        Run official TREC evaluation protocol on agent memory system
 
        Args:
            retrieval_system: Agent memory system to evaluate
            topic_set: TREC topic set year (e.g., "2023", "2022")
        """
        # Load official TREC topics and qrels
        topics = self._load_trec_topics(topic_set)
        qrels = self._load_trec_qrels(topic_set)
 
        # Generate retrieval results
        trec_results = []
        for topic in topics:
            query_id = topic['id']
            query_text = topic['title'] + " " + topic.get('description', '')
 
            # Retrieve documents using the memory system
            retrieved_docs = retrieval_system.retrieve(query_text, top_k=1000)
 
            # Convert to TREC format
            for rank, doc in enumerate(retrieved_docs, 1):
                result = TRECResult(
                    query_id=query_id,
                    doc_id=doc.id,
                    rank=rank,
                    score=doc.score,
                    run_id=f"{self.collection_name}_run"
                )
                trec_results.append(result)
 
        # Save results in TREC format
        results_file = f"trec_results_{topic_set}.txt"
        self._save_trec_results(trec_results, results_file)
 
        # Evaluate using trec_eval
        evaluation_metrics = self._run_trec_eval(results_file, qrels)
 
        return evaluation_metrics
 
    def _load_trec_topics(self, topic_set: str):
        """Load TREC topics for evaluation"""
        # This would load from official TREC topic files
        # For demonstration, we'll create sample topics
        sample_topics = [
            {
                'id': '1',
                'title': 'machine learning algorithms',
                'description': 'What are the most effective machine learning algorithms for classification tasks?',
                'narrative': 'Relevant documents should discuss various machine learning algorithms...'
            },
            {
                'id': '2',
                'title': 'neural network optimization',
                'description': 'How can neural networks be optimized for better performance?',
                'narrative': 'Documents about optimization techniques for neural networks are relevant...'
            },
            # Add more topics based on actual TREC data
        ]
 
        print(f"Loaded {len(sample_topics)} TREC topics for evaluation")
        return sample_topics
 
    def _load_trec_qrels(self, topic_set: str):
        """Load TREC qrels (query relevance judgments)"""
        # This would load from official TREC qrels files
        # Format: query_id 0 doc_id relevance_level
        sample_qrels = {
            '1': {
                'doc_001': 2,  # Highly relevant
                'doc_002': 1,  # Partially relevant
                'doc_003': 0,  # Not relevant
                'doc_004': 2   # Highly relevant
            },
            '2': {
                'doc_005': 1,
                'doc_006': 2,
                'doc_007': 0,
                'doc_008': 1
            }
        }
 
        print(f"Loaded qrels for {len(sample_qrels)} queries")
        return sample_qrels
 
    def _save_trec_results(self, results: List[TRECResult], filename: str):
        """Save results in official TREC format"""
        with open(filename, 'w') as f:
            for result in results:
                # TREC format: query_id Q0 doc_id rank score run_id
                f.write(f"{result.query_id} Q0 {result.doc_id} {result.rank} {result.score} {result.run_id}\n")
 
    def _run_trec_eval(self, results_file: str, qrels: Dict):
        """Run official trec_eval evaluation"""
        # This would call the official trec_eval binary
        # For demonstration, we'll implement basic metrics
        evaluation_results = {}
 
        # Parse results file
        results_by_query = defaultdict(list)
        with open(results_file, 'r') as f:
            for line in f:
                parts = line.strip().split()
                query_id = parts[0]
                doc_id = parts[2]
                rank = int(parts[3])
                score = float(parts[4])
 
                results_by_query[query_id].append({
                    'doc_id': doc_id,
                    'rank': rank,
                    'score': score
                })
 
        # Calculate metrics for each query
        query_metrics = {}
        for query_id, query_results in results_by_query.items():
            if query_id not in qrels:
                continue
 
            query_qrels = qrels[query_id]
            query_metrics[query_id] = self._calculate_query_metrics(query_results, query_qrels)
 
        # Aggregate metrics across queries
        for metric in self.official_metrics:
            metric_values = [qm.get(metric, 0) for qm in query_metrics.values()]
            evaluation_results[metric] = np.mean(metric_values) if metric_values else 0
 
        evaluation_results['num_queries'] = len(query_metrics)
        evaluation_results['query_metrics'] = query_metrics
 
        return evaluation_results
 
    def _calculate_query_metrics(self, results: List[Dict], qrels: Dict):
        """Calculate metrics for a single query"""
        metrics = {}
 
        # Sort results by rank
        results.sort(key=lambda x: x['rank'])
 
        # Calculate precision at different cutoffs
        for k in [5, 10, 15, 20, 30, 100, 200, 500, 1000]:
            if k <= len(results):
                top_k_docs = results[:k]
                relevant_retrieved = sum(1 for doc in top_k_docs
                                       if doc['doc_id'] in qrels and qrels[doc['doc_id']] > 0)
                metrics[f'P_{k}'] = relevant_retrieved / k
            else:
                metrics[f'P_{k}'] = 0
 
        # Calculate recall at different cutoffs
        total_relevant = sum(1 for relevance in qrels.values() if relevance > 0)
        for k in [5, 10, 15, 20, 30, 100, 200, 500, 1000]:
            if k <= len(results) and total_relevant > 0:
                top_k_docs = results[:k]
                relevant_retrieved = sum(1 for doc in top_k_docs
                                       if doc['doc_id'] in qrels and qrels[doc['doc_id']] > 0)
                metrics[f'recall_{k}'] = relevant_retrieved / total_relevant
            else:
                metrics[f'recall_{k}'] = 0
 
        # Calculate Average Precision (AP)
        ap = self._calculate_average_precision(results, qrels)
        metrics['map'] = ap
 
        # Calculate NDCG
        for k in [5, 10, 15, 20, 30, 100, 200, 500, 1000]:
            ndcg_k = self._calculate_ndcg_at_k(results, qrels, k)
            metrics[f'ndcg_cut_{k}'] = ndcg_k
 
        # Calculate overall NDCG
        metrics['ndcg'] = self._calculate_ndcg_at_k(results, qrels, len(results))
 
        # Calculate reciprocal rank
        metrics['recip_rank'] = self._calculate_reciprocal_rank(results, qrels)
 
        # Calculate bpref
        metrics['bpref'] = self._calculate_bpref(results, qrels)
 
        return metrics
 
    def _calculate_average_precision(self, results: List[Dict], qrels: Dict):
        """Calculate Average Precision for a query"""
        relevant_retrieved = 0
        ap_sum = 0
 
        for i, result in enumerate(results):
            doc_id = result['doc_id']
            if doc_id in qrels and qrels[doc_id] > 0:
                relevant_retrieved += 1
                precision_at_i = relevant_retrieved / (i + 1)
                ap_sum += precision_at_i
 
        total_relevant = sum(1 for relevance in qrels.values() if relevance > 0)
        return ap_sum / total_relevant if total_relevant > 0 else 0
 
    def _calculate_ndcg_at_k(self, results: List[Dict], qrels: Dict, k: int):
        """Calculate NDCG@k"""
        import math
 
        # DCG@k
        dcg = 0
        for i, result in enumerate(results[:k]):
            doc_id = result['doc_id']
            relevance = qrels.get(doc_id, 0)
            if i == 0:
                dcg += relevance
            else:
                dcg += relevance / math.log2(i + 1)
 
        # IDCG@k (ideal DCG)
        ideal_relevances = sorted(qrels.values(), reverse=True)[:k]
        idcg = 0
        for i, relevance in enumerate(ideal_relevances):
            if i == 0:
                idcg += relevance
            else:
                idcg += relevance / math.log2(i + 1)
 
        return dcg / idcg if idcg > 0 else 0
 
    def _calculate_reciprocal_rank(self, results: List[Dict], qrels: Dict):
        """Calculate Reciprocal Rank (RR)"""
        for i, result in enumerate(results):
            doc_id = result['doc_id']
            if doc_id in qrels and qrels[doc_id] > 0:
                return 1 / (i + 1)
        return 0
 
    def _calculate_bpref(self, results: List[Dict], qrels: Dict):
        """Calculate Binary Preference (bpref)"""
        relevant_docs = [doc_id for doc_id, rel in qrels.items() if rel > 0]
        non_relevant_docs = [doc_id for doc_id, rel in qrels.items() if rel == 0]
 
        if not relevant_docs:
            return 0
 
        bpref_sum = 0
        for rel_doc in relevant_docs:
            # Find rank of relevant document
            rel_rank = None
            for i, result in enumerate(results):
                if result['doc_id'] == rel_doc:
                    rel_rank = i
                    break
 
            if rel_rank is not None:
                # Count non-relevant docs ranked higher
                higher_non_rel = 0
                for i in range(rel_rank):
                    if results[i]['doc_id'] in non_relevant_docs:
                        higher_non_rel += 1
 
                # bpref contribution
                max_non_rel = min(len(non_relevant_docs), len(relevant_docs))
                bpref_sum += 1 - (higher_non_rel / max_non_rel) if max_non_rel > 0 else 1
 
        return bpref_sum / len(relevant_docs)
 
# Example usage
trec_benchmark = TRECBenchmark("agent_memory_system")
 
# This would be your actual retrieval system
class MockRetrievalSystem:
    def retrieve(self, query, top_k=1000):
        # Mock implementation - replace with your actual system
        return [
            type('Doc', (), {'id': f'doc_{i:03d}', 'score': 1.0 - i * 0.001})()
            for i in range(1, top_k + 1)
        ]
 
# Run TREC evaluation
mock_system = MockRetrievalSystem()
trec_results = trec_benchmark.run_official_trec_evaluation(mock_system, "2023")
 
print("TREC Evaluation Results:")
for metric, value in trec_results.items():
    if isinstance(value, float):
        print(f"{metric}: {value:.4f}")

class BEIRStandardBenchmark:
    def __init__(self):
        self.beir_datasets = [
            'trec-covid', 'nfcorpus', 'nq', 'hotpotqa', 'fiqa', 'arguana',
            'webis-touche2020', 'cqadupstack', 'quora', 'dbpedia-entity',
            'scidocs', 'fever', 'climate-fever', 'scifact', 'robust04'
        ]
        self.standard_metrics = ['NDCG@10', 'MAP', 'Recall@100', 'Precision@10']
 
    def run_beir_evaluation_suite(self, retrieval_system, datasets: List[str] = None):
        """
        Run comprehensive BEIR evaluation across multiple datasets
 
        Args:
            retrieval_system: System to evaluate
            datasets: List of BEIR datasets to use (default: all)
        """
        if datasets is None:
            datasets = self.beir_datasets
 
        overall_results = {}
        dataset_results = {}
 
        for dataset_name in datasets:
            print(f"Evaluating on {dataset_name}...")
 
            try:
                dataset_result = self._evaluate_single_beir_dataset(
                    retrieval_system, dataset_name
                )
                dataset_results[dataset_name] = dataset_result
 
                # Aggregate for overall score
                for metric in self.standard_metrics:
                    if metric not in overall_results:
                        overall_results[metric] = []
                    overall_results[metric].append(dataset_result.get(metric, 0))
 
            except Exception as e:
                print(f"Error evaluating {dataset_name}: {e}")
                continue
 
        # Calculate overall scores
        aggregated_results = {}
        for metric, values in overall_results.items():
            aggregated_results[f'{metric}_mean'] = np.mean(values)
            aggregated_results[f'{metric}_std'] = np.std(values)
 
        return {
            'aggregated_results': aggregated_results,
            'dataset_results': dataset_results,
            'datasets_evaluated': len(dataset_results),
            'beir_score': self._calculate_beir_score(aggregated_results)
        }
 
    def _evaluate_single_beir_dataset(self, retrieval_system, dataset_name: str):
        """Evaluate on a single BEIR dataset"""
        from beir import util, LoggingHandler
        from beir.datasets.data_loader import GenericDataLoader
        from beir.retrieval.evaluation import EvaluateRetrieval
 
        # Download and load dataset
        dataset_path = util.download_and_unzip(dataset_name, "datasets")
        corpus, queries, qrels = GenericDataLoader(dataset_path).load(split="test")
 
        # Create retrieval wrapper for BEIR
        class BEIRRetrievalWrapper:
            def __init__(self, system):
                self.system = system
 
            def search(self, corpus, queries, top_k, *args, **kwargs):
                results = {}
                for query_id, query_text in queries.items():
                    retrieved_docs = self.system.retrieve(query_text, top_k=top_k)
 
                    # Convert to BEIR format
                    results[query_id] = {}
                    for doc in retrieved_docs:
                        results[query_id][doc.id] = doc.score
 
                return results
 
        # Run evaluation
        retrieval_wrapper = BEIRRetrievalWrapper(retrieval_system)
        evaluator = EvaluateRetrieval(retrieval_wrapper)
 
        # Evaluate with standard metrics
        results = evaluator.evaluate(corpus, queries, qrels)
 
        return results
 
    def _calculate_beir_score(self, aggregated_results: Dict):
        """Calculate overall BEIR score"""
        # Standard BEIR scoring uses average of NDCG@10 across datasets
        ndcg_10_mean = aggregated_results.get('NDCG@10_mean', 0)
        return ndcg_10_mean
 
    def generate_beir_report(self, results: Dict):
        """Generate BEIR evaluation report"""
        report = ["# BEIR Standard Evaluation Report\n"]
 
        # Overall performance
        report.append("## Overall Performance")
        aggregated = results['aggregated_results']
        for metric in self.standard_metrics:
            mean_key = f'{metric}_mean'
            std_key = f'{metric}_std'
            if mean_key in aggregated:
                report.append(f"- **{metric}**: {aggregated[mean_key]:.4f} ± {aggregated[std_key]:.4f}")
 
        report.append(f"\n**BEIR Score**: {results['beir_score']:.4f}")
 
        # Per-dataset breakdown
        report.append("\n## Per-Dataset Results")
        report.append("| Dataset | NDCG@10 | MAP | Recall@100 | Precision@10 |")
        report.append("|---------|---------|-----|------------|--------------|")
 
        for dataset, metrics in results['dataset_results'].items():
            ndcg = metrics.get('NDCG@10', 0)
            map_score = metrics.get('MAP', 0)
            recall = metrics.get('Recall@100', 0)
            precision = metrics.get('Precision@10', 0)
 
            report.append(f"| {dataset} | {ndcg:.4f} | {map_score:.4f} | {recall:.4f} | {precision:.4f} |")
 
        return "\n".join(report)
 
# Example usage
beir_benchmark = BEIRStandardBenchmark()
 
# Run BEIR evaluation (with subset for demo)
test_datasets = ['scifact', 'nfcorpus', 'arguana']
beir_results = beir_benchmark.run_beir_evaluation_suite(
    your_retrieval_system, datasets=test_datasets
)
 
# Generate report
beir_report = beir_benchmark.generate_beir_report(beir_results)
print(beir_report)
 
print(f"\nBEIR Score: {beir_results['beir_score']:.4f}")

class MLOpsPerformanceBenchmark:
    def __init__(self):
        self.performance_standards = {
            'latency': {
                'p50_ms': 100,    # 50th percentile
                'p95_ms': 300,    # 95th percentile
                'p99_ms': 1000    # 99th percentile
            },
            'throughput': {
                'min_qps': 10,    # Queries per second
                'target_qps': 100,
                'max_qps': 1000
            },
            'availability': {
                'uptime_percent': 99.9,
                'error_rate_percent': 0.1
            },
            'accuracy': {
                'min_precision': 0.8,
                'min_recall': 0.7,
                'min_f1': 0.75
            }
        }
 
    def benchmark_against_standards(self, memory_system, test_duration_seconds: int = 3600):
        """
        Benchmark agent memory system against MLOps standards
 
        Args:
            memory_system: System to benchmark
            test_duration_seconds: Duration to run benchmark
        """
        print(f"Running MLOps benchmark for {test_duration_seconds} seconds...")
 
        # Run comprehensive performance test
        performance_results = self._run_performance_test(memory_system, test_duration_seconds)
 
        # Evaluate against standards
        compliance_results = self._evaluate_compliance(performance_results)
 
        return {
            'performance_results': performance_results,
            'compliance_results': compliance_results,
            'overall_compliance': self._calculate_overall_compliance(compliance_results)
        }
 
    def _run_performance_test(self, memory_system, duration: int):
        """Run comprehensive performance test"""
        import threading
        import time
        from concurrent.futures import ThreadPoolExecutor
        from queue import Queue
 
        # Metrics collection
        latencies = []
        errors = []
        successful_operations = 0
        start_time = time.time()
        metrics_queue = Queue()
 
        def worker():
            """Worker thread for performance testing"""
            nonlocal successful_operations
 
            while time.time() - start_time < duration:
                operation_start = time.time()
 
                try:
                    # Perform test operation
                    query = f"test query {np.random.randint(1000)}"
                    results = memory_system.retrieve_similar(query, top_k=10)
 
                    operation_end = time.time()
                    latency = operation_end - operation_start
 
                    metrics_queue.put({
                        'type': 'latency',
                        'value': latency,
                        'timestamp': operation_end
                    })
 
                    successful_operations += 1
 
                except Exception as e:
                    metrics_queue.put({
                        'type': 'error',
                        'value': str(e),
                        'timestamp': time.time()
                    })
 
                # Small delay to avoid overwhelming the system
                time.sleep(0.01)
 
        # Start worker threads
        num_workers = 10
        with ThreadPoolExecutor(max_workers=num_workers) as executor:
            futures = [executor.submit(worker) for _ in range(num_workers)]
 
            # Collect metrics while test is running
            while time.time() - start_time < duration:
                try:
                    metric = metrics_queue.get(timeout=1)
                    if metric['type'] == 'latency':
                        latencies.append(metric['value'])
                    elif metric['type'] == 'error':
                        errors.append(metric)
                except:
                    continue
 
            # Wait for all workers to complete
            for future in futures:
                future.result()
 
        total_time = time.time() - start_time
        total_operations = successful_operations + len(errors)
 
        # Calculate performance metrics
        return {
            'latency_stats': {
                'p50_ms': np.percentile(latencies, 50) * 1000 if latencies else 0,
                'p95_ms': np.percentile(latencies, 95) * 1000 if latencies else 0,
                'p99_ms': np.percentile(latencies, 99) * 1000 if latencies else 0,
                'mean_ms': np.mean(latencies) * 1000 if latencies else 0
            },
            'throughput_stats': {
                'qps': successful_operations / total_time if total_time > 0 else 0,
                'total_operations': total_operations,
                'successful_operations': successful_operations
            },
            'reliability_stats': {
                'error_rate_percent': (len(errors) / total_operations * 100) if total_operations > 0 else 0,
                'success_rate_percent': (successful_operations / total_operations * 100) if total_operations > 0 else 0,
                'total_errors': len(errors)
            },
            'test_duration': total_time
        }
 
    def _evaluate_compliance(self, performance_results: Dict):
        """Evaluate compliance against standards"""
        compliance = {}
 
        # Latency compliance
        latency_results = performance_results['latency_stats']
        latency_standards = self.performance_standards['latency']
 
        compliance['latency'] = {
            'p50_compliant': latency_results['p50_ms'] <= latency_standards['p50_ms'],
            'p95_compliant': latency_results['p95_ms'] <= latency_standards['p95_ms'],
            'p99_compliant': latency_results['p99_ms'] <= latency_standards['p99_ms'],
            'actual_p50': latency_results['p50_ms'],
            'actual_p95': latency_results['p95_ms'],
            'actual_p99': latency_results['p99_ms']
        }
 
        # Throughput compliance
        throughput_results = performance_results['throughput_stats']
        throughput_standards = self.performance_standards['throughput']
 
        compliance['throughput'] = {
            'min_qps_met': throughput_results['qps'] >= throughput_standards['min_qps'],
            'target_qps_met': throughput_results['qps'] >= throughput_standards['target_qps'],
            'actual_qps': throughput_results['qps']
        }
 
        # Reliability compliance
        reliability_results = performance_results['reliability_stats']
        reliability_standards = self.performance_standards['availability']
 
        compliance['reliability'] = {
            'error_rate_acceptable': reliability_results['error_rate_percent'] <= reliability_standards['error_rate_percent'],
            'actual_error_rate': reliability_results['error_rate_percent'],
            'uptime_equivalent': 100 - reliability_results['error_rate_percent']
        }
 
        return compliance
 
    def _calculate_overall_compliance(self, compliance_results: Dict):
        """Calculate overall compliance score"""
        checks = []
 
        # Latency checks
        latency = compliance_results['latency']
        checks.extend([
            latency['p50_compliant'],
            latency['p95_compliant'],
            latency['p99_compliant']
        ])
 
        # Throughput checks
        throughput = compliance_results['throughput']
        checks.extend([
            throughput['min_qps_met'],
            throughput['target_qps_met']
        ])
 
        # Reliability checks
        reliability = compliance_results['reliability']
        checks.append(reliability['error_rate_acceptable'])
 
        # Calculate compliance percentage
        compliance_score = sum(checks) / len(checks)
 
        return {
            'compliance_percentage': compliance_score * 100,
            'total_checks': len(checks),
            'passed_checks': sum(checks),
            'is_compliant': compliance_score >= 0.8  # 80% threshold
        }
 
    def generate_compliance_report(self, benchmark_results: Dict):
        """Generate MLOps compliance report"""
        perf = benchmark_results['performance_results']
        comp = benchmark_results['compliance_results']
        overall = benchmark_results['overall_compliance']
 
        report = ["# MLOps Performance Compliance Report\n"]
 
        # Overall compliance
        report.append("## Overall Compliance")
        report.append(f"- **Compliance Score**: {overall['compliance_percentage']:.1f}%")
        report.append(f"- **Checks Passed**: {overall['passed_checks']}/{overall['total_checks']}")
        report.append(f"- **Status**: {'✅ COMPLIANT' if overall['is_compliant'] else '❌ NON-COMPLIANT'}")
 
        # Latency compliance
        report.append("\n## Latency Compliance")
        latency_comp = comp['latency']
        latency_std = self.performance_standards['latency']
 
        report.append(f"- **P50 Latency**: {latency_comp['actual_p50']:.1f}ms (target: {latency_std['p50_ms']}ms) "
                     f"{'✅' if latency_comp['p50_compliant'] else '❌'}")
        report.append(f"- **P95 Latency**: {latency_comp['actual_p95']:.1f}ms (target: {latency_std['p95_ms']}ms) "
                     f"{'✅' if latency_comp['p95_compliant'] else '❌'}")
        report.append(f"- **P99 Latency**: {latency_comp['actual_p99']:.1f}ms (target: {latency_std['p99_ms']}ms) "
                     f"{'✅' if latency_comp['p99_compliant'] else '❌'}")
 
        # Throughput compliance
        report.append("\n## Throughput Compliance")
        throughput_comp = comp['throughput']
        throughput_std = self.performance_standards['throughput']
 
        report.append(f"- **Actual QPS**: {throughput_comp['actual_qps']:.1f}")
        report.append(f"- **Min QPS Met**: {'✅' if throughput_comp['min_qps_met'] else '❌'} "
                     f"(target: {throughput_std['min_qps']})")
        report.append(f"- **Target QPS Met**: {'✅' if throughput_comp['target_qps_met'] else '❌'} "
                     f"(target: {throughput_std['target_qps']})")
 
        # Reliability compliance
        report.append("\n## Reliability Compliance")
        reliability_comp = comp['reliability']
        reliability_std = self.performance_standards['availability']
 
        report.append(f"- **Error Rate**: {reliability_comp['actual_error_rate']:.2f}% "
                     f"(target: <{reliability_std['error_rate_percent']}%) "
                     f"{'✅' if reliability_comp['error_rate_acceptable'] else '❌'}")
        report.append(f"- **Effective Uptime**: {reliability_comp['uptime_equivalent']:.2f}%")
 
        return "\n".join(report)
 
# Example usage
mlops_benchmark = MLOpsPerformanceBenchmark()
 
# Run MLOps compliance benchmark
mlops_results = mlops_benchmark.benchmark_against_standards(
    your_memory_system, test_duration_seconds=300  # 5 minutes for demo
)
 
# Generate compliance report
compliance_report = mlops_benchmark.generate_compliance_report(mlops_results)
print(compliance_report)
 
# Check overall compliance
if mlops_results['overall_compliance']['is_compliant']:
    print("\n🎉 System is MLOps compliant!")
else:
    print("\n⚠️  System needs improvement to meet MLOps standards")

class CloudNativeScalabilityBenchmark:
    def __init__(self):
        self.scalability_standards = {
            'horizontal_scaling': {
                'max_scale_out_factor': 10,     # Can scale to 10x instances
                'scale_out_time_seconds': 300,   # Scale out within 5 minutes
                'scale_in_time_seconds': 180,    # Scale in within 3 minutes
            },
            'resource_efficiency': {
                'cpu_utilization_target': 0.7,   # 70% CPU utilization
                'memory_utilization_target': 0.8, # 80% memory utilization
                'storage_efficiency': 0.85        # 85% storage efficiency
            },
            'fault_tolerance': {
                'recovery_time_seconds': 120,     # Recover within 2 minutes
                'data_durability': 0.999999,      # 99.9999% durability
                'availability_sla': 0.999         # 99.9% availability
            }
        }
 
    def benchmark_cloud_native_capabilities(self, deployment_config: Dict):
        """
        Benchmark cloud-native scalability and resilience
 
        Args:
            deployment_config: Configuration for cloud deployment
        """
        results = {}
 
        # Test horizontal scaling
        print("Testing horizontal scaling...")
        scaling_results = self._test_horizontal_scaling(deployment_config)
        results['horizontal_scaling'] = scaling_results
 
        # Test resource efficiency
        print("Testing resource efficiency...")
        efficiency_results = self._test_resource_efficiency(deployment_config)
        results['resource_efficiency'] = efficiency_results
 
        # Test fault tolerance
        print("Testing fault tolerance...")
        fault_tolerance_results = self._test_fault_tolerance(deployment_config)
        results['fault_tolerance'] = fault_tolerance_results
 
        # Evaluate compliance
        compliance = self._evaluate_cloud_native_compliance(results)
        results['compliance'] = compliance
 
        return results
 
    def _test_horizontal_scaling(self, deployment_config: Dict):
        """Test horizontal scaling capabilities"""
        import time
 
        scaling_results = {
            'scale_out_tests': [],
            'scale_in_tests': [],
            'load_distribution_tests': []
        }
 
        # Simulate scale-out test
        for scale_factor in [2, 5, 10]:
            print(f"Testing scale-out to {scale_factor}x instances...")
 
            start_time = time.time()
 
            # Simulate scaling operation
            scale_out_successful = self._simulate_scale_operation(
                deployment_config, 'out', scale_factor
            )
 
            scale_out_time = time.time() - start_time
 
            scaling_results['scale_out_tests'].append({
                'scale_factor': scale_factor,
                'scale_time_seconds': scale_out_time,
                'successful': scale_out_successful,
                'meets_standard': scale_out_time <= self.scalability_standards['horizontal_scaling']['scale_out_time_seconds']
            })
 
            # Test load distribution after scaling
            if scale_out_successful:
                load_distribution = self._test_load_distribution(deployment_config, scale_factor)
                scaling_results['load_distribution_tests'].append({
                    'scale_factor': scale_factor,
                    'load_distribution_variance': load_distribution
                })
 
        # Simulate scale-in test
        for scale_factor in [5, 2, 1]:
            print(f"Testing scale-in to {scale_factor}x instances...")
 
            start_time = time.time()
 
            scale_in_successful = self._simulate_scale_operation(
                deployment_config, 'in', scale_factor
            )
 
            scale_in_time = time.time() - start_time
 
            scaling_results['scale_in_tests'].append({
                'scale_factor': scale_factor,
                'scale_time_seconds': scale_in_time,
                'successful': scale_in_successful,
                'meets_standard': scale_in_time <= self.scalability_standards['horizontal_scaling']['scale_in_time_seconds']
            })
 
        return scaling_results
 
    def _test_resource_efficiency(self, deployment_config: Dict):
        """Test resource utilization efficiency"""
        efficiency_results = {}
 
        # Simulate resource monitoring
        resource_metrics = self._simulate_resource_monitoring(deployment_config)
 
        # Calculate efficiency metrics
        efficiency_results['cpu_efficiency'] = {
            'average_utilization': resource_metrics['cpu']['average'],
            'peak_utilization': resource_metrics['cpu']['peak'],
            'meets_target': abs(resource_metrics['cpu']['average'] - self.scalability_standards['resource_efficiency']['cpu_utilization_target']) <= 0.1
        }
 
        efficiency_results['memory_efficiency'] = {
            'average_utilization': resource_metrics['memory']['average'],
            'peak_utilization': resource_metrics['memory']['peak'],
            'meets_target': abs(resource_metrics['memory']['average'] - self.scalability_standards['resource_efficiency']['memory_utilization_target']) <= 0.1
        }
 
        efficiency_results['storage_efficiency'] = {
            'utilization': resource_metrics['storage']['utilization'],
            'compression_ratio': resource_metrics['storage']['compression_ratio'],
            'meets_target': resource_metrics['storage']['utilization'] >= self.scalability_standards['resource_efficiency']['storage_efficiency']
        }
 
        return efficiency_results
 
    def _test_fault_tolerance(self, deployment_config: Dict):
        """Test fault tolerance and recovery capabilities"""
        fault_tolerance_results = {
            'recovery_tests': [],
            'data_durability_tests': [],
            'availability_tests': []
        }
 
        # Test recovery from different failure scenarios
        failure_scenarios = ['instance_failure', 'network_partition', 'storage_failure']
 
        for scenario in failure_scenarios:
            print(f"Testing recovery from {scenario}...")
 
            # Simulate failure
            failure_start = time.time()
            self._simulate_failure(deployment_config, scenario)
 
            # Measure recovery time
            recovery_time = self._measure_recovery_time(deployment_config, scenario)
            recovery_end = time.time()
 
            total_recovery_time = recovery_end - failure_start
 
            fault_tolerance_results['recovery_tests'].append({
                'scenario': scenario,
                'recovery_time_seconds': total_recovery_time,
                'meets_standard': total_recovery_time <= self.scalability_standards['fault_tolerance']['recovery_time_seconds'],
                'data_loss': self._check_data_loss(deployment_config, scenario)
            })
 
        # Test data durability
        durability_result = self._test_data_durability(deployment_config)
        fault_tolerance_results['data_durability_tests'] = durability_result
 
        # Test availability under various conditions
        availability_result = self._test_availability(deployment_config)
        fault_tolerance_results['availability_tests'] = availability_result
 
        return fault_tolerance_results
 
    def _simulate_scale_operation(self, config: Dict, direction: str, scale_factor: int):
        """Simulate scaling operation"""
        import time
 
        # Simulate scaling delay
        base_delay = 30  # Base 30 seconds for scaling
        scale_delay = scale_factor * 10  # Additional delay based on scale
 
        time.sleep(min(base_delay + scale_delay, 300) / 60)  # Scale down for demo
 
        # Simulate success/failure (90% success rate)
        return np.random.random() > 0.1
 
    def _test_load_distribution(self, config: Dict, scale_factor: int):
        """Test load distribution across scaled instances"""
        # Simulate load measurements across instances
        instance_loads = np.random.normal(0.7, 0.1, scale_factor)  # Mean 70% utilization
 
        # Calculate variance in load distribution
        load_variance = np.var(instance_loads)
 
        return load_variance
 
    def _simulate_resource_monitoring(self, config: Dict):
        """Simulate resource monitoring data"""
        return {
            'cpu': {
                'average': np.random.normal(0.68, 0.05),  # Around 68% average
                'peak': np.random.normal(0.85, 0.1)      # Around 85% peak
            },
            'memory': {
                'average': np.random.normal(0.78, 0.08),  # Around 78% average
                'peak': np.random.normal(0.92, 0.05)      # Around 92% peak
            },
            'storage': {
                'utilization': np.random.normal(0.87, 0.03),  # Around 87% utilization
                'compression_ratio': np.random.normal(2.5, 0.2)  # 2.5x compression
            }
        }
 
    def _simulate_failure(self, config: Dict, scenario: str):
        """Simulate different failure scenarios"""
        print(f"Simulating {scenario}...")
        time.sleep(1)  # Simulate failure occurrence
 
    def _measure_recovery_time(self, config: Dict, scenario: str):
        """Measure recovery time from failure"""
        # Simulate recovery process
        recovery_times = {
            'instance_failure': np.random.normal(90, 20),     # ~90 seconds
            'network_partition': np.random.normal(45, 10),    # ~45 seconds
            'storage_failure': np.random.normal(150, 30)      # ~150 seconds
        }
 
        return max(recovery_times.get(scenario, 120), 10)  # Minimum 10 seconds
 
    def _check_data_loss(self, config: Dict, scenario: str):
        """Check for data loss after failure"""
        # Simulate data integrity check
        data_loss_probability = {
            'instance_failure': 0.01,     # 1% chance
            'network_partition': 0.001,   # 0.1% chance
            'storage_failure': 0.05       # 5% chance
        }
 
        return np.random.random() < data_loss_probability.get(scenario, 0.01)
 
    def _test_data_durability(self, config: Dict):
        """Test data durability over time"""
        # Simulate data durability measurement
        total_objects = 1000000
        lost_objects = np.random.poisson(1)  # Very low loss rate
 
        durability = 1 - (lost_objects / total_objects)
 
        return {
            'measured_durability': durability,
            'meets_standard': durability >= self.scalability_standards['fault_tolerance']['data_durability'],
            'total_objects_tested': total_objects,
            'objects_lost': lost_objects
        }
 
    def _test_availability(self, config: Dict):
        """Test system availability"""
        # Simulate availability measurement over time period
        total_time_hours = 24 * 30  # 30 days
        downtime_minutes = np.random.exponential(5)  # Average 5 minutes downtime
 
        availability = 1 - (downtime_minutes / (total_time_hours * 60))
 
        return {
            'measured_availability': availability,
            'meets_standard': availability >= self.scalability_standards['fault_tolerance']['availability_sla'],
            'total_time_hours': total_time_hours,
            'downtime_minutes': downtime_minutes
        }
 
    def _evaluate_cloud_native_compliance(self, results: Dict):
        """Evaluate compliance with cloud-native standards"""
        compliance_checks = []
 
        # Horizontal scaling compliance
        scaling_results = results['horizontal_scaling']
 
        # Check if can scale to at least 10x
        max_successful_scale = max([t['scale_factor'] for t in scaling_results['scale_out_tests'] if t['successful']], default=1)
        compliance_checks.append(max_successful_scale >= self.scalability_standards['horizontal_scaling']['max_scale_out_factor'])
 
        # Check scaling time compliance
        scale_out_times_compliant = all([t['meets_standard'] for t in scaling_results['scale_out_tests'] if t['successful']])
        scale_in_times_compliant = all([t['meets_standard'] for t in scaling_results['scale_in_tests'] if t['successful']])
        compliance_checks.extend([scale_out_times_compliant, scale_in_times_compliant])
 
        # Resource efficiency compliance
        efficiency_results = results['resource_efficiency']
        compliance_checks.extend([
            efficiency_results['cpu_efficiency']['meets_target'],
            efficiency_results['memory_efficiency']['meets_target'],
            efficiency_results['storage_efficiency']['meets_target']
        ])
 
        # Fault tolerance compliance
        fault_tolerance_results = results['fault_tolerance']
 
        recovery_compliant = all([t['meets_standard'] for t in fault_tolerance_results['recovery_tests']])
        durability_compliant = fault_tolerance_results['data_durability_tests']['meets_standard']
        availability_compliant = fault_tolerance_results['availability_tests']['meets_standard']
 
        compliance_checks.extend([recovery_compliant, durability_compliant, availability_compliant])
 
        # Calculate overall compliance
        overall_compliance = sum(compliance_checks) / len(compliance_checks)
 
        return {
            'compliance_percentage': overall_compliance * 100,
            'total_checks': len(compliance_checks),
            'passed_checks': sum(compliance_checks),
            'is_cloud_native_compliant': overall_compliance >= 0.8,
            'detailed_checks': {
                'horizontal_scaling': scale_out_times_compliant and scale_in_times_compliant,
                'resource_efficiency': all([
                    efficiency_results['cpu_efficiency']['meets_target'],
                    efficiency_results['memory_efficiency']['meets_target'],
                    efficiency_results['storage_efficiency']['meets_target']
                ]),
                'fault_tolerance': recovery_compliant and durability_compliant and availability_compliant
            }
        }
 
# Example usage
cloud_benchmark = CloudNativeScalabilityBenchmark()
 
# Configure deployment for testing
deployment_config = {
    'platform': 'kubernetes',
    'min_replicas': 1,
    'max_replicas': 20,
    'resource_requests': {'cpu': '500m', 'memory': '1Gi'},
    'resource_limits': {'cpu': '2000m', 'memory': '4Gi'}
}
 
# Run cloud-native benchmark
cloud_results = cloud_benchmark.benchmark_cloud_native_capabilities(deployment_config)
 
# Check compliance
compliance = cloud_results['compliance']
print(f"Cloud-Native Compliance: {compliance['compliance_percentage']:.1f}%")
 
if compliance['is_cloud_native_compliant']:
    print("✅ System meets cloud-native standards!")
else:
    print("❌ System needs improvements for cloud-native compliance")
 
    # Show which areas need work
    for area, compliant in compliance['detailed_checks'].items():
        status = "✅" if compliant else "❌"
        print(f"  {area}: {status}")