checkpoint: preprocess

airflow/dags/experiment_dag.py

+from airflow import DAG
+from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
+from datetime import datetime
+
+with DAG(
+    dag_id='experiment_dag_minio',
+    schedule_interval=None,
+    start_date=datetime(2025, 1, 8),
+    catchup=False,
+) as dag:
+
+    ingest_training_data_task = SparkSubmitOperator(
+        task_id='ingest_training_data',
+        application='/opt/airflow/functions/ingest_training_data.py',
+        conn_id='spark_default',
+        name='ingest_training_data_job',
+        application_args=[
+            '/opt/workspace/dataset.csv',  # Input path
+            'my-bucket',  # MinIO bucket name
+            'ingested_data.csv',  # MinIO object key
+        ],
+        conf={
+            'spark.executor.memory': '512m',
+        },
+        verbose=True,
+    )
+
+    preprocess_task = SparkSubmitOperator(
+        task_id='preprocess',
+        application='/opt/airflow/functions/preprocess.py',
+        conn_id='spark_default',
+        name='preprocess_job',
+        application_args=[
+            'my-bucket',  # MinIO bucket name
+            'ingested_data.csv',  # Input object key
+            'preprocessed_data.csv',  # Output object key
+        ],
+        conf={
+            'spark.executor.memory': '512m',
+        },
+        verbose=True,
+    )
+
+    ingest_training_data_task >> preprocess_task

airflow/dags/test_dag.py

 from airflow import DAG
 from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
+from airflow.operators.bash import BashOperator
 from datetime import datetime
 
 with DAG(
@@ -11,13 +12,17 @@ with DAG(
 
     spark_submit_task = SparkSubmitOperator(
         task_id='submit_spark_job',
-        application='/opt/workspace/wordcount.py',
+        application='/opt/airflow/functions/wordcount.py',
         conn_id='spark_default',
         name='wordcount_job',
         application_args=['/opt/workspace/input.txt', '/opt/workspace/output/'],
-        conf={'spark.executor.memory': '512m'},
+        conf={
+            'spark.executor.memory': '512m',
+            'spark.hadoop.fs.local.block.size': '134217728',
+            'spark.hadoop.fs.permissions.umask-mode': '000',
+        },
         verbose=True,
     )
 
-
-    spark_submit_task
+    # Define the task sequence
+    spark_submit_task
\ No newline at end of file

airflow/functions/ingest_training_data.py

+from pyspark.sql import SparkSession
+import pyspark.pandas as ps
+import boto3
+import sys
+
+def ingest_training_data(input_path, bucket_name, object_key):
+    # Create a Spark session
+    spark = SparkSession.builder.appName("Data Ingestion").getOrCreate()
+
+    # Read the dataset into a pandas-on-Spark DataFrame
+    df = ps.read_csv(input_path)
+
+    # Save the ingested data to a local file
+    temp_file = "/tmp/ingested_data.csv"
+    df.to_csv(temp_file, index=False)
+
+    # Upload the file to MinIO
+    s3 = boto3.client(
+        's3',
+        endpoint_url='http://minio:9000',
+        aws_access_key_id='minioadmin',
+        aws_secret_access_key='minioadmin',
+    )
+    s3.upload_file(temp_file, bucket_name, object_key)
+    print(f"Ingested data uploaded to MinIO: {bucket_name}/{object_key}")
+
+    # Stop the Spark session
+    spark.stop()
+
+if __name__ == "__main__":
+    input_path = sys.argv[1]
+    bucket_name = sys.argv[2]
+    object_key = sys.argv[3]
+    ingest_training_data(input_path, bucket_name, object_key)

airflow/functions/preprocess.py

+from pyspark.sql import SparkSession
+import pyspark.pandas as ps
+import boto3
+import sys
+
+def preprocess(bucket_name, object_key, output_key):
+    # Create a Spark session
+    spark = SparkSession.builder.appName("Data Preprocessing").getOrCreate()
+
+    # Download the file from MinIO
+    temp_file = "/tmp/ingested_data.csv"
+    s3 = boto3.client(
+        's3',
+        endpoint_url='http://minio:9000',
+        aws_access_key_id='minioadmin',
+        aws_secret_access_key='minioadmin',
+    )
+    s3.download_file(bucket_name, object_key, temp_file)
+
+    # Load the data
+    df = ps.read_csv(temp_file)
+
+    # Perform preprocessing
+    df = df.drop_duplicates()
+    df = df.dropna()
+
+    gender_mapping = {"Male": 0, "Female": 1}
+    df["gender"] = df["gender"].map(gender_mapping)
+
+    binary_mapping = {"No": 0, "Yes": 1}
+    binary_col = ["Partner", "Dependents", "PhoneService", "PaperlessBilling"]
+    for col_name in binary_col:
+        df[col_name] = df[col_name].map(binary_mapping)
+
+    categorical_col = [
+        "MultipleLines",
+        "InternetService",
+        "OnlineSecurity",
+        "OnlineBackup",
+        "DeviceProtection",
+        "TechSupport",
+        "StreamingTV",
+        "StreamingMovies",
+        "Contract",
+        "PaymentMethod",
+    ]
+    for col_name in categorical_col:
+        df[col_name] = df[col_name].astype("category").cat.codes
+
+    # Save the preprocessed data back to MinIO
+    preprocessed_file = "/tmp/preprocessed_data.csv"
+    df.to_csv(preprocessed_file, index=False)
+    s3.upload_file(preprocessed_file, bucket_name, output_key)
+    print(f"Preprocessed data uploaded to MinIO: {bucket_name}/{output_key}")
+
+    spark.stop()
+
+if __name__ == "__main__":
+    bucket_name = sys.argv[1]
+    object_key = sys.argv[2]
+    output_key = sys.argv[3]
+    preprocess(bucket_name, object_key, output_key)

workspace/wordcount.py → airflow/functions/wordcount.py

@@ -45,7 +45,7 @@ def main():
 
 
     # Collect results and save to the output file
-    word_counts.saveAsTextFile(output_path)
+    # word_counts.saveAsTextFile(output_path)
 
     print("=============================================================")
     print("Word counts saved")

docker-compose.yml

@@ -14,6 +14,7 @@ services:
     depends_on:
       - spark-master
     command: bash -c "rm -f /opt/airflow/airflow-webserver.pid && airflow db init && (airflow scheduler & airflow webserver)"
+
   spark-master:
     image: bitnami/spark:latest
     container_name: spark-master
@@ -55,8 +56,24 @@ services:
     depends_on:
       - spark-master
 
+  minio:
+    image: quay.io/minio/minio:latest
+    container_name: minio
+    command: server /data --console-address ":9001"
+    environment:
+      - MINIO_ROOT_USER=minioadmin
+      - MINIO_ROOT_PASSWORD=minioadmin
+    ports:
+      - "9000:9000" # MinIO API
+      - "9001:9001" # MinIO Console
+    networks:
+      - spark-cluster
+    volumes:
+      - minio-data:/data
+
 volumes:
   shared-workspace:
+  minio-data:
 
 networks:
   spark-cluster:

workspace/input.txt

-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum
-Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?
\ No newline at end of file