Chapters Prev Premium | Chapter 2: Cloudera CDP Data Engineer Certification Preparation

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Hello and welcome to ReadioBook.com. In this Chapter-2 : Cloudera Data Platform, Kubernetes and Spark.

CDP Integration: Cloudera Data Platform (CDP) provides a robust environment for running Spark applications on Kubernetes, offering advanced features like enhanced security and efficient workload management. This section will guide you through integrating Spark with CDP on Kubernetes, focusing on leveraging its key features.


Deploying Spark Applications:

Creating a Spark Application in PySpark: Develop a Spark application using PySpark. For example, a simple data processing application:

Containerizing the PySpark Application:

- Create a Docker container for your Spark application.
- Push the container to a registry accessible by your Kubernetes cluster.
Deploying on Kubernetes through CDP:

- Write a Kubernetes deployment YAML for your Spark application.
- Use CDP to deploy the application onto the Kubernetes cluster.

Leveraging CDP Features:

Security Integration: Utilize CDP’s comprehensive security features including Kerberos for authentication and Sentry for authorization. Example configuration for enabling Kerberos:

Workload Management: Use CDP’s workload management capabilities to allocate resources efficiently. Configure resource quotas and limits for Spark applications in Kubernetes.

Integrating Kubernetes with the Cloudera Data Platform (CDP) involves several key steps to configure the CDP Kubernetes Service. This integration allows you to effectively manage Kubernetes resources directly from CDP. Here's a detailed guide with an example to help you through the process:


Step 1: Preparing Your Kubernetes Environment:
- Kubernetes Cluster: Ensure you have a Kubernetes cluster set up, either on-premises or on a cloud provider like AWS, Azure, or Google Cloud Platform.
- Cluster Requirements: Verify that your Kubernetes cluster meets the requirements specified by CDP, such as version compatibility, network configurations, and resource quotas.


Step 2: Accessing Cloudera Data Platform:
- CDP Account: Log in to your Cloudera Data Platform account.
- Access Management: Ensure you have the necessary permissions or administrative rights to integrate services and manage resources.


Step 3: Configuring the Kubernetes Service in CDP:
- Navigating to Kubernetes Service: In the CDP console, navigate to the Kubernetes Service section.
- Adding a New Kubernetes Cluster:

- Select the option to add/register a new Kubernetes cluster.
- Provide the required details of your Kubernetes cluster, such as cluster name, API server endpoint, and authentication credentials (e.g., kubeconfig file content).

Step 4: Setting up Service Account and RBAC: Create a Service Account in Kubernetes: This account will be used by CDP to interact with your Kubernetes cluster.

- Configure RBAC for the Service Account: Assign necessary roles and permissions to the service account for managing resources. Example RBAC configuration:

Step 5: Integrating with CDP:
- Provide Service Account Credentials to CDP:

- Extract the token from the service account created and provide it to CDP during the cluster registration process.

Finalize the Integration: Complete the registration process in CDP by verifying the connection and ensuring that CDP can communicate with the Kubernetes cluster.


Step 6: Deploying Spark Applications:
- Spark Application Setup: Prepare your Spark application, ensuring it's containerized and ready for deployment on Kubernetes.
- Deploy through CDP: Use CDP to deploy and manage your Spark application on the integrated Kubernetes cluster.

Example: Deploying a Spark Job

Cloudera Manager Integration:

When deploying Spark on Kubernetes, Cloudera Manager can be utilized to streamline the management of resources, applications, and monitoring. Setting up the environment for running Spark on Kubernetes with Cloudera Manager involves several key steps. This process can be broadly divided into three main parts: Installing Cloudera Manager, Configuring Kubernetes, and Ensuring Compatibility between Spark, Kubernetes, and Cloudera Manager. Let's delve into each of these steps with more detail and examples.


Installing Cloudera Manager: Before you can manage anything with Cloudera Manager, you need to have it installed on a system that will act as the management server. This system should meet the hardware and software requirements specified by Cloudera.


Refer our trainings on Quicktechie.com for more Hands-on Exercises:

Steps for Installation:
- Prepare the Server: Choose a server that meets the minimum hardware and software requirements for Cloudera Manager. Make sure it has network access to the Kubernetes cluster.
- Install the Cloudera Manager Server: Download the Cloudera Manager installer from Cloudera’s official website. Run the installer script on your server. This will install the Cloudera Manager Server and the embedded PostgreSQL database. Example command:

- Access the Cloudera Manager Web UI: Once the installation is complete, you can access the Cloudera Manager Web UI through a web browser by navigating to

- The default login is usually 'admin' for both username and password.

Configuring Kubernetes Cluster: You need a Kubernetes cluster where your Spark applications will run. This can be an existing cluster or a new one set up for this purpose. Steps for Configuring Kubernetes:
- Set Up a Kubernetes Cluster: You can use cloud services like Google Kubernetes Engine (GKE), Amazon EKS, or Azure Kubernetes Service (AKS), or set up a cluster manually using tools like kubeadm. Ensure that the Kubernetes API server is reachable from the Cloudera Manager server.
- Configure Nodes and Networking: Ensure your nodes have sufficient resources (CPU, Memory) for running Spark jobs. Set up proper networking for pod communication. This might include setting up network policies and ingress controllers.
- Create Necessary Kubernetes Configurations: Set up namespaces, if needed, for organizing resources. Create service accounts with appropriate permissions for Cloudera Manager to interact with Kubernetes.


Ensuring Compatibility: Ensure that the versions of Spark, Kubernetes, and Cloudera Manager are compatible. This is crucial for smooth operation and integration.
- Compatibility Checks: Check Version Compatibility: Refer to the official documentation of Cloudera Manager to ensure it supports the version of Kubernetes you are using.

Example Scenario: Imagine setting up a Kubernetes cluster on AWS EKS for deploying Spark applications.


EKS Cluster Creation:

- Use AWS Management Console or AWS CLI to create an EKS cluster.
- Configure worker nodes that the Spark jobs will use.
Networking and Access:

- Set up VPC, subnets, and security groups for the EKS cluster.
- Ensure that the Cloudera Manager server can communicate with the EKS cluster (e.g., through a VPN or direct link).

Cloudera Manager Installation:

- Install Cloudera Manager on a server that has network access to the EKS cluster.
- Access the Cloudera Manager Web UI and start configuring it to manage the Kubernetes cluster.This setup provides a foundation for managing Spark applications on Kubernetes using Cloudera Manager. The specifics can vary based on the cloud provider or the specifics of your on-premise Kubernetes cluster. The key is to ensure that all components are correctly installed, configured, and networked together.

Integrating Spark with Kubernetes: Integrating Apache Spark with Kubernetes involves setting up your Kubernetes environment to run Spark applications. Here’s a more detailed breakdown of the process.
- Container Image: Spark jobs in Kubernetes run inside containers. You need a container image with Spark installed. This can be a standard image provided by the Spark project, or a custom image tailored to your requirements.

Preparing the Kubernetes Cluster

- Cluster Setup: If you don’t already have a Kubernetes cluster, set one up. The complexity of this step depends on whether you’re using a cloud provider (which often provides easier setup) or setting up a cluster on-premises.
- Role-Based Access Control (RBAC): Ensure that RBAC is enabled in your Kubernetes cluster. This is important for security and proper management of resources.
- Persistent Volumes (Optional): If your Spark applications need to store data persistently, set up Persistent Volumes in Kubernetes.


Configuring Spark to Use Kubernetes:
- Spark Configuration: Configure Spark to use Kubernetes as its cluster manager. This is done by setting the 'spark.master' property to 'k8s://api-server-url' in your Spark configuration as below.

- Kubernetes Context: Ensure that your 'kubectl' is configured to communicate with the intended Kubernetes cluster. You can check the current context using 'kubectl config current-context'.


Building and Pushing the Docker Image:
- Dockerfile for Spark: Create a Dockerfile that includes the Spark binaries and any other dependencies your application requires.

- Building the Image: Build the Docker image using the 'docker build' command.

- Pushing to a Registry: Push the built image to a container registry accessible by your Kubernetes cluster (like Docker Hub, Google Container Registry, etc.).

Submitting Spark Jobs to Kubernetes: Using 'spark-submit': Use the 'spark-submit' command to submit your Spark application to the Kubernetes cluster.

In this command:
- Replace 'k8s-api-server: port' with your Kubernetes API server's address and port.

- 'my-spark-image' is the Docker image you pushed to the registry.
- 'my-spark-app.py' is your Spark application.
Cloudera Manager Configuration for KubernetesThis step is crucial as it enables Cloudera Manager to interact with and manage the Kubernetes environment where your Spark applications are deployed.
Adding Kubernetes Service in Cloudera Manager: To add Kubernetes as a service in Cloudera Manager, you typically follow these steps:



- Access Cloudera Manager: Log in to the Cloudera Manager web interface.
- Navigate to Cluster Configuration: Go to the cluster where you want to add the Kubernetes service.
- Add Service: Select "Add a Service" from the options available. In the list of services, you should find Kubernetes (the availability of this option depends on your Cloudera Manager version and the package you are using). Select Kubernetes and proceed to add it.
- Configure the Service: You will be prompted to configure the Kubernetes service. This includes specifying the Kubernetes API server URL and other necessary configurations that Cloudera Manager needs to communicate with your Kubernetes cluster.
- Save and Restart: After configuring, save the changes and, if required, restart the service to apply the new configuration.


Configuring Service Accounts in Kubernetes: For Cloudera Manager to manage Spark applications in Kubernetes, you need to create a service account in Kubernetes with the appropriate permissions. Here's how you can do it:
- Create a Service Account: Use 'kubectl' to create a new service account. For example:

- Assign Role and Permissions: You need to ensure that this service account has the required permissions to manage resources in the Kubernetes cluster. This typically involves creating a role with the necessary permissions and binding it to the service account. For example, you might create a role that allows managing pods, services, and other resources necessary for Spark. Then, use a role binding or cluster role binding to assign this role to the service account. For example:

- Configure Cloudera Manager to Use the Service Account: In Cloudera Manager’s configuration for Kubernetes, specify the service account name and the namespace where it resides. This ensures that when Cloudera Manager interacts with the Kubernetes API, it uses the credentials and permissions of this service account.

Example: Service Account and Role Binding: Assuming you have a basic understanding of Kubernetes and 'kubectl', here’s an example script to create a service account and role binding.

In this example: 'cloudera-manager' is the name of the service account. We are binding it to 'cluster-admin', a default cluster role that usually has wide permissions. In a production environment, you should create a custom role with only the necessary permissions. By following these steps, you integrate Cloudera Manager with Kubernetes, enabling it to manage and monitor Spark applications running in a Kubernetes environment. This integration is vital for centralized administration and effective management of resources. Remember, the specifics can vary based on your cluster setup and security policies, so adjust the steps as needed for your environment.

Deploying Spark Applications on Kubernetes Deploying Spark applications on Kubernetes and using Cloudera Manager for monitoring and management. Let's break down this step into more detail. Deploying Spark applications on Kubernetes and monitoring them with Cloudera Manager involves submitting your application using 'spark-submit', and then utilizing Cloudera Manager’s comprehensive suite of monitoring tools to manage and optimize the application's performance and resource utilization. This allows for efficient scaling, troubleshooting, and management of Spark applications within a Kubernetes environment.

Preparing the Spark Application: First, you need a Spark application ready for deployment. This could be a JAR (for Scala/Java) or a Python script for PySpark. For example:

Using 'spark-submit' for Deployment: The 'spark-submit' script is used to submit your application to a Kubernetes cluster. Here’s how a typical command looks.

Monitoring and Management with Cloudera Manager: After deploying the Spark application on Kubernetes, Cloudera Manager can be used for monitoring and management.
- Integration with Cloudera Manager: To integrate with Cloudera Manager, ensure that your Kubernetes cluster is registered and recognized within Cloudera Manager. This might require installing specific plugins or agents on your Kubernetes nodes.
- Monitoring Applications: Once your Spark application is running, you can monitor it through Cloudera Manager. This includes: Viewing resource utilization (CPU, memory) of your Spark applications. Tracking the status of Spark jobs and stages. Accessing logs for debugging and analysis. Setting up alerts for any anomalies or thresholds exceeded.


Example: Monitoring a Spark Application: Imagine you have deployed the 'example.py' script. In Cloudera Manager:

- Navigate to the Spark service.
- Look for your application under the "Applications" tab.
- Click on your application to view detailed metrics, like execution DAG, memory usage, and executor details.
- Set up alerts or thresholds for monitoring the application's health.
Advanced Configurations and Tuning Fine-tuning resource allocation for Spark applications in Kubernetes ensures efficient and optimal performance. Meanwhile, implementing network policies ensures secure communication and prevents unauthorized access. These advanced configurations are essential for managing complex Spark applications in a Kubernetes environment, offering both scalability and security.
Resource Allocation for Spark on Kubernetes: Resource allocation in Kubernetes involves specifying the amount of CPU and memory (RAM) that your Spark applications can use. Proper resource allocation is crucial for balancing performance and efficiency.


Defining Resource Requests and Limits: Requests: These are the minimum resources required by your Spark application. Kubernetes uses this to decide on which node to place the pod.

Defining Resource Requests and Limits: Limits: These are the maximum resources that a Spark application can use. It prevents applications from consuming excessive resources.

Spark Configuration for Kubernetes: Configure Spark properties to specify resources for the driver and executors. Example properties:


- spark.kubernetes.\
driver.limit.cores:
Maximum number of cores for the driver.
- spark.kubernetes.\
executor.limit.cores:
Maximum number of cores for each executor.
- spark.kubernetes.\
driver.request.cores:
Requested number of cores for the driver.
- spark.kubernetes.\
executor.request.cores:
Requested number of cores for each executor.
Example Spark Submit Command:

Dynamic Allocation: Spark can dynamically allocate executors based on workload. This is useful for handling varying loads efficiently. Enable this by setting 'spark.dynamicAllocation.enabled' to 'true' and configuring related properties.

Implementing Network Policies in Kubernetes: Network policies in Kubernetes provide a way to control network access into and out of your Spark application pods.

Default Policies: By default, Kubernetes pods are non-isolated; they accept traffic from any source. Network policies allow you to define how pods communicate with each other and other network endpoints.


Defining a Network Policy: Create a policy that controls the traffic to and from Spark application pods. Define which pods are allowed to communicate with your Spark application.

Example Network Policy: In this example, the policy allows ingress traffic from pods labeled 'app: allowed-app' and egress traffic to pods labeled 'app: external-service'.

Applying the Network Policy: Apply this policy by running 'kubectl apply -f network-policy-file.yaml'. This enforces the defined traffic rules on all pods with the label 'app: spark'.

Cloudera CDP, YARN and Kubernetes
Understanding YARN (Yet Another Resource Negotiator) :

- Purpose: YARN is a core component of Hadoop, designed to manage computing resources in clusters and host various data processing jobs. Before YARN, Hadoop's MapReduce was both a processing system and a resource management system, which was less flexible.
- Architecture: ResourceManager (RM): Orchestrates the use of resources and manages the cluster's compute capacity.



- Architecture: NodeManager (NM): Per-node agent responsible for containers, monitoring resource usage (CPU, memory, disk, network), and reporting the same to the ResourceManager.
- Architecture: ApplicationMaster (AM): An instance that negotiates resources from the ResourceManager and works with the NodeManager to execute and monitor tasks.


YARN's Workflow:

- A client submits an application.
- The ResourceManager allocates a container and starts the ApplicationMaster.
- The ApplicationMaster negotiates resources with the ResourceManager and works with NodeManagers to execute tasks in containers.
- Upon completion, the ApplicationMaster returns the final status to the ResourceManager, which then notifies the client.

Kubernetes Overview:

- Purpose: Kubernetes is an open-source platform designed to automate deploying, scaling, and operating application containers. It helps in managing containerized applications in different deployment environments (physical, virtual, cloud, etc.).
- Key Components: Pods: The smallest deployable units that can be created, scheduled, and managed. A pod is a group of one or more containers.

- Key Components: Nodes: Worker machines in Kubernetes, which can be a VM or a physical computer, serving as a host to Pods.
- Key Components: Control Plane: The set of processes managing the cluster, including the Kubernetes Master, etcd, kube-apiserver, kube-controller-manager, and kube-scheduler.


Kubernetes Workflow:

- Define the application's container and services in a deployment configuration.
- Post the configuration to Kubernetes API Server.
- Kubernetes Control Plane schedules the application’s containers onto Nodes.
- Kubernetes Service provides a static IP address and DNS name for the application.

YARN on Kubernetes Integration: Concept of Integration:

- Objective: To leverage Kubernetes as a resource manager within YARN. This means Kubernetes handles the orchestration of YARN application containers.
- Benefits: Scalability: Kubernetes excels at scaling applications, which enhances YARN’s ability to manage resources dynamically.
- Benefits: Resource Utilization: Kubernetes can optimize the use of resources, leading to better efficiency.
- Benefits: Flexibility: Combines the robust data processing capabilities of YARN with the advanced container management features of Kubernetes.


Implementation Overview:
- Configuration: Set up Kubernetes as a resource manager in YARN.
- Application Submission: Submit applications to YARN as usual.
- Resource Allocation: YARN interacts with Kubernetes to allocate containers for the applications.
- Execution and Management: Kubernetes handles the deployment, scaling, and management of these containers, while YARN manages the application logic and workflow.


Example Scenario: Imagine you have a big data application that requires dynamic scaling based on the data volume and computational needs. By integrating YARN with Kubernetes, you can deploy this application on a Hadoop cluster managed by YARN, while Kubernetes takes care of efficiently scaling and managing the underlying containers. This ensures optimal resource utilization and provides the flexibility to handle varying workloads efficiently.