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Executing batch jobs in Spark Cluster

In this example we will: - Create a Spark cluster - Submit a job python file - Terminate the cluster after job is completed.

Before you begin

  • Create "spark" under your "~/my" folder
  • And copy job.py under this folder
import practicuscore as prt

job_dir = "~/my/spark"

distributed_config = prt.distributed.JobConfig(
    job_type = prt.distributed.JobType.spark,
    job_dir = job_dir,
    py_file = "job.py",
    worker_count = 2,
)

worker_config = prt.WorkerConfig(
    worker_size="X-Small",
    distributed_config=distributed_config,
    log_level="DEBUG",
)

coordinator_worker = prt.create_worker(
    worker_config=worker_config,
)

prt.distributed.live_view(
    job_dir=job_dir,
    job_id=coordinator_worker.job_id,
)

# You can view the logs during or after the job is completed
# To view coordinator (master) set rank = 0
rank = 0
# To view other workers set rank = 1,2, ..

prt.distributed.view_log(
    job_dir=job_dir,
    job_id=coordinator_worker.job_id,
    rank=rank
)

Wrapping up

  • Once the job is completed, you can view the results in ~/my/spark/result.csv/
  • Please note that result.csv is a folder that contains parts of the processed file by each worker (Spark executors)
  • Also note that you do not need to terminate the cluster since it has a 'py_file' to execute, which defaults terminate_on_completion parameter to True.
  • You can change terminate_on_completion to False to keep the cluster running after the job is completed to troubleshoot issues.
  • You can view other prt.distributed.JobConfig properties to customize the cluster

Supplementary Files

job.py

import practicuscore as prt 

# Let's get a Spark session
print("Getting Spark session")
spark = prt.distributed.get_client()

data = [("Alice", 29), ("Bob", 34), ("Cathy", 23)]
columns = ["Name", "Age"]

print("Creating DataFrame")
df = spark.createDataFrame(data, columns)

print("Calculating")
df_filtered = df.filter(df.Age > 30)

print("Writing to csv")
df_filtered.write.csv("/home/ubuntu/my/spark/result.csv", header=True, mode="overwrite")

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