DAG: audience-formatter_v003

schedule: None

audience-formatter_v003

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from airflow import DAG
from template.dag_template import build_dag
from airflow.utils.dates import days_ago
from template.utils import set_config_variable_in_emr_steps
from airflow.models import Variable

# DAG specific parameters
config = Variable.get("audience_formatter_conf", deserialize_json=True)

ENV = Variable.get("env")
config['ENV'] = ENV.lower()
MEDIA_OWNER = config['MEDIA_OWNER']
AUD_VERSION = config['AUD_VERSION']

cluster_name = 'audience-formatter-' + MEDIA_OWNER + '-' + AUD_VERSION
dag_id = 'audience-formatter_v003'

emr_steps = """[
  {
        "step-name": "audience-formatter",
        "config-json": [
            {"spark.yarn.executor.memoryOverhead":"2g"},
            {"spark.app.env":"$ENV"},
            {"spark.sql.sources.partitionColumnTypeInference.enabled":false},
            {"spark.driver.extraClassPath":"/usr/share/aws/emr/emrfs/lib/emrfs-hadoop-assembly-2.30.0.jar"}
        ],
        "main-class": "audience_formatter_job.core",
        "parameters": ["--audience-version", "$AUD_VERSION",
                       "--market-cat-id", "$MARKET_CAT_ID",
                       "--market-name", "$MEDIA_OWNER",
                       "--partitions", "$PARTITIONS",
                       "--input", "$INPUT_PATH",
                       "--output", "$OUTPUT_PATH"],
        "artifact": "audience-formatter-job"
  }
]"""

# cluster level parameters (optional)
cluster_args = {
    "cluster-name": cluster_name,
    "audience-config-file": "audience_formatter_conf",
    "master-instance-types": "m5.2xlarge,m5.4xlarge",
    "core-instance-types": "m5.2xlarge,m5.4xlarge",
    "task-instance-types": "m5.2xlarge,m5.4xlarge",
    "core-instance-capacity": 5,
    "task-instance-capacity": 0,
    "emr-version": "emr-5.21.0"
}

# set config variables in emr-steps
emr_steps = set_config_variable_in_emr_steps(emr_steps, config)

# dag parameter
dag_args = {
    'owner': 'data.engineers@viooh.com',
    'start_date': days_ago(1)
}

dag = DAG(
    dag_id,
    schedule_interval=None,  # cron expression
    default_args=dag_args)

build_dag(emr_steps=emr_steps, dag=dag, cluster_args=cluster_args)