orchestration

## What I do - Orchestrate complex multi-container workloads - Manage container lifecycle and scheduling - Implement job scheduling and cron jobs - Configure resource allocation and quotas - Set up auto-scaling and self-healing - Manage stateful workloads ## When to use me - When managing containerized applications - When scheduling batch jobs - When implementing microservices - When scaling applications automatically - When managing stateful applications - When orchestrating hybrid workloads ## Key Concepts ### Kubernetes Jobs and CronJobs ```yaml apiVersion: batch/v1 kind: Job metadata: name: data-processor spec: ttlSecondsAfterFinished: 100 backoffLimit: 3 template: spec: restartPolicy: OnFailure containers: - name: processor image: data-processor:latest command: ["./process.sh"] env: - name: BATCH_SIZE value: "1000" resources: requests: memory: "256Mi" cpu: "250m" limits: memory: "512Mi" cpu: "500m" --- apiVersion: batch/v1 kind: CronJob metadata: name: daily-backup spec: schedule: "0 2 * * *" successfulJobsHistoryLimit: 3 failedJobsHistoryLimit: 1 concurrencyPolicy: Forbid jobTemplate: spec: template: spec: restartPolicy: OnFailure containers: - name: backup image: backup-tool:latest command: ["./backup.sh"] env: - name: BACKUP_PATH value: "/data" ``` ### Nomad Configuration ```hcl job "data-processor" { datacenters = ["dc1"] type = "batch" group "processor" { count = 10 task "process" { driver = "docker" config { image = "processor:latest" args = ["--batch-size", "1000"] } resources { cpu = 500 memory = 512 } constraint { attribute = "${attr.platform.arch}" value = "amd64" } service { name = "processor" port = "http" check { type = "http" path = "/health" interval = "10s" timeout = "5s" } } } } } ``` ### Docker Swarm ```yaml version: '3.8' services: app: image: myapp:latest deploy: replicas: 3 update_config: parallelism: 1 delay: 10s failure_action: rollback restart_policy: condition: on-failure delay: 5s max_attempts: 3 resources: limits: cpus: '0.5' memory: 512M networks: - backend healthcheck: test: ["CMD", "curl", "-f", "http://localhost:3000/health"] interval: 30s timeout: 10s retries: 3 networks: backend: driver: overlay ``` ### Workflow Orchestration ```python # Airflow DAG example from airflow import DAG from airflow.operators.python import PythonOperator from datetime import datetime, timedelta default_args = { 'owner': 'dataeng', 'depends_on_past': False, 'start_date': datetime(2024, 1, 1), 'retries': 3, 'retry_delay': timedelta(minutes=5), } dag = DAG( 'etl_pipeline', default_args=default_args, schedule_interval='@daily', ) def extract(): pass def transform(): pass def load(): pass t1 = PythonOperator( task_id='extract', python_callable=extract, dag=dag, ) t2 = PythonOperator( task_id='transform', python_callable=transform, dag=dag, ) t3 = PythonOperator( task_id='load', python_callable=load, dag=dag, ) t1 >> t2 >> t3 ``` ### Argo Workflows ```yaml apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: name: ml-pipeline spec: entrypoint: ml-training templates: - name: ml-training dag: tasks: - name: preprocess template: preprocess - name: train template: train dependencies: [preprocess] - name: evaluate template: evaluate dependencies: [train] - name: deploy template: deploy dependencies: [evaluate] - name: preprocess container: image: ml-preprocess:latest command: [python, preprocess.py] - name: train container: image: ml-train:latest command: [python, train.py] - name: evaluate container: image: ml-eval:latest command: [python, evaluate.py] - name: deploy container: image: ml-deploy:latest command: [python, deploy.py] ``` ### Key Orchestration Features - **Scheduling**: Time-based and event-based - **Resource Management**: CPU, memory, GPU - **Dependency Management**: DAG-based workflows - **Failure Handling**: Retries, fallbacks - **Scaling**: Horizontal and vertical - **State Management**: Persistent volumes, checkpoints - **Security**: RBAC, secrets management