Deployment and Production

This tutorial covers deploying and managing Apache Spark applications in production environments.

Cluster Deployment Modes (Standalone, YARN, Kubernetes, Mesos)

Standalone Mode

Simplest deployment mode.

1. Start the master:

   ./sbin/start-master.sh
   

2. Start the workers:

   ./sbin/start-worker.sh spark://<master-url>
   

YARN Mode

Runs on Hadoop YARN.

1. Configure yarn-site.xml.

2. Set HADOOP_CONF_DIR.

3. Submit the application:

   spark-submit --class <main-class> \
   

--master yarn
--deploy-mode cluster
```

Kubernetes Mode

Runs on Kubernetes.

1. Create a Kubernetes cluster.

2. Configure Spark to use Kubernetes.

3. Submit the application:

   spark-submit --class <main-class> \
   

--master k8s://
--deploy-mode cluster
```

Mesos Mode

Runs on Apache Mesos.

1. Configure Mesos.

2. Submit the application:

   spark-submit --class <main-class> \
   

--master mesos://
--deploy-mode cluster
```

Resource Management

Dynamic Allocation

Enabled by default.

Setting Executor Cores and Memory

spark.executor.cores
spark.executor.memory

Scheduling and Dynamic Allocation

Fair Scheduler

Allocates resources fairly across applications.

Dynamic Allocation

Dynamically adjusts the number of executors based on workload.

Logging and Monitoring

Spark UI

Access at http://<driver-node>:4040.

History Server

Configured in spark-defaults.conf.

Security Configuration

Authentication

Enable authentication to secure the cluster.

Authorization

Control access to resources based on user roles.

High Availability Setup

Standby Master

Configured to handle failover.

Zoo