class FlinkMemoryOptimizer:
    def __init__(self):
        self.memory_configs = {}
    
    def generate_optimized_config(self, workload_type, data_volume):
        """워크로드 타입에 따른 메모리 설정 최적화"""
        
        if workload_type == "streaming":
            return self.get_streaming_memory_config(data_volume)
        elif workload_type == "batch":
            return self.get_batch_memory_config(data_volume)
        elif workload_type == "machine_learning":
            return self.get_ml_memory_config(data_volume)
        else:
            return self.get_default_memory_config()
    
    def get_streaming_memory_config(self, data_volume):
        """스트리밍 워크로드용 메모리 설정"""
        if data_volume == "low":  # < 1GB/min
            return {
                "taskmanager.memory.process.size": "2g",
                "taskmanager.memory.managed.size": "512m",
                "taskmanager.memory.jvm-metaspace.size": "256m",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.2"
            }
        elif data_volume == "medium":  # 1-10GB/min
            return {
                "taskmanager.memory.process.size": "4g",
                "taskmanager.memory.managed.size": "1g",
                "taskmanager.memory.jvm-metaspace.size": "512m",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.2"
            }
        else:  # > 10GB/min
            return {
                "taskmanager.memory.process.size": "8g",
                "taskmanager.memory.managed.size": "2g",
                "taskmanager.memory.jvm-metaspace.size": "1g",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.2"
            }
    
    def get_batch_memory_config(self, data_volume):
        """배치 워크로드용 메모리 설정"""
        if data_volume == "small":  # < 100GB
            return {
                "taskmanager.memory.process.size": "4g",
                "taskmanager.memory.managed.size": "2g",
                "taskmanager.memory.jvm-metaspace.size": "512m",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.1"
            }
        elif data_volume == "large":  # 100GB - 1TB
            return {
                "taskmanager.memory.process.size": "8g",
                "taskmanager.memory.managed.size": "4g",
                "taskmanager.memory.jvm-metaspace.size": "1g",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.1"
            }
        else:  # > 1TB
            return {
                "taskmanager.memory.process.size": "16g",
                "taskmanager.memory.managed.size": "8g",
                "taskmanager.memory.jvm-metaspace.size": "2g",
                "taskmanager.memory.jvm-overhead.fraction": "0.1",
                "taskmanager.memory.network.fraction": "0.1"
            }
    
    def get_ml_memory_config(self, data_volume):
        """머신러닝 워크로드용 메모리 설정"""
        return {
            "taskmanager.memory.process.size": "12g",
            "taskmanager.memory.managed.size": "6g",
            "taskmanager.memory.jvm-metaspace.size": "1g",
            "taskmanager.memory.jvm-overhead.fraction": "0.15",
            "taskmanager.memory.network.fraction": "0.1"
        }
    
    def get_default_memory_config(self):
        """기본 메모리 설정"""
        return {
            "taskmanager.memory.process.size": "4g",
            "taskmanager.memory.managed.size": "1g",
            "taskmanager.memory.jvm-metaspace.size": "512m",
            "taskmanager.memory.jvm-overhead.fraction": "0.1",
            "taskmanager.memory.network.fraction": "0.2"
        }

class NetworkOptimizer:
    def __init__(self):
        self.network_configs = {}
    
    def optimize_network_performance(self, cluster_size, data_throughput):
        """네트워크 성능 최적화"""
        
        config = {
            # 네트워크 버퍼 설정
            "taskmanager.network.memory.fraction": "0.2",
            "taskmanager.network.memory.min": "128mb",
            "taskmanager.network.memory.max": "1gb",
            
            # 네트워크 스레드 설정
            "taskmanager.network.netty.num-arenas": str(min(cluster_size, 4)),
            "taskmanager.network.netty.server.numThreads": "4",
            "taskmanager.network.netty.client.numThreads": "4",
            
            # 연결 설정
            "taskmanager.network.netty.server.backlog": "0",
            "taskmanager.network.netty.client.connectTimeoutSec": "10",
            
            # 버퍼 설정
            "taskmanager.network.memory.buffers-per-channel": "2",
            "taskmanager.network.memory.floating-buffers-per-gate": "8",
        }
        
        # 데이터 처리량에 따른 추가 최적화
        if data_throughput > 1000:  # MB/s
            config.update({
                "taskmanager.network.memory.fraction": "0.3",
                "taskmanager.network.memory.max": "2gb",
                "taskmanager.network.memory.buffers-per-channel": "4",
                "taskmanager.network.memory.floating-buffers-per-gate": "16",
            })
        
        return config
    
    def optimize_shuffle_performance(self, data_size, parallelism):
        """Shuffle 성능 최적화"""
        
        # 데이터 크기에 따른 파티션 수 조정
        optimal_partitions = min(max(data_size // (100 * 1024 * 1024), 1), 1000)
        
        config = {
            "taskmanager.numberOfTaskSlots": str(min(parallelism, 4)),
            "parallelism.default": str(parallelism),
            
            # Shuffle 최적화
            "taskmanager.memory.segment-size": "32kb",
            "taskmanager.network.sort-shuffle.min-parallelism": "1",
            "taskmanager.network.sort-shuffle.min-buffers": "64",
            
            # 압축 설정
            "taskmanager.network.compression.enabled": "true",
            "taskmanager.network.compression.codec": "lz4",
        }
        
        return config

class StateBackendOptimizer:
    def __init__(self):
        self.rocksdb_configs = {}
    
    def optimize_rocksdb_backend(self, state_size, access_pattern):
        """RocksDB 백엔드 최적화"""
        
        if access_pattern == "read_heavy":
            return self.get_read_optimized_config(state_size)
        elif access_pattern == "write_heavy":
            return self.get_write_optimized_config(state_size)
        elif access_pattern == "mixed":
            return self.get_balanced_config(state_size)
        else:
            return self.get_default_config()
    
    def get_read_optimized_config(self, state_size):
        """읽기 최적화 설정"""
        block_cache_size = min(state_size // 4, 512 * 1024 * 1024)  # 512MB max
        
        return {
            # 블록 캐시 최적화
            "state.backend.rocksdb.block.cache-size": f"{block_cache_size}b",
            "state.backend.rocksdb.block.cache-size-per-column-family": f"{block_cache_size // 4}b",
            
            # 블록 크기 최적화
            "state.backend.rocksdb.block.blocksize": "16kb",
            "state.backend.rocksdb.block.bloom-filter-bits-per-key": "10",
            
            # 압축 설정
            "state.backend.rocksdb.compaction.level0-file-num-compaction-trigger": "4",
            "state.backend.rocksdb.compaction.level0-slowdown-writes-trigger": "20",
            "state.backend.rocksdb.compaction.level0-stop-writes-trigger": "36",
            
            # 읽기 최적화
            "state.backend.rocksdb.readoptions.async-io": "true",
            "state.backend.rocksdb.readoptions.cache-index-and-filter-blocks": "true",
        }
    
    def get_write_optimized_config(self, state_size):
        """쓰기 최적화 설정"""
        write_buffer_size = min(state_size // 8, 128 * 1024 * 1024)  # 128MB max
        
        return {
            # 쓰기 버퍼 최적화
            "state.backend.rocksdb.write-buffer-size": f"{write_buffer_size}b",
            "state.backend.rocksdb.max-write-buffer-number": "4",
            "state.backend.rocksdb.min-write-buffer-number-to-merge": "2",
            
            # WAL 설정
            "state.backend.rocksdb.wal.enabled": "true",
            "state.backend.rocksdb.wal.sync": "false",
            
            # 압축 설정
            "state.backend.rocksdb.compaction.level0-file-num-compaction-trigger": "2",
            "state.backend.rocksdb.compaction.level0-slowdown-writes-trigger": "8",
            "state.backend.rocksdb.compaction.level0-stop-writes-trigger": "12",
            
            # 쓰기 최적화
            "state.backend.rocksdb.writeoptions.sync": "false",
            "state.backend.rocksdb.writeoptions.disable-wal": "false",
        }
    
    def get_balanced_config(self, state_size):
        """균형 잡힌 설정"""
        return {
            # 블록 캐시
            "state.backend.rocksdb.block.cache-size": f"{min(state_size // 8, 256 * 1024 * 1024)}b",
            
            # 쓰기 버퍼
            "state.backend.rocksdb.write-buffer-size": f"{min(state_size // 16, 64 * 1024 * 1024)}b",
            "state.backend.rocksdb.max-write-buffer-number": "3",
            
            # 압축 설정
            "state.backend.rocksdb.compaction.level0-file-num-compaction-trigger": "4",
            "state.backend.rocksdb.compaction.level0-slowdown-writes-trigger": "20",
            "state.backend.rocksdb.compaction.level0-stop-writes-trigger": "36",
            
            # 일반 설정
            "state.backend.rocksdb.block.blocksize": "16kb",
            "state.backend.rocksdb.wal.enabled": "true",
        }

class FlinkDisasterRecovery:
    def __init__(self, kubernetes_client, flink_client):
        self.k8s_client = kubernetes_client
        self.flink_client = flink_client
        self.recovery_strategies = {}
    
    def setup_disaster_recovery(self):
        """재해 복구 시스템 설정"""
        
        # 1. 체크포인트 모니터링
        self.setup_checkpoint_monitoring()
        
        # 2. 자동 재시작 설정
        self.setup_auto_restart()
        
        # 3. 백업 및 복원 시스템
        self.setup_backup_system()
        
        # 4. 장애 전파 방지
        self.setup_circuit_breaker()
    
    def setup_checkpoint_monitoring(self):
        """체크포인트 모니터링 설정"""
        checkpoint_config = {
            "execution.checkpointing.interval": "60s",
            "execution.checkpointing.mode": "EXACTLY_ONCE",
            "execution.checkpointing.timeout": "600s",
            "execution.checkpointing.min-pause": "60s",
            "execution.checkpointing.max-concurrent-checkpoints": "1",
            "execution.checkpointing.unaligned": "true",
            "execution.checkpointing.alignment-timeout": "0s",
            
            # 체크포인트 저장소
            "state.backend": "rocksdb",
            "state.checkpoints.dir": "s3://flink-checkpoints/checkpoints",
            "state.savepoints.dir": "s3://flink-savepoints/savepoints",
            
            # 고가용성
            "high-availability": "kubernetes",
            "high-availability.storageDir": "s3://flink-ha/ha",
            "high-availability.cluster-id": "flink-cluster"
        }
        
        return checkpoint_config
    
    def setup_auto_restart(self):
        """자동 재시작 설정"""
        restart_config = {
            "restart-strategy": "exponential-delay",
            "restart-strategy.exponential-delay.initial-backoff": "10s",
            "restart-strategy.exponential-delay.max-backoff": "2min",
            "restart-strategy.exponential-delay.backoff-multiplier": "2.0",
            "restart-strategy.exponential-delay.reset-backoff-threshold": "10min",
            "restart-strategy.exponential-delay.jitter-factor": "0.1"
        }
        
        return restart_config
    
    def setup_backup_system(self):
        """백업 시스템 설정"""
        backup_config = {
            # 자동 세이브포인트
            "execution.savepoint.interval": "1h",
            "execution.savepoint.trigger": "periodic",
            
            # 백업 저장소
            "state.backend.incremental": "true",
            "state.backend.rocksdb.checkpoint.transfer.thread.num": "4",
            
            # 압축 설정
            "state.backend.rocksdb.compression.type": "LZ4",
            "state.backend.rocksdb.compaction.level.max-size-level-base": "256mb"
        }
        
        return backup_config
    
    def setup_circuit_breaker(self):
        """서킷 브레이커 설정"""
        circuit_breaker_config = {
            # 네트워크 타임아웃
            "taskmanager.network.netty.client.connectTimeoutSec": "30",
            "taskmanager.network.netty.client.requestTimeoutSec": "60",
            
            # 재시도 설정
            "taskmanager.network.retries": "3",
            "taskmanager.network.retry-delay": "10s",
            
            # 백프레셔 설정
            "taskmanager.network.memory.fraction": "0.2",
            "taskmanager.network.memory.max": "1gb"
        }
        
        return circuit_breaker_config
    
    def execute_disaster_recovery(self, failure_type, job_name):
        """재해 복구 실행"""
        
        if failure_type == "job_failure":
            return self.recover_from_job_failure(job_name)
        elif failure_type == "cluster_failure":
            return self.recover_from_cluster_failure(job_name)
        elif failure_type == "data_corruption":
            return self.recover_from_data_corruption(job_name)
        else:
            return self.recover_from_unknown_failure(job_name)
    
    def recover_from_job_failure(self, job_name):
        """작업 실패에서 복구"""
        recovery_steps = [
            "1. 실패한 작업 중지",
            "2. 최신 체크포인트 확인",
            "3. 상태 백엔드 검증",
            "4. 작업 재시작",
            "5. 복구 상태 모니터링"
        ]
        
        # 실제 복구 로직 구현
        try:
            # 1. 작업 중지
            self.flink_client.cancel_job(job_name)
            
            # 2. 최신 체크포인트 확인
            latest_checkpoint = self.get_latest_checkpoint(job_name)
            
            # 3. 상태 검증
            if self.validate_checkpoint(latest_checkpoint):
                # 4. 작업 재시작
                self.flink_client.restart_job(job_name, latest_checkpoint)
                return {"status": "success", "message": "Job recovered successfully"}
            else:
                # 5. 세이브포인트에서 복구 시도
                latest_savepoint = self.get_latest_savepoint(job_name)
                if latest_savepoint:
                    self.flink_client.restart_job(job_name, latest_savepoint)
                    return {"status": "success", "message": "Job recovered from savepoint"}
                else:
                    return {"status": "failed", "message": "No valid checkpoint or savepoint found"}
                    
        except Exception as e:
            return {"status": "error", "message": f"Recovery failed: {str(e)}"}
    
    def recover_from_cluster_failure(self, job_name):
        """클러스터 실패에서 복구"""
        recovery_steps = [
            "1. 클러스터 상태 확인",
            "2. Kubernetes 리소스 재시작",
            "3. Flink 클러스터 재배포",
            "4. 작업 복구",
            "5. 전체 시스템 검증"
        ]
        
        try:
            # 1. 클러스터 상태 확인
            cluster_status = self.check_cluster_status()
            
            if cluster_status["healthy"]:
                return self.recover_from_job_failure(job_name)
            else:
                # 2. 클러스터 재시작
                self.restart_flink_cluster()
                
                # 3. 작업 복구
                return self.recover_from_job_failure(job_name)
                
        except Exception as e:
            return {"status": "error", "message": f"Cluster recovery failed: {str(e)}"}
    
    def get_latest_checkpoint(self, job_name):
        """최신 체크포인트 조회"""
        checkpoints = self.flink_client.list_checkpoints(job_name)
        if checkpoints:
            return max(checkpoints, key=lambda x: x["timestamp"])
        return None
    
    def get_latest_savepoint(self, job_name):
        """최신 세이브포인트 조회"""
        savepoints = self.flink_client.list_savepoints(job_name)
        if savepoints:
            return max(savepoints, key=lambda x: x["timestamp"])
        return None
    
    def validate_checkpoint(self, checkpoint):
        """체크포인트 유효성 검증"""
        if not checkpoint:
            return False
        
        # 체크포인트 파일 존재 확인
        # 체크포인트 메타데이터 검증
        # 체크포인트 무결성 검사
        
        return True
    
    def check_cluster_status(self):
        """클러스터 상태 확인"""
        try:
            # Kubernetes 클러스터 상태 확인
            pods = self.k8s_client.list_pods_for_all_namespaces(
                label_selector="app=flink"
            )
            
            healthy_pods = 0
            total_pods = len(pods.items)
            
            for pod in pods.items:
                if pod.status.phase == "Running":
                    healthy_pods += 1
            
            health_ratio = healthy_pods / total_pods if total_pods > 0 else 0
            
            return {
                "healthy": health_ratio > 0.8,
                "health_ratio": health_ratio,
                "total_pods": total_pods,
                "healthy_pods": healthy_pods
            }
            
        except Exception as e:
            return {"healthy": False, "error": str(e)}
    
    def restart_flink_cluster(self):
        """Flink 클러스터 재시작"""
        try:
            # JobManager 재시작
            self.k8s_client.delete_namespaced_deployment(
                name="flink-jobmanager",
                namespace="flink-production"
            )
            
            # TaskManager 재시작
            self.k8s_client.delete_namespaced_deployment(
                name="flink-taskmanager",
                namespace="flink-production"
            )
            
            # 재시작 대기
            import time
            time.sleep(30)
            
            return {"status": "success", "message": "Cluster restarted successfully"}
            
        except Exception as e:
            return {"status": "error", "message": f"Cluster restart failed: {str(e)}"}

class FlinkOperationsAutomation:
    def __init__(self):
        self.operation_templates = {}
        self.monitoring_thresholds = {}
    
    def setup_operational_automation(self):
        """운영 자동화 시스템 설정"""
        
        # 1. 자동 스케일링
        self.setup_auto_scaling()
        
        # 2. 자동 백업
        self.setup_auto_backup()
        
        # 3. 자동 업데이트
        self.setup_auto_update()
        
        # 4. 자동 모니터링
        self.setup_auto_monitoring()
    
    def setup_auto_scaling(self):
        """자동 스케일링 설정"""
        scaling_config = {
            "kubernetes.operator.auto-scaling.enabled": "true",
            "kubernetes.operator.auto-scaling.metrics.window": "60s",
            "kubernetes.operator.auto-scaling.metrics.delay": "30s",
            "kubernetes.operator.auto-scaling.scale-up.grace-period": "60s",
            "kubernetes.operator.auto-scaling.scale-down.grace-period": "300s",
            "kubernetes.operator.auto-scaling.target.utilization": "0.8",
            "kubernetes.operator.auto-scaling.min.replicas": "2",
            "kubernetes.operator.auto-scaling.max.replicas": "10"
        }
        
        return scaling_config
    
    def setup_auto_backup(self):
        """자동 백업 설정"""
        backup_schedule = {
            "checkpoint_interval": "1h",
            "savepoint_interval": "24h",
            "retention_policy": "7d",
            "backup_storage": "s3://flink-backups/",
            "compression": "gzip",
            "encryption": "aes256"
        }
        
        return backup_schedule
    
    def setup_auto_update(self):
        """자동 업데이트 설정"""
        update_config = {
            "update_strategy": "rolling",
            "update_window": "02:00-06:00",
            "health_check_timeout": "300s",
            "rollback_threshold": "10%",
            "canary_percentage": "10%"
        }
        
        return update_config
    
    def setup_auto_monitoring(self):
        """자동 모니터링 설정"""
        monitoring_config = {
            "metrics_collection_interval": "10s",
            "alert_evaluation_interval": "30s",
            "retention_period": "30d",
            "anomaly_detection": "enabled",
            "performance_baseline": "auto"
        }
        
        return monitoring_config

class FlinkPerformanceOptimizer:
    def __init__(self):
        self.optimization_rules = {}
    
    def optimize_for_workload(self, workload_type, metrics):
        """워크로드 타입에 따른 성능 최적화"""
        
        if workload_type == "low_latency":
            return self.optimize_for_low_latency(metrics)
        elif workload_type == "high_throughput":
            return self.optimize_for_high_throughput(metrics)
        elif workload_type == "memory_intensive":
            return self.optimize_for_memory_intensive(metrics)
        else:
            return self.optimize_for_balanced(metrics)
    
    def optimize_for_low_latency(self, metrics):
        """저지연 최적화"""
        optimizations = {
            "execution.buffer-timeout": "1ms",
            "execution.checkpointing.interval": "10s",
            "execution.checkpointing.timeout": "30s",
            "taskmanager.network.memory.fraction": "0.1",
            "taskmanager.network.memory.min": "64mb",
            "taskmanager.network.memory.max": "256mb",
            "taskmanager.network.netty.server.backlog": "0",
            "taskmanager.network.netty.client.connectTimeoutSec": "5"
        }
        
        return optimizations
    
    def optimize_for_high_throughput(self, metrics):
        """고처리량 최적화"""
        optimizations = {
            "execution.buffer-timeout": "100ms",
            "execution.checkpointing.interval": "300s",
            "execution.checkpointing.timeout": "600s",
            "taskmanager.network.memory.fraction": "0.3",
            "taskmanager.network.memory.min": "512mb",
            "taskmanager.network.memory.max": "2gb",
            "taskmanager.network.memory.buffers-per-channel": "4",
            "taskmanager.network.memory.floating-buffers-per-gate": "16"
        }
        
        return optimizations
    
    def optimize_for_memory_intensive(self, metrics):
        """메모리 집약적 최적화"""
        optimizations = {
            "taskmanager.memory.managed.size": "4g",
            "taskmanager.memory.process.size": "8g",
            "state.backend.rocksdb.block.cache-size": "2gb",
            "state.backend.rocksdb.write-buffer-size": "256mb",
            "state.backend.rocksdb.max-write-buffer-number": "4",
            "state.backend.rocksdb.compaction.level0-file-num-compaction-trigger": "2"
        }
        
        return optimizations

class EnterpriseFlinkPlatform:
    def __init__(self):
        self.components = {
            "deployment": FlinkDeploymentManager(),
            "monitoring": FlinkMonitoringSystem(),
            "automation": FlinkOperationsAutomation(),
            "recovery": FlinkDisasterRecovery(),
            "optimization": FlinkPerformanceOptimizer()
        }
    
    def deploy_enterprise_platform(self):
        """엔터프라이즈 플랫폼 배포"""
        
        deployment_steps = [
            "1. 네임스페이스 및 RBAC 설정",
            "2. Flink Operator 설치",
            "3. 모니터링 스택 배포",
            "4. Flink 클러스터 배포",
            "5. 자동화 규칙 설정",
            "6. 알림 시스템 구성",
            "7. 백업 시스템 설정"
        ]
        
        for step in deployment_steps:
            print(f"Executing: {step}")
            self.execute_deployment_step(step)
    
    def execute_deployment_step(self, step):
        """배포 단계 실행"""
        if "네임스페이스" in step:
            self.create_namespaces_and_rbac()
        elif "Operator" in step:
            self.install_flink_operator()
        elif "모니터링" in step:
            self.deploy_monitoring_stack()
        elif "클러스터" in step:
            self.deploy_flink_cluster()
        elif "자동화" in step:
            self.setup_automation_rules()
        elif "알림" in step:
            self.configure_alerting()
        elif "백업" in step:
            self.setup_backup_system()
    
    def create_namespaces_and_rbac(self):
        """네임스페이스 및 RBAC 생성"""
        namespaces = [
            "flink-production",
            "flink-staging",
            "monitoring",
            "logging"
        ]
        
        for namespace in namespaces:
            self.create_namespace(namespace)
            self.create_rbac_for_namespace(namespace)
    
    def install_flink_operator(self):
        """Flink Operator 설치"""
        operator_manifest = """
        apiVersion: v1
        kind: Namespace
        metadata:
          name: flink-operator
        ---
        apiVersion: apps/v1
        kind: Deployment
        metadata:
          name: flink-operator
          namespace: flink-operator
        spec:
          replicas: 1
          selector:
            matchLabels:
              app: flink-operator
          template:
            metadata:
              labels:
                app: flink-operator
            spec:
              serviceAccountName: flink-operator
              containers:
              - name: flink-operator
                image: apache/flink-kubernetes-operator:1.7.0
                ports:
                - containerPort: 8080
                env:
                - name: OPERATOR_NAME
                  value: "flink-operator"
                - name: WATCH_NAMESPACE
                  value: ""
                resources:
                  requests:
                    memory: "256Mi"
                    cpu: "100m"
                  limits:
                    memory: "512Mi"
                    cpu: "500m"
        """
        
        self.apply_manifest(operator_manifest)
    
    def deploy_monitoring_stack(self):
        """모니터링 스택 배포"""
        monitoring_components = [
            "prometheus",
            "grafana",
            "alertmanager",
            "jaeger",
            "elasticsearch",
            "kibana"
        ]
        
        for component in monitoring_components:
            self.deploy_monitoring_component(component)
    
    def deploy_flink_cluster(self):
        """Flink 클러스터 배포"""
        cluster_configs = {
            "production": self.get_production_cluster_config(),
            "staging": self.get_staging_cluster_config(),
            "development": self.get_development_cluster_config()
        }
        
        for env, config in cluster_configs.items():
            self.deploy_cluster_for_environment(env, config)
    
    def get_production_cluster_config(self):
        """프로덕션 클러스터 설정"""
        return {
            "flinkConfiguration": {
                "taskmanager.numberOfTaskSlots": "8",
                "parallelism.default": "16",
                "state.backend": "rocksdb",
                "state.checkpoints.dir": "s3://prod-flink-checkpoints/checkpoints",
                "state.savepoints.dir": "s3://prod-flink-savepoints/savepoints",
                "execution.checkpointing.interval": "60s",
                "execution.checkpointing.mode": "EXACTLY_ONCE",
                "high-availability": "kubernetes",
                "high-availability.storageDir": "s3://prod-flink-ha/ha",
                "restart-strategy": "exponential-delay",
                "restart-strategy.exponential-delay.initial-backoff": "10s",
                "restart-strategy.exponential-delay.max-backoff": "2min",
                "metrics.reporter.prom.class": "org.apache.flink.metrics.prometheus.PrometheusReporter",
                "metrics.reporter.prom.port": "9249"
            },
            "jobManager": {
                "replicas": 2,
                "resource": {
                    "memory": "4g",
                    "cpu": "2000m"
                }
            },
            "taskManager": {
                "replicas": 5,
                "resource": {
                    "memory": "8g",
                    "cpu": "4000m"
                }
            }
        }
    
    def get_staging_cluster_config(self):
        """스테이징 클러스터 설정"""
        return {
            "flinkConfiguration": {
                "taskmanager.numberOfTaskSlots": "4",
                "parallelism.default": "8",
                "state.backend": "rocksdb",
                "state.checkpoints.dir": "s3://staging-flink-checkpoints/checkpoints",
                "execution.checkpointing.interval": "120s",
                "execution.checkpointing.mode": "AT_LEAST_ONCE",
                "restart-strategy": "fixed-delay",
                "restart-strategy.fixed-delay.attempts": "3",
                "restart-strategy.fixed-delay.delay": "10s"
            },
            "jobManager": {
                "replicas": 1,
                "resource": {
                    "memory": "2g",
                    "cpu": "1000m"
                }
            },
            "taskManager": {
                "replicas": 2,
                "resource": {
                    "memory": "4g",
                    "cpu": "2000m"
                }
            }
        }
    
    def get_development_cluster_config(self):
        """개발 클러스터 설정"""
        return {
            "flinkConfiguration": {
                "taskmanager.numberOfTaskSlots": "2",
                "parallelism.default": "2",
                "state.backend": "memory",
                "execution.checkpointing.interval": "300s",
                "execution.checkpointing.mode": "AT_LEAST_ONCE",
                "restart-strategy": "fixed-delay",
                "restart-strategy.fixed-delay.attempts": "1",
                "restart-strategy.fixed-delay.delay": "5s"
            },
            "jobManager": {
                "replicas": 1,
                "resource": {
                    "memory": "1g",
                    "cpu": "500m"
                }
            },
            "taskManager": {
                "replicas": 1,
                "resource": {
                    "memory": "2g",
                    "cpu": "1000m"
                }
            }
        }
    
    def setup_automation_rules(self):
        """자동화 규칙 설정"""
        automation_rules = {
            "auto_scaling": {
                "enabled": True,
                "min_replicas": 2,
                "max_replicas": 10,
                "target_cpu": 80,
                "target_memory": 80,
                "scale_up_cooldown": "60s",
                "scale_down_cooldown": "300s"
            },
            "auto_backup": {
                "enabled": True,
                "checkpoint_interval": "1h",
                "savepoint_interval": "24h",
                "retention_days": 7,
                "backup_storage": "s3://flink-backups/"
            },
            "auto_update": {
                "enabled": False,  # 프로덕션에서는 수동 승인 필요
                "update_window": "02:00-06:00",
                "canary_percentage": 10,
                "health_check_timeout": "300s"
            },
            "auto_recovery": {
                "enabled": True,
                "max_restart_attempts": 3,
                "restart_delay": "10s",
                "escalation_timeout": "300s"
            }
        }
        
        for rule_type, config in automation_rules.items():
            self.configure_automation_rule(rule_type, config)
    
    def configure_alerting(self):
        """알림 시스템 구성"""
        alert_rules = {
            "critical": [
                "FlinkJobFailed",
                "ClusterDown",
                "DataLoss",
                "CheckpointFailure"
            ],
            "warning": [
                "HighCheckpointDuration",
                "HighBackpressure",
                "HighMemoryUsage",
                "LowThroughput"
            ],
            "info": [
                "JobStarted",
                "JobCompleted",
                "ScalingEvent",
                "BackupCompleted"
            ]
        }
        
        notification_channels = {
            "email": {
                "enabled": True,
                "recipients": ["admin@company.com", "oncall@company.com"],
                "severity": ["critical", "warning"]
            },
            "slack": {
                "enabled": True,
                "webhook_url": "https://hooks.slack.com/services/...",
                "channels": ["#flink-alerts", "#data-team"],
                "severity": ["critical", "warning"]
            },
            "pagerduty": {
                "enabled": True,
                "integration_key": "integration_key_here",
                "severity": ["critical"]
            }
        }
        
        self.configure_alert_rules(alert_rules)
        self.configure_notification_channels(notification_channels)
    
    def setup_backup_system(self):
        """백업 시스템 설정"""
        backup_config = {
            "checkpoints": {
                "enabled": True,
                "interval": "1h",
                "retention": "7d",
                "storage": "s3://flink-checkpoints/",
                "compression": "gzip"
            },
            "savepoints": {
                "enabled": True,
                "interval": "24h",
                "retention": "30d",
                "storage": "s3://flink-savepoints/",
                "compression": "gzip"
            },
            "configurations": {
                "enabled": True,
                "interval": "1d",
                "retention": "90d",
                "storage": "s3://flink-configs/",
                "include": ["flink-conf.yaml", "log4j.properties"]
            },
            "state_backend": {
                "enabled": True,
                "interval": "1h",
                "retention": "7d",
                "storage": "s3://flink-state/",
                "encryption": "aes256"
            }
        }
        
        self.configure_backup_system(backup_config)
    
    def create_comprehensive_dashboard(self):
        """종합 대시보드 생성"""
        dashboard_config = {
            "title": "Enterprise Flink Platform Dashboard",
            "panels": [
                {
                    "title": "Cluster Overview",
                    "type": "stat",
                    "metrics": [
                        "flink_jobmanager_numRunningJobs",
                        "flink_jobmanager_numCompletedJobs",
                        "flink_jobmanager_numFailedJobs"
                    ]
                },
                {
                    "title": "Performance Metrics",
                    "type": "timeseries",
                    "metrics": [
                        "flink_taskmanager_job_task_operator_numRecordsInPerSecond",
                        "flink_taskmanager_job_task_operator_numRecordsOutPerSecond",
                        "flink_taskmanager_job_task_operator_latency"
                    ]
                },
                {
                    "title": "Resource Usage",
                    "type": "timeseries",
                    "metrics": [
                        "flink_taskmanager_Status_JVM_Memory_Heap_Used",
                        "flink_taskmanager_Status_JVM_CPU_Load",
                        "flink_taskmanager_Status_JVM_Threads_Count"
                    ]
                },
                {
                    "title": "Checkpoint Health",
                    "type": "timeseries",
                    "metrics": [
                        "flink_jobmanager_job_lastCheckpointDuration",
                        "flink_jobmanager_job_lastCheckpointSize",
                        "flink_jobmanager_job_lastCheckpointAlignmentBuffered"
                    ]
                },
                {
                    "title": "Backpressure",
                    "type": "heatmap",
                    "metrics": [
                        "flink_taskmanager_job_task_backPressuredTimeMsPerSecond"
                    ]
                }
            ]
        }
        
        return dashboard_config

# 플랫폼 실행 예제
def deploy_enterprise_flink_platform():
    """엔터프라이즈 Flink 플랫폼 배포"""
    platform = EnterpriseFlinkPlatform()
    
    print("🚀 Starting Enterprise Flink Platform Deployment...")
    
    # 1. 플랫폼 배포
    platform.deploy_enterprise_platform()
    
    # 2. 대시보드 생성
    dashboard = platform.create_comprehensive_dashboard()
    
    # 3. 테스트 실행
    platform.run_integration_tests()
    
    print("✅ Enterprise Flink Platform deployed successfully!")
    
    return platform

if __name__ == "__main__":
    platform = deploy_enterprise_flink_platform()