gcp-spark

name: gcp-spark description: | Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery. license: Apache-2.0 metadata: version: v2 publisher: google

Spark on Dataproc

[!IMPORTANT]

You MUST ALWAYS follow the Task Execution Workflow when writing spark code.

Task Execution Workflow

1. Understand schemas: ALWAYS use @skill:discovering-gcp-data-assets skill or references/schema_direct_inspection.md to understand input and output schemas. Include the schema in your thought process BEFORE generating any code. Do NOT guess column names.
2. Generate spark code:
   • Output Format: ALWAYS generate code in Python Notebooks (.ipynb) format. Generate scripts (.py) only if explicitly requested.
   • Read and Write data: ALWAYS Refer to references/read_write_data.md when reading or writing data.
   • ML Tasks: Refer to @skill:ml-best-practices skill and references/ml_tasks.md when generating ML code.
   • Spark Optimizations: ALWAYS refer to references/spark_optimizations.md when generating spark code and apply optimization whenever applicable.
3. Verify schema before write: ALWAYS verify that the dataframe and destination schema match, use df.printSchema() for dataframe schema and refer to @skill:discovering-gcp-data-assets skill or references/schema_direct_inspection.md to verify destination schema.
4. Compile code before executing: For notebooks convert them to python script using jupyter nbconvert --to script your-notebook.ipynb first, then compile code using python3 -m py_compile your-notebook.py.
5. Execute script: ONLY when generating a .py script refer to references/gcloud_dataproc.md on writing command to execute generated code on Dataproc. This DOES NOT apply when generating notebooks.

Common Mistakes Checklist

[!CAUTION]

Ensure you verify this checklist to avoid mistakes

Before submitting a job, verify:

• All imports present (col, when, lit, etc. from pyspark.sql.functions)
• vector_to_array from correct module use from pyspark.ml.functions import vector_to_array (NOT pyspark.sql.functions)
• DataFrame schema matches target Iceberg table verify with df.printSchema() before writing
• CSV files read with header and inferSchema without these, the header row becomes data and all columns are strings
• Avoid toPandas() Converting a pyspark dataframe to pandas by calling toPandas() can lead to out of memory errors. Only acceptable for building visualizations in Spark 3.5

IAM Requirements

The Dataproc service account needs:

• roles/dataproc.worker: Job execution
• roles/biglake.admin: Iceberg table management
• roles/bigquery.jobUser: Query materialization
• roles/storage.objectUser: Read/write GCS
• roles/spanner.databaseUser: Spanner writes

Spark resource management

Refer to references/gcloud_dataproc.md for detailed guidelines on managing Spark clusters, jobs, batches, and interactive sessions.