Building and Deploying a Big Data Platform with Apache DolphinScheduler and Submitting Tasks to AWS

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Introduction

In today’s data-driven world, the need for robust, scalable, and efficient big data platforms has never been greater. Companies across various industries are leveraging big data technologies to gain insights, drive decision-making, and improve operational efficiency. Apache DolphinScheduler, an open-source distributed workflow scheduling system, has emerged as a powerful tool for orchestrating complex data workflows. Coupled with Amazon Web Services (AWS), organizations can build a highly flexible and scalable big data platform.

This article will guide you through building and deploying a big data platform using Apache DolphinScheduler. We’ll also cover how to submit tasks to AWS, providing coding examples to illustrate the process.

Overview of Apache DolphinScheduler

Apache DolphinScheduler is a distributed and extensible workflow scheduler that is primarily used to orchestrate data processing tasks. It provides a user-friendly interface for designing, scheduling, monitoring, and managing complex workflows. It supports various job types, including Shell scripts, Python scripts, SQL queries, and big data processing tasks like Hadoop and Spark.

Key Features of Apache DolphinScheduler

  1. Visual DAGs: DolphinScheduler allows users to create workflows as Directed Acyclic Graphs (DAGs) visually, making it easier to understand and manage complex workflows.
  2. Fault Tolerance: It offers fault-tolerant execution, ensuring that tasks are retried automatically in case of failures.
  3. Scalability: DolphinScheduler can scale horizontally, supporting large-scale data processing workflows.
  4. Multi-Tenancy: It supports multiple tenants, making it suitable for enterprise environments.
  5. Flexible Scheduling: DolphinScheduler provides cron-based scheduling, real-time scheduling, and manual execution options.

Setting Up Apache DolphinScheduler

Step 1: Installing Apache DolphinScheduler

To install Apache DolphinScheduler, you need a Linux-based system. Below are the steps to install DolphinScheduler:

  1. Install Prerequisites: Install Java, MySQL, and other necessary dependencies:
    bash
    sudo apt-get update
    sudo apt-get install openjdk-8-jdk mysql-server
  2. Download and Extract DolphinScheduler: Download the latest version from the official website:
    bash
    wget https://downloads.apache.org/dolphinscheduler/1.3.6/apache-dolphinscheduler-1.3.6-bin.tar.gz
    tar -xzvf apache-dolphinscheduler-1.3.6-bin.tar.gz
  3. Configure MySQL Database: Create a MySQL database and user for DolphinScheduler:
    sql
    CREATE DATABASE dolphinscheduler DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci;
    CREATE USER 'ds_user'@'%' IDENTIFIED BY 'password';
    GRANT ALL PRIVILEGES ON dolphinscheduler.* TO 'ds_user'@'%';
    FLUSH PRIVILEGES;
  4. Configure DolphinScheduler: Modify the application.properties file to include database configurations:
    bash
    vim conf/application.properties

    Set the database connection details:

    properties
    spring.datasource.url=jdbc:mysql://localhost:3306/dolphinscheduler?useUnicode=true&characterEncoding=UTF-8&useSSL=false
    spring.datasource.username=ds_user
    spring.datasource.password=password
  5. Start DolphinScheduler: Start the DolphinScheduler services:
    bash
    sh bin/dolphinscheduler-daemon.sh start master-server
    sh bin/dolphinscheduler-daemon.sh start worker-server
    sh bin/dolphinscheduler-daemon.sh start alert-server
  6. Access the Web Interface: Open a web browser and go to http://<your-server-ip>:12345/dolphinscheduler/ to access the DolphinScheduler UI.

Step 2: Creating a Workflow

Once DolphinScheduler is up and running, the next step is to create a workflow.

  1. Login to DolphinScheduler: Use the default admin credentials (admin/admin) to log in.
  2. Create a Project: Create a new project under the Project Management section.
  3. Design a Workflow:
    • Go to the DAG creation page.
    • Add tasks such as Shell, Python, or SQL tasks.
    • Link tasks to define the execution order.
  4. Configure Task Parameters: For each task, set parameters such as resource files, execution scripts, and environment variables.
  5. Schedule the Workflow: Set the workflow schedule using cron expressions or run it manually.

Integrating Apache DolphinScheduler with AWS

Step 3: Submitting Tasks to AWS

AWS offers various services that can be integrated with Apache DolphinScheduler for scalable data processing. In this section, we’ll focus on submitting tasks to AWS Lambda and AWS EMR (Elastic MapReduce).

Submitting a Task to AWS Lambda

AWS Lambda allows you to run code without provisioning servers. You can trigger a Lambda function from DolphinScheduler.

  1. Create an AWS Lambda Function: Go to the AWS Management Console and create a new Lambda function in your preferred language (e.g., Python, Node.js).
  2. Write the Lambda Function: Here is a simple example in Python:
    python
    def lambda_handler(event, context):
    return {
    'statusCode': 200,
    'body': 'Hello from DolphinScheduler!'
    }
  3. Invoke the Lambda Function from DolphinScheduler: Create a Shell task in DolphinScheduler to invoke the Lambda function using AWS CLI:
    bash
    aws lambda invoke --function-name myLambdaFunction output.txt

    Ensure that the AWS CLI is installed on the server running DolphinScheduler and is configured with the necessary IAM permissions.

Submitting a Task to AWS EMR

AWS EMR is a cloud big data platform that allows you to run Hadoop, Spark, and other big data frameworks. DolphinScheduler can be used to submit jobs to an EMR cluster.

  1. Create an EMR Cluster: Use the AWS Management Console to create an EMR cluster. Ensure that the cluster is configured with Hadoop or Spark, depending on your needs.
  2. Submit a Job to EMR: In DolphinScheduler, create a Shell or Python task that submits a job to the EMR cluster. Here’s an example of submitting a Spark job:
    bash
    aws emr add-steps --cluster-id j-XXXXXXXXXXXXX \
    --steps Type=Spark,Name="Spark Application",ActionOnFailure=CONTINUE,\
    Args=[--class,org.apache.spark.examples.SparkPi,\
    s3://path-to-your-jar/spark-examples.jar,10]
  3. Monitor the Job: You can monitor the progress of your EMR job from the AWS Management Console or using the AWS CLI.

Step 4: Automating with Apache DolphinScheduler

After setting up tasks that interact with AWS, you can use DolphinScheduler to automate these workflows. For instance, you could schedule a daily Spark job on EMR or trigger a Lambda function in response to a specific event.

  1. Create a Workflow: Combine the AWS Lambda and EMR tasks into a single workflow in DolphinScheduler.
  2. Set Workflow Dependencies: Define dependencies between tasks, ensuring that the EMR job runs only after the Lambda function executes successfully.
  3. Schedule the Workflow: Schedule the workflow to run at a specific time or in response to a trigger event.

Best Practices for Deploying a Big Data Platform

Security Considerations

  1. IAM Roles: Use AWS Identity and Access Management (IAM) roles to control access to AWS resources. Assign specific roles to the DolphinScheduler instance to minimize security risks.
  2. Encryption: Ensure that all data in transit and at rest is encrypted, particularly when dealing with sensitive information.

Performance Optimization

  1. Resource Allocation: Monitor the resource utilization of your DolphinScheduler instance and adjust CPU, memory, and storage allocations accordingly.
  2. Workflow Optimization: Optimize workflows by reducing unnecessary tasks and ensuring that tasks are executed in parallel wherever possible.

Monitoring and Logging

  1. CloudWatch Integration: Integrate DolphinScheduler with AWS CloudWatch to monitor logs and set up alerts for critical events.
  2. DolphinScheduler Logging: Enable and regularly review DolphinScheduler logs to identify and troubleshoot issues promptly.

Conclusion

Building and deploying a big data platform with Apache DolphinScheduler and integrating it with AWS allows organizations to orchestrate and manage complex data processing workflows efficiently. By leveraging the power of DolphinScheduler’s workflow automation and AWS’s scalable cloud services, you can create a robust, flexible, and highly available big data solution.

This article has provided a comprehensive guide to setting up Apache DolphinScheduler, creating workflows, and submitting tasks to AWS services such as Lambda and EMR. With proper security measures, performance optimization, and monitoring in place, this platform can serve as the backbone of your organization’s big data strategy, driving insights and value from vast amounts of data.

By following the steps and best practices outlined above, you are well on your way to building a scalable and efficient big data platform that meets your organization’s needs.