Skill: Serverless Spark & Lightning Engine Optimization

1. Description & Rationale

This skill establishes the configuration practices for executing PySpark workloads on Managed Service for Apache Spark Serverless. To achieve optimal performance, workloads should leverage the Spark Lightning Engine (vectorized execution) and Spark Connect for decoupled session management.

2. Environment Prerequisites

• Execution Engine: Managed Service for Apache Spark Serverless.
• Tier: Premium (required for Lightning Engine).
• SDK: google-cloud-dataproc (specifically for Spark Connect).

3. Agent Execution Guidelines (System Prompts)

When configuring Spark sessions:

1. Enforce Lightning Engine: Always configure the session to use lightningEngine.
2. Enforce Premium Tier: Set the runtime to premium.
3. Use Spark Connect: Prefer DataprocSparkSession over standard SparkSession when working with Serverless Spark to support modern decoupled execution.

4. Opinionated Code Patterns

A. Creating the Optimized Spark Session

The agent must use the following pattern to initialize the Spark session:

from google.cloud.dataproc_spark_connect import DataprocSparkSession
from google.cloud.dataproc_v1 import Session

def create_spark_session():
    """Creates a SparkSession with BigQuery and GCS support using Lightning Engine."""
    session = Session()
    session.runtime_config.version = "3.0" # Adjust version if needed

    # Enable premium tier and lightning engine
    session.runtime_config.properties = {
        "dataproc.runtime": "premium",
        "spark.dataproc.engine": "lightningEngine",
        "spark.dynamicAllocation.maxExecutors": "8",
    }

    spark = (
        DataprocSparkSession.builder.appName("Detox-Spark-Demo")
        .dataprocSessionConfig(session)
        .getOrCreate()
    )
    return spark

spark = create_spark_session()
print("AI-Native Spark Session Created with Lightning Engine")

5. Verification Checklist

• Premium Tier: Verify "dataproc.runtime": "premium" is in the properties.
• Lightning Engine: Verify "spark.dataproc.engine": "lightningEngine" is set.
• Spark Connect: Ensure DataprocSparkSession is used instead of pyspark.sql.SparkSession.