Docker Hadoop Spark

Running Hadoop and Spark in Docker containers can be a convenient way to set up and experiment with these big data technologies without the need for complex manual installations. Here’s a high-level overview of how you can run Hadoop and Spark in Docker containers:

1. Docker Installation:

   • Ensure that you have Docker installed on your system. You can download and install Docker from the official website for your specific operating system.

2. Pull Docker Images:

   • You can find official Docker images for Hadoop and Spark on Docker Hub. Here are the image names you can use:

   • Hadoop: sequenceiq/hadoop-docker

   • Spark: bigdata/docker-spark

   Use the docker pull command to download these images to your local machine:

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   docker pull sequenceiq/hadoop-docker docker pull bigdata/docker-spark

3. Create Docker Network:

   • To ensure that your Hadoop and Spark containers can communicate with each other, create a Docker network. This allows them to connect via container names:
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   docker network create --driver bridge hadoop-network

4. Run Hadoop Container:

   • Start a Hadoop container using the pulled image. You’ll need to expose ports for Hadoop services and link it to the created network:
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   docker run -d --name hadoop-container --hostname hadoop --network hadoop-network -p 50070:50070 -p 8088:8088 -p 8030:8030 -p 8031:8031 -p 8032:8032 -p 8033:8033 sequenceiq/hadoop-docker

   This command starts a Hadoop container with the necessary ports exposed.

5. Run Spark Container:

   • Start a Spark container using the pulled image. Link it to the same network as the Hadoop container:
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   docker run -it --name spark-container --hostname spark --network hadoop-network -e ENABLE_INIT_DAEMON=false -p 4040:4040 bigdata/docker-spark

   This command starts a Spark container and exposes the Spark UI port.

6. Access Hadoop and Spark:

   • You can access the Hadoop web UI by opening a web browser and navigating to http://localhost:50070 for the NameNode UI and http://localhost:8088 for the ResourceManager UI.
   • The Spark UI can be accessed at http://localhost:4040.

7. Submit Spark Jobs:

   • Now that both Hadoop and Spark containers are running, you can submit Spark jobs to the Spark container. You can use the spark-submit script within the Spark container to submit your jobs.