Is it possible to use dagster to orchestrate spark etl pipeline that is run in a self-hosted spark cluster in docker container?

Yes, it is possible to use Dagster to orchestrate Spark ETL pipelines that run in a self-hosted Spark cluster within a Docker container. The documentation outlines two main approaches for writing Dagster assets that invoke Spark computations: 1. Asset body submits Spark job: In this approach, the code inside the asset definition submits a Spark job to an external system like Databricks or EMR, usually pointing to a jar or zip of Python files that contain the actual Spark data transformations and actions. If you want to run a Spark job against YARN or a Spark Standalone cluster, the code inside your asset can issue a shell command that invokes

spark-submit

. This approach is suitable for migrating existing Spark jobs and works for Spark jobs written in Java or Scala. However, it is less integrated with Dagster's features, and writing unit tests can be cumbersome. 2. Asset accepts and produces DataFrames or RDDs: With this approach, the asset definition consists of pure logical data transformations on Spark DataFrames or RDDs. The

@asset

-decorated function accepts

DataFrames

as parameters and returns

DataFrames

upon completion. An IO manager handles writing and reading the DataFrames to and from persistent storage. If you want your Spark driver to run inside a Spark cluster, you use a "step launcher" resource that informs Dagster how to launch the step. This approach has a clean local testing story and allows you to run an entire pipeline of Spark assets in a single process. However, it only works with PySpark, and setting up a step launcher can be difficult. For a self-hosted Spark cluster in a Docker container, you would likely use the first approach, where the asset body submits the Spark job, possibly using

spark-submit

to submit jobs to your cluster. This allows you to manage your Spark cluster independently of Dagster while still using Dagster to orchestrate the overall ETL pipeline. Remember that when integrating with Spark, you may need to consider networking, storage, and other infrastructure concerns to ensure that Dagster can communicate with the Spark cluster and that data can be persisted and accessed as needed.

@Scout what would my docker-compose, dockerfile, and asset body look like in the first approach?

The documentation does not provide a complete example of a

docker-compose.yml

,

Dockerfile

, and asset body specifically for orchestrating a Spark ETL pipeline in a self-hosted Spark cluster within a Docker container. However, I can guide you through the general components you would need for each part based on the documentation's guidance on Docker and Kubernetes deployments, as well as general Docker and Spark practices. ### Dockerfile Your

Dockerfile

would need to include your Dagster project, the Dagster library, and any other dependencies required to submit jobs to your Spark cluster. Here's a simplified example:

Dockerfile
FROM python:3.8-slim

# Install Java, which is required for Spark
RUN apt-get update && \
    apt-get install -y openjdk-11-jre-headless && \
    apt-get clean;

# Set the working directory
WORKDIR /app

# Copy your Dagster project code into the container
COPY . /app

# Install Dagster and any other dependencies
RUN pip install dagster dagster-spark

# Your entrypoint here would be whatever is appropriate for your Dagster deployment
CMD ["dagit", "-h", "0.0.0.0", "-p", "3000"]

### docker-compose.yml Your

docker-compose.yml

would define services for Dagster and your Spark cluster. Here's a simplified example:

yaml
version: '3.7'
services:
  dagster:
    build: .
    ports:
      - "3000:3000"
    environment:
      - SPARK_MASTER_URL=<spark://spark-master:7077>
    depends_on:
      - spark-master

  spark-master:
    image: bitnami/spark:latest
    environment:
      - SPARK_MODE=master
    ports:
      - "8080:8080"
      - "7077:7077"

  spark-worker:
    image: bitnami/spark:latest
    environment:
      - SPARK_MODE=worker
      - SPARK_MASTER_URL=<spark://spark-master:7077>
    depends_on:
      - spark-master

### Asset Body The asset body would use Python's subprocess module to call

spark-submit

and submit a job to the Spark cluster. Here's a simplified example:

python
from dagster import asset
import subprocess

@asset
def my_spark_etl():
    # Define the spark-submit command
    submit_command = [
        "spark-submit",
        "--master", "<spark://spark-master:7077>",
        "--class", "org.myorg.MySparkJob",
        "/path/to/my/spark-job.jar"
    ]
    
    # Run the spark-submit command
    result = subprocess.run(submit_command, capture_output=True, text=True)
    
    # Handle the result
    if result.returncode != 0:
        raise Exception(f"Spark job failed with the following output: {result.stderr}")
    
    # Return some result or output path
    return "path/to/output"

Please note that this is a very high-level example and does not include error handling, logging, or other best practices. You would need to adapt this to your specific Spark job, cluster configuration, and

@Scout what if my spark job is writting in pyspark (python file) with multiple python files imports?

If your Spark job is written in PySpark and consists of multiple Python files, you will need to ensure that all the necessary Python files are available to the Spark context when you submit the job. This typically involves packaging your Python files into a

.zip

or

.egg

file and using the

--py-files

argument with

spark-submit

to distribute them to the Spark workers. Here's how you might adjust the

Dockerfile

,

docker-compose.yml

, and asset body to handle a PySpark job with multiple Python file dependencies: ### Dockerfile The

Dockerfile

would remain largely the same, but you should ensure that all your Python files are copied into the Docker image:

Dockerfile
FROM python:3.8-slim

# Install Java, which is required for Spark
RUN apt-get update && \
    apt-get install -y openjdk-11-jre-headless && \
    apt-get clean;

# Set the working directory
WORKDIR /app

# Copy your Dagster project code and PySpark job files into the container
COPY . /app

# Install Dagster, Dagster-Spark, and any other dependencies
RUN pip install dagster dagster-spark pyspark

# Your entrypoint here would be whatever is appropriate for your Dagster deployment
CMD ["dagit", "-h", "0.0.0.0", "-p", "3000"]

### docker-compose.yml The

docker-compose.yml

file would not need significant changes from the previous example, as it's mainly concerned with service orchestration and networking. ### Asset Body For the asset body, you would use the

--py-files

argument to include your

.zip

or

.egg

file when submitting the job. Here's an example of how you might adjust the asset body:

python
from dagster import asset
import subprocess

@asset
def my_pyspark_etl():
    # Path to your main PySpark script
    main_script = "/path/to/my_main_pyspark_script.py"
    
    # Path to the .zip file containing your Python dependencies
    py_files = "/path/to/my_python_files.zip"
    
    # Define the spark-submit command
    submit_command = [
        "spark-submit",
        "--master", "<spark://spark-master:7077>",
        "--py-files", py_files,
        main_script
    ]
    
    # Run the spark-submit command
    result = subprocess.run(submit_command, capture_output=True, text=True)
    
    # Handle the result
    if result.returncode != 0:
        raise Exception(f"Spark job failed with the following output: {result.stderr}")
    
    # Return some result or output path
    return "path/to/output"

In this example,

my_python_files.zip

should contain all the Python files that your main PySpark script (

my_main_pyspark_script.py

) depends on. You can create this

.zip

file as part of your build process or within your Dockerfile. Remember to adjust the paths to match where your files are located within the Docker container. Also, ensure that your Docker c